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📝 WalkthroughWalkthroughThis PR introduces a comprehensive Ralph marketplace workflow system alongside documentation updates. It renames the core workflow to "simple," adds marketplace state management with event logging, implements budget allocation and weight tracking logic, creates orchestration tooling, and expands documentation with marketplace agent specifications and configuration guidance. Changes
Sequence Diagram(s)sequenceDiagram
participant Issue
participant Broker
participant Bidders
participant Evaluator
participant Implementation
participant StateManager
participant WeightSystem
Issue->>Broker: Issue received
activate Broker
Broker->>Broker: Extract requirements
Broker->>Bidders: Spawn bidders with context
activate Bidders
Bidders->>Bidders: Generate proposals
Bidders->>Broker: Submit proposals
deactivate Bidders
Broker->>Evaluator: Score proposals<br/>(Spec, Quality, Risk, Innovation, Efficiency)
activate Evaluator
Evaluator->>Evaluator: Rank bidders
Evaluator->>Broker: Return rankings
deactivate Evaluator
Broker->>Implementation: Execute top proposal
activate Implementation
Implementation->>Implementation: Implement changes
Implementation->>Implementation: Run verification checks
Implementation->>Broker: Return result
deactivate Implementation
Broker->>StateManager: Record outcome
activate StateManager
StateManager->>StateManager: Append event to log
deactivate StateManager
Broker->>WeightSystem: Calculate weight updates
activate WeightSystem
WeightSystem->>WeightSystem: Determine outcome type<br/>+ accuracy bonus
WeightSystem->>Broker: Return deltas
deactivate WeightSystem
Broker->>StateManager: Update weights & efficiency
activate StateManager
StateManager->>StateManager: Recompute state from events
StateManager->>Broker: Persist updated state
deactivate StateManager
Broker->>Broker: Check if replan needed
alt Failure detected
Broker->>Broker: Trigger replan round
Broker->>Bidders: Replan context
else Success
Broker->>Issue: Complete
end
deactivate Broker
Estimated code review effort🎯 4 (Complex) | ⏱️ ~50 minutes Possibly related PRs
🚥 Pre-merge checks | ✅ 2 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (2 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing touches
🧪 Generate unit tests (beta)
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Pull request overview
This PR introduces a marketplace pattern for Ralph where multiple "smart" bots compete to provide solutions by submitting proposals that an arbiter agent evaluates and implements. The system includes budget allocation, weight-based learning, and state management through an append-only event log.
Changes:
- Added marketplace orchestration commands and state management infrastructure
- Created Claude agent definitions for arbiter, smart bots, and specialist bots
- Implemented budget allocation, weight calculation, and state persistence modules
- Added markdown linting to pre-commit hooks
Reviewed changes
Copilot reviewed 19 out of 22 changed files in this pull request and generated 7 comments.
Show a summary per file
| File | Description |
|---|---|
| tools/ralph_marketplace_weights.py | Weight update calculations for bot performance |
| tools/ralph_marketplace_state.py | State management with append-only event log |
| tools/ralph_marketplace_orchestrator.py | Main orchestrator for marketplace commands |
| tools/ralph_marketplace_budget.py | Budget allocation across bots based on weights |
| proposal_transcript.md | Documentation of design conversation |
| proposal_followups.md | Future enhancements and deferred features |
| maskfile.md | Added marketplace commands and markdown linting |
| README.md | Fixed markdown line length issue |
| CLAUDE.md | Comprehensive marketplace documentation |
| .ralph/README.md | Documentation for state directory structure |
| .ralph/.gitignore | Gitignore for cached state files |
| .pre-commit-config.yaml | Added markdown linting hook |
| .markdownlint.yaml | Markdown linting configuration |
| .flox/env/manifest.toml | Added markdownlint-cli dependency |
| .claude/agents/ralph_specialists/*.md | Specialist bot agent definitions |
| .claude/agents/ralph_smart_bot.md | Smart bot agent definition |
| .claude/agents/ralph_arbiter.md | Arbiter agent definition |
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chrisaddy
previously approved these changes
Jan 30, 2026
Contributor
Greptile OverviewGreptile SummaryThis PR introduces a marketplace-based autonomous development workflow where multiple "bidder" agents compete to provide the best solution proposals for GitHub issues. The proposal includes comprehensive infrastructure for state management, budget allocation, and agent orchestration. Key Changes:
Architecture highlights:
Note: This is marked as draft and untested per PR description. The implementation is well-structured with clear separation of concerns. Confidence Score: 4/5
|
| Filename | Overview |
|---|---|
| tools/ralph_marketplace_state.py | Implements event-sourced state management with proper error handling and efficient computation |
| tools/ralph_marketplace_orchestrator.py | Simple CLI orchestrator for marketplace commands with clear structure |
| tools/ralph_marketplace_budget.py | Budget allocation with zero-sum logic and minimum allocation enforcement |
| tools/ralph_marketplace_weights.py | Clean weight calculation logic with well-defined outcome types and deltas |
| .claude/agents/ralph_bidder.md | Comprehensive bidder agent specification with domain expertise and workflow guidance |
| .claude/agents/ralph_broker.md | Detailed broker agent specification with scoring dimensions and verification workflow |
| CLAUDE.md | Added comprehensive marketplace workflow documentation with architecture and learning model |
| maskfile.md | Reorganized Ralph commands into simple/marketplace variants with proper namespace structure |
| .gitignore | Added marketplace cache files to gitignore, but paths appear incorrect |
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Actionable comments posted: 25
🤖 Fix all issues with AI agents
In @.claude/agents/ralph_arbiter.md:
- Line 243: The commit footer currently hardcodes the co-author string
"Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>" which will become
stale; change this to a parameterized placeholder or remove the specific model
version by replacing that literal with a variable (e.g., CO_AUTHOR_NAME or
CO_AUTHOR_LINE) or a generic "Claude Sonnet <noreply@anthropic.com>" in the code
that emits the footer so the value can be updated centrally; locate the exact
string "Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>" in the
.claude/agents/ralph_arbiter.md content and update the emission site to read
from a configurable constant or template instead of the hardcoded version.
In @.claude/agents/ralph_smart_bot.md:
- Around line 29-37: The replan context block using the {{`#if` replan_round}}
section needs surrounding blank lines and a fenced code block language specifier
to satisfy markdownlint (MD022/MD031/MD040); update the template around the "##
Previous Failure Context" and the "Failure Details" block so there is a blank
line before the heading and a blank line before and after the fenced block, and
change the fence to include a language (for example replace ``` with ```text for
the {{failure_details}} block). Apply the same blank-line + fence-language
pattern to the other fenced blocks in this document (search for other
occurrences of triple-backtick fences and headings inside
Handlebars/Handlebars-like sections such as {{`#if` ...}} / {{/if}}).
In @.claude/agents/ralph_specialists/infrastructure_specialist.md:
- Around line 62-74: The markdown paths referencing infrastructure/__main__.py
should be wrapped in backticks to avoid MD050 strong-style parsing; update all
occurrences of infrastructure/__main__.py in the document (including where ECS
Service oscmcompany-auth, Task Definition AuthTaskDefinition, Load Balancer
auth-tg, and IAM Role auth-service-role are annotated) so they read as
`infrastructure/__main__.py`, ensuring code formatting prevents markdownlint
strong-emphasis issues and improves readability.
- Around line 46-56: The Markdown list item "2. **Examine infrastructure** code"
contains a fenced code block that needs blank lines before and after to satisfy
markdownlint MD031; edit the section around the triple-backtick fence (the
fenced block under the "Examine infrastructure" list item) to insert one blank
line immediately before the opening ```bash and one blank line immediately after
the closing ``` so the fenced block is separated from the list paragraph.
In @.claude/agents/ralph_specialists/python_specialist.md:
- Around line 64-82: The nested triple-fenced code block in the markdown (the
outer block that contains the FastAPI example including the sync_equity_bars
function and its code using EquityBarsRequest/EquityBarsResponse) must use a
4-backtick fence and have a blank line before the inner ```python fence to
satisfy markdownlint; update the outer fence that currently surrounds the Python
snippet to ```` (four backticks) and insert a blank line immediately before the
inner ```python fence so the example renders correctly and resolves MD031/MD040.
In @.claude/agents/ralph_specialists/risk_specialist.md:
- Around line 212-214: The heading and text use "Rate limiting" as a compound
adjective; update occurrences such as the heading "Rate limiting missing" and
the mitigation line "Add rate limiting middleware (X requests per minute)" to
hyphenate the compound adjective as "Rate-limiting" and "rate-limiting
middleware" respectively so the phrase is grammatically correct.
In @.claude/agents/ralph_specialists/rust_specialist.md:
- Line 77: Fenced code blocks at the noted positions are missing language
identifiers (MD040); update each missing fence (the block around line 77 and the
blocks at 135 and 160) by adding the appropriate language tag (e.g., ```rust,
```toml, ```json, or ```text) to the opening fence, and if any nested fenced
blocks are used, ensure they are either given explicit language tags or
restructured to avoid ambiguous closing fences; locate the three anonymous
fences in ralph_specialists/rust_specialist.md and add the correct language
identifiers consistent with the contained content.
- Around line 48-77: The markdown has MD031 violations due to missing blank
lines around fenced code blocks in
.claude/agents/ralph_specialists/rust_specialist.md; fix by adding a blank line
before and after each triple-backtick fenced block (e.g., the bash block
containing Grep(...) / Read(...) and the subsequent ```markdown block introduced
at "3. **Provide specific answer**"), and apply the same pattern wherever fenced
blocks are nested in list items (ensure one blank line separating list text and
the opening ``` and another blank line after the closing ```).
In `@CLAUDE.md`:
- Around line 208-222: The Event schema fenced JSON block in CLAUDE.md violates
markdownlint MD031; add a blank line before the "**Event schema:**" fenced block
and another blank line after the closing ```json``` fence so there is an empty
line separating the surrounding text and the code fence (update the section
containing the "Event schema:" heading and the ```json ... ``` block
accordingly).
In `@tools/ralph_marketplace_budget.py`:
- Line 13: Replace the Unicode multiplication sign (×) with the ASCII character
'x' in the comment "Allocation is based on combined score: weight × efficiency."
(and the other identical occurrence at the second location) so it reads
"Allocation is based on combined score: weight x efficiency."; update both
occurrences in tools/ralph_marketplace_budget.py where that exact comment
appears.
- Line 6: Remove the unused import BotState from the import statement in
tools.ralph_marketplace_budget.py: change the line that currently imports
"BotState, MarketplaceState" from tools.ralph_marketplace_state to only import
"MarketplaceState" so that BotState is no longer referenced in the module
imports.
- Around line 72-74: The loop that enforces a minimum of 1 for each allocation
can be simplified: replace the conditional assignment in the for-loop over
integer_allocations (variable name integer_allocations, loop variable bot_id)
with using max(current_value, 1) to set the value, i.e., assign
integer_allocations[bot_id] = max(integer_allocations[bot_id], 1) (or use a dict
comprehension mapping each bot_id to max(value, 1)) to achieve the same behavior
more concisely.
- Line 117: The f-string in the lines.append call exceeds the 88-char limit;
modify the code that builds the warning string (the lines.append call
referencing total_allocated and state.total_budget_pool) to produce a shorter
line under 88 chars — e.g., split the message across multiple concatenated
strings or use a shorter template like "WARNING: Total allocated (%s) != pool
(%s)" % (total_allocated, state.total_budget_pool) or build the message in a
separate variable before appending so the resulting source line length stays
within 88 characters.
- Around line 71-74: The current loop that enforces a minimum of 1 on
integer_allocations runs after remainder distribution and can make the sum
exceed total_budget_pool; update the allocation algorithm so minimum allocation
is enforced before distributing the remainder (or, alternatively, when you bump
any allocation from 0 to 1, decrement the remainder/adjust other allocations to
keep the zero-sum invariant). Locate the integer_allocations structure and the
remainder-distribution code in this module (the loop that sets
integer_allocations[bot_id] < 1 -> 1 and the code that computes/distributes
remainder) and either (a) run the minimum-enforcement pass immediately after
initial proportional integer truncation and before remainder logic, or (b) when
bumping zeros to ones, reduce the remainder or steal from largest allocations so
that sum(integer_allocations) == total_budget_pool remains true.
In `@tools/ralph_marketplace_orchestrator.py`:
- Line 26: The three print statements use an unnecessary f-string prefix for
plain strings (e.g., print(f"\nConfiguration:"), and the similar prints flagged
at the other locations); remove the leading f from those literals so they become
regular strings (e.g., print("\nConfiguration:")) to avoid misleading use of
f-strings — locate the print calls in tools/ralph_marketplace_orchestrator.py
that match print(f"\nConfiguration:"), and the two other flagged print(...)
occurrences and drop the 'f' prefix.
- Line 12: Remove the unused import by deleting the import statement that brings
in format_weight_update_summary from tools.ralph_marketplace_weights in
tools/ralph_marketplace_orchestrator.py; locate the line "from
tools.ralph_marketplace_weights import format_weight_update_summary" and remove
it (or alternatively import only the symbols actually used) so the unused symbol
format_weight_update_summary is not imported.
- Around line 52-58: The local variable config returned by
state_manager.load_config() in display_marketplace_status is unused; remove the
call or rename it to a throwaway (e.g., _config) to silence the unused variable
warning — locate the try block that calls state_manager.load_state() and
state_manager.load_config() and either delete the state_manager.load_config()
line or change the assigned name to _config so no behavior changes occur.
- Around line 109-126: The reset_marketplace function performs a destructive
operation without confirmation; update reset_marketplace to require explicit
user consent by adding an interactive yes/no prompt (or accept a --force
boolean/flag parameter) before calling MarketplaceStateManager.reset_state, and
abort without calling reset_state if the user declines; ensure the prompt text
clearly states that all event history will be deleted and document or wire the
new --force option into the CLI entrypoint that invokes reset_marketplace so
automated runs can bypass the prompt.
- Around line 185-190: Several print() calls use an unnecessary f-string prefix;
remove the leading f from any print literal that has no {} placeholders (e.g.,
the lines printing "The arbiter will now orchestrate the competition:", " 3.
Evaluate and rank proposals", " 4. Implement top-ranked proposal", and " 5.
Update marketplace state based on outcome") while keeping the f-prefix on prints
that do interpolate variables (the lines that reference issue_number and
config['num_bots']). Locate the print calls that include issue_number and
config['num_bots'] to ensure those remain f-strings, and update the other
print() calls to regular string literals.
In `@tools/ralph_marketplace_state.py`:
- Around line 138-144: The loop that processes events in
tools/ralph_marketplace_state.py currently ignores events whose bot_id is
missing from bots (the for event in events block using bot_id, outcome,
weight_delta), which can hide configuration drift; update the handling so that
when bot_id not in bots you emit a warning (use the module's logger or a
designated logger) including the missing bot_id and relevant event details
(e.g., outcome and weight_delta) for traceability, then continue to the next
event rather than silently skipping.
- Line 202: The timestamp uses naive datetime.now() for the last_updated field;
make it timezone-aware by using datetime with an explicit timezone (e.g.,
datetime.now(timezone.utc)) and update the imports accordingly (ensure timezone
is imported or use datetime.timezone); apply this change for the occurrences
that set last_updated (the assignment using
last_updated=datetime.now().isoformat() and the similar occurrence around line
281) so all event timestamps are emitted as ISO8601 strings with timezone
information.
- Around line 99-106: The loop that opens files from events_dir and calls
json.load can raise json.JSONDecodeError for corrupted files; update the code
that populates events (the loop using events_dir.glob and json.load) to catch
json.JSONDecodeError (and optionally other Exceptions), log a warning including
the event_file name and error details, skip the offending file, and continue;
ensure the rest of the logic (events.append and events.sort by e["timestamp"])
is unchanged so valid events are still collected and returned.
- Around line 161-170: The running-average update in the "if 'accuracy' in
event" block incorrectly uses total_implementations
(bot.implementations_succeeded + bot.implementations_failed) as the denominator,
which treats missing accuracy as 0; change the logic to use a separate counter
for only implementations with accuracy (e.g., add or use bot.accuracy_count) or
only increment the denominator when event contains "accuracy". Specifically, in
the block guarded by if "accuracy" in event, update bot.average_accuracy using
bot.accuracy_count (increment it when accuracy present) instead of
total_implementations so the average reflects only entries with accuracy.
In `@tools/ralph_marketplace_weights.py`:
- Around line 87-89: The code raises ValueError directly when bot_rank is None;
follow repo convention by first assigning the error string to a variable named
message and then raise ValueError(message). Update the conditional that checks
bot_rank (and references bot_id) to build message = f"Bot {bot_id} not found in
rankings" and then raise ValueError(message) instead of raising inline.
- Around line 61-67: The function determine_outcome_type currently accepts the
boolean flags is_replan and resubmitted_same as positional parameters which
triggers Ruff FBT warnings; update the signature so these two flags are
keyword-only (for example by adding a bare * before them) so callers must pass
is_replan=... and resubmitted_same=..., and adjust any call sites to use keyword
arguments if needed; keep the function name determine_outcome_type and the
default values False intact.
Address PR #739 review feedback: - Remove unused imports (BotState, format_weight_update_summary) - Add encoding="utf-8" to all file operations (PEP 597) - Replace Unicode × with ASCII * in comments (RUF002) - Fix budget allocation logic to prevent exceeding total pool - Fix outcome type matching to handle all OutcomeType variants - Add warning for events with unknown bot_id - Use timezone-aware datetime (UTC) for timestamps - Fix accuracy calculation to only count events with accuracy - Make boolean parameters keyword-only in determine_outcome_type - Use message variable for ValueError per repo convention - Fix line length issues (E501) Root cause: Initial implementation had several code quality issues flagged by static analysis tools and missing best practices for file I/O encoding. Fix: Applied all suggested fixes from CodeRabbit, Greptile, and Copilot reviews. The most critical fix was the budget allocation logic which could violate the zero-sum invariant by enforcing minimum allocation after remainder distribution. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Address PR #739 review feedback: - Remove version number from "Claude Sonnet 4.5" co-author string - Replace manual event file creation with MarketplaceStateManager.record_event() - Add blank lines and fence language in replan context block - Add blank line before Event schema JSON fence in CLAUDE.md Root cause: Markdown linting rules require blank lines around code fences and headings for proper rendering. Hardcoded version numbers become stale. Fix: Applied markdownlint corrections and updated event recording to use the proper state management API instead of manual file operations. Co-Authored-By: Claude Sonnet <noreply@anthropic.com>
- Add blank lines around fenced code blocks (MD031) - Add language identifiers to code fences (MD040) - Wrap file paths in backticks (MD050) - Hyphenate compound adjectives Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add /dev/tty availability check in marketplace reset command - Document 'promise' tag in markdownlint configuration Root cause: The reset command used /dev/tty without checking if it's available, which would fail in non-interactive environments (CI, pipes). The 'promise' HTML tag needed documentation explaining its purpose. Fix: Added conditional check for terminal availability with clear error message. Added inline comment explaining the custom promise tag used by Ralph workflow for completion signaling. Co-Authored-By: Claude Sonnet <noreply@anthropic.com>
- Remove unused config variable in display_marketplace_status - Fix line length issues (E501) by splitting long strings - Use datetime.UTC alias instead of timezone.utc (UP017) - Add ruff configuration to tools/pyproject.toml to ignore acceptable warnings for CLI tools (T201 print statements, complexity warnings) All lint checks now pass. Co-Authored-By: Claude Sonnet <noreply@anthropic.com>
forstmeier
dismissed stale reviews from coderabbitai[bot] and chrisaddy
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| etc/ | ||
| ./ralph/marketplace.json |
Contributor
There was a problem hiding this comment.
Paths should not start with ./ in .gitignore. Leading ./ is treated literally, so these patterns won't match files in .ralph/.
Suggested change
| etc/ | |
| ./ralph/marketplace.json | |
| .ralph/marketplace.json | |
| .ralph/.state_version |
Prompt To Fix With AI
This is a comment left during a code review.
Path: .gitignore
Line: 23:24
Comment:
Paths should not start with `./` in `.gitignore`. Leading `./` is treated literally, so these patterns won't match files in `.ralph/`.
```suggestion
.ralph/marketplace.json
.ralph/.state_version
```
How can I resolve this? If you propose a fix, please make it concise.
Contributor
There was a problem hiding this comment.
Actionable comments posted: 8
🤖 Fix all issues with AI agents
In @.claude/agents/ralph_broker.md:
- Around line 69-171: Fix markdownlint issues by ensuring fenced code blocks
have blank lines before/after the triple backticks, add a language tag (use
```python for Task blocks and ```json for JSON blocks), normalize ordered list
numbering to use consistent "1." style (or correct incremental numbers) and
remove unintended leading hyphens before code examples, and correct emphasis
spacing (remove extra spaces inside * * or _ _). Locate the Task(...) snippet
and the JSON example blocks in the Proposal Evaluation section and apply these
fixes to the other fenced blocks referenced (roughly the blocks around the shown
Task example and the ranges noted) so MD031, MD040, MD029, and MD037 are
resolved.
- Around line 323-350: Rewrite the replan-round section to remove repetitive
phrasing and normalize list numbering to a consistent ordered style (e.g.,
1./2./3.), rephrase occurrences like “fails again” to a single concise phrase
such as “replan fails twice” or “replan fails” with context, and keep
weight-change bullets short and parallel (reference the “Failed bidder” and “If
replan succeeds / If replan fails again” lines to consolidate into “If replan
succeeds” and “If replan fails twice” alternatives); also ensure there is a
blank line before and after the fenced human-intervention code block so the
snippet is separated from surrounding text and adjust the line that penalizes
resubmitting the same proposal to read clearly (e.g., “If bidder resubmits the
same proposal after failure: -0.05 weight”).
In @.gitignore:
- Around line 22-26: The current broad ignore "**/*.json" hides tracked files
like .ralph/config.json and .ralph/events/*.json; instead either remove the
global "**/*.json" rule and add targeted ignore patterns for only the generated
JSON files, or keep the global rule but add negation entries to unignore the
tracked files (e.g. add !.ralph/config.json and !.ralph/events/*.json and any
other tracked .ralph/*.json you need), ensuring .ralph/marketplace.json and
.ralph/events/*.json remain tracked.
In @.ralph/marketplace.md:
- Around line 7-14: Update the fenced code blocks in .ralph/marketplace.md (the
directory tree block, the “[timestamp]-[bot_id]-[outcome].json” filename block,
and the other affected blocks at the ranges noted) by ensuring each
triple-backtick fence has a blank line before and after the code block and
include a language identifier (use "text") after the opening ```; specifically
modify the directory tree block, the standalone filename block, and the other
code sections referenced (around lines 46-54, 97-120, 135-143, 149-165) so they
follow the pattern ```text\n<code>\n``` with blank lines separating the fences
from surrounding paragraphs.
In `@tools/ralph_marketplace_budget.py`:
- Around line 31-35: The equal-score branch currently casts equal_budget to int
which truncates remainders and under-allocates the pool; change the logic when
total_combined == 0 to compute an integer base = total_budget // len(state.bots)
and remainder = total_budget - base * len(state.bots), then return a mapping
that gives base to every bot_id in state.bots and adds +1 to the first
`remainder` bot_ids (use a deterministic order such as the iteration order of
state.bots or sorted(state.bots)) so all values are ints and sum exactly to
total_budget; update any references to `equal_budget`, `total_combined`, and
`state.bots` accordingly.
- Line 6: The bare import line fails when this file is loaded as
tools.ralph_marketplace_budget; update the import for MarketplaceState to try
the top-level import first and fall back to the package-relative import. Replace
the single-import in tools.ralph_marketplace_budget (the line importing
MarketplaceState) with a try/except that attempts "from ralph_marketplace_state
import MarketplaceState" and on ImportError uses "from .ralph_marketplace_state
import MarketplaceState" so both bare and tools-prefixed imports work.
In `@tools/ralph_marketplace_state.py`:
- Around line 192-247: The redistribution only handles deficit but not excess,
and it uses the wrong sign when applying deficit; update the iterative clamp
logic (the loop that builds clamped, computes excess/deficit and then applies
redistribution) to handle both cases: when deficit>0, compute each absorber's
headroom = max_weight - clamped[bot_id], sum headroom and distribute
min(deficit, total_headroom) proportionally by headroom (increase
clamped[bot_id] by allocated amount, capped at max_weight); when excess>0,
compute each donor's reducible = clamped[bot_id] - min_weight, sum reducible and
reduce clamped[bot_id] proportionally by min(excess, total_reducible) (decrease
toward min_weight); ensure you use bots and clamped keys consistently and
recompute totals each iteration so clamped always stays within
[min_weight,max_weight] before assigning back to bot.weight and then perform the
final normalization step.
- Around line 98-106: Replace the print-based warning in the JSONDecodeError
handler with structlog's exception logging: obtain a logger via
structlog.get_logger() (no args) and call logger.exception(...) inside the
except block for json.JSONDecodeError to log the failure and stack trace when
reading from self.events_dir.glob("*.json") (refer to the try/except around
event = json.load(f) and the events list append). Ensure the log message
includes the event_file.name and the exception context so the stack trace is
recorded instead of printing.
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| 3. Output extracted requirements in JSON format: | ||
| ```json | ||
| { | ||
| "explicit_requirements": [ | ||
| { | ||
| "id": "req_1", | ||
| "checkbox": "Add user authentication endpoint", | ||
| "components": [ | ||
| "Create new HTTP endpoint", | ||
| "Endpoint purpose: authentication", | ||
| "Must be accessible via REST API" | ||
| ] | ||
| } | ||
| ], | ||
| "implicit_requirements": [ | ||
| { | ||
| "id": "req_implicit_1", | ||
| "text": "Must have test coverage for authentication", | ||
| "reasoning": "Security-critical code requires tests per CLAUDE.md" | ||
| } | ||
| ] | ||
| } | ||
| ``` | ||
|
|
||
| ### Phase 2: Proposal Evaluation | ||
|
|
||
| 1. Spawn {{num_bots}} bidders in parallel using Task tool: | ||
| ``` | ||
| Task( | ||
| subagent_type="general-purpose", | ||
| prompt="You are a bidder competing in the Ralph marketplace. Read the extracted requirements and submit a lightweight proposal...", | ||
| description="Bidder proposal" | ||
| ) | ||
| ``` | ||
|
|
||
| 2. Receive proposals from bidders (identities hidden as proposal_1, proposal_2, proposal_3) | ||
|
|
||
| 3. Score each proposal on 5 dimensions: | ||
|
|
||
| **Spec Alignment (32%)** | ||
| - Checkbox coverage: checkboxes_addressed / total_checkboxes | ||
| - Component coverage: components_addressed / total_components | ||
| - Implicit requirement coverage: implicit_requirements_addressed / total_implicit | ||
| - Weighted score: (checkbox * 0.5) + (component * 0.3) + (implicit * 0.2) | ||
|
|
||
| **Technical Quality (22%)** | ||
| - Does it match existing architectural patterns? (Read affected files to verify) | ||
| - Does it create circular dependencies or tight coupling? | ||
| - Is it maintainable and follows codebase conventions? | ||
| - Subjective rating 0.0-1.0 with explicit reasoning | ||
|
|
||
| **Innovation (15%)** | ||
| - Is the approach novel or elegant? | ||
| - Does it simplify the problem space? | ||
| - Is it simpler than obvious alternatives? | ||
| - Subjective rating 0.0-1.0 with explicit reasoning | ||
|
|
||
| **Risk Assessment (21%)** | ||
| - Files affected: fewer = lower risk (normalize to 0-1) | ||
| - Breaking changes: does it modify public APIs? (check signatures) | ||
| - Security implications: proper risk assessment conducted? | ||
| - Score = 1 - (normalized_risk_factors) | ||
|
|
||
| **Efficiency (10%)** | ||
| - Estimated lines of code | ||
| - Number of files touched | ||
| - Number of modules affected | ||
| - Score = 1 - (normalized_complexity) | ||
|
|
||
| 4. Calculate total score for each proposal: | ||
| ```python | ||
| total_score = ( | ||
| spec_score * 0.32 + | ||
| technical_quality_score * 0.22 + | ||
| innovation_score * 0.15 + | ||
| risk_score * 0.21 + | ||
| efficiency_score * 0.10 | ||
| ) | ||
| ``` | ||
|
|
||
| 5. Rank proposals by total score | ||
| - Tie-breaker: Earlier submission timestamp wins | ||
|
|
||
| 6. Output rankings with transparent scores and reasoning: | ||
| ```json | ||
| { | ||
| "rankings": [ | ||
| { | ||
| "rank": 1, | ||
| "proposal_id": "proposal_2", | ||
| "total_score": 0.87, | ||
| "scores": { | ||
| "spec_alignment": 0.92, | ||
| "technical_quality": 0.85, | ||
| "innovation": 0.80, | ||
| "risk": 0.90, | ||
| "efficiency": 0.88 | ||
| }, | ||
| "reasoning": "Strong spec alignment with comprehensive component coverage. Elegant approach using existing middleware pattern. Low risk with minimal file changes." | ||
| } | ||
| ] | ||
| } | ||
| ``` |
Contributor
There was a problem hiding this comment.
Resolve markdownlint warnings for fenced blocks, list numbering, and emphasis spacing.
There are multiple MD031 (blank lines around fences), MD040 (missing fence language), MD029 (ordered list numbering), and MD037 (spaces inside emphasis) warnings in these sections. Example fix for the Task block:
📌 Example fix (apply similarly to other fenced blocks)
-2. Receive proposals from bidders (identities hidden as proposal_1, proposal_2, proposal_3)
-```
-Task(
- subagent_type="general-purpose",
- prompt="You are a bidder competing in the Ralph marketplace. Read the extracted requirements and submit a lightweight proposal...",
- description="Bidder proposal"
-)
-```
+2. Receive proposals from bidders (identities hidden as proposal_1, proposal_2, proposal_3)
+
+```python
+Task(
+ subagent_type="general-purpose",
+ prompt="You are a bidder competing in the Ralph marketplace. Read the extracted requirements and submit a lightweight proposal...",
+ description="Bidder proposal"
+)
+```Also applies to: 183-223, 256-305, 309-321
🧰 Tools
🪛 markdownlint-cli2 (0.20.0)
[warning] 70-70: Fenced code blocks should be surrounded by blank lines
(MD031, blanks-around-fences)
[warning] 96-96: Fenced code blocks should be surrounded by blank lines
(MD031, blanks-around-fences)
[warning] 96-96: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
[warning] 104-104: Ordered list item prefix
Expected: 1; Actual: 2; Style: 1/2/3
(MD029, ol-prefix)
[warning] 106-106: Ordered list item prefix
Expected: 2; Actual: 3; Style: 1/2/3
(MD029, ol-prefix)
[warning] 112-112: Spaces inside emphasis markers
(MD037, no-space-in-emphasis)
[warning] 112-112: Spaces inside emphasis markers
(MD037, no-space-in-emphasis)
[warning] 138-138: Ordered list item prefix
Expected: 3; Actual: 4; Style: 1/2/3
(MD029, ol-prefix)
[warning] 139-139: Fenced code blocks should be surrounded by blank lines
(MD031, blanks-around-fences)
[warning] 149-149: Ordered list item prefix
Expected: 1; Actual: 5; Style: 1/2/3
(MD029, ol-prefix)
[warning] 152-152: Ordered list item prefix
Expected: 2; Actual: 6; Style: 1/2/3
(MD029, ol-prefix)
[warning] 153-153: Fenced code blocks should be surrounded by blank lines
(MD031, blanks-around-fences)
🤖 Prompt for AI Agents
In @.claude/agents/ralph_broker.md around lines 69 - 171, Fix markdownlint
issues by ensuring fenced code blocks have blank lines before/after the triple
backticks, add a language tag (use ```python for Task blocks and ```json for
JSON blocks), normalize ordered list numbering to use consistent "1." style (or
correct incremental numbers) and remove unintended leading hyphens before code
examples, and correct emphasis spacing (remove extra spaces inside * * or _ _).
Locate the Task(...) snippet and the JSON example blocks in the Proposal
Evaluation section and apply these fixes to the other fenced blocks referenced
(roughly the blocks around the shown Task example and the ranges noted) so
MD031, MD040, MD029, and MD037 are resolved.
Comment on lines
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| 2. Request new proposals from all bidders: | ||
| - **Failed bidder MUST submit new proposal** (cannot resubmit same) | ||
| - Other bidders CAN resubmit previous proposals OR submit new ones | ||
| - Bidders see full failure context to inform revisions | ||
|
|
||
| 3. Return to Phase 2 (Proposal Evaluation) with new proposals | ||
|
|
||
| 4. Weight updates for replan: | ||
| - Failed bidder: -0.15 weight (heavy penalty for wrong prediction) | ||
| - If replan succeeds: | ||
| - New winner: +0.12 weight (bonus for learning from failure) | ||
| - If replan fails again: | ||
| - Failed bidder again: -0.20 weight (repeated failure) | ||
| - All bidders: -0.05 weight (collective failure) | ||
| - If bidder resubmits same proposal after failure: -0.05 weight (not adapting) | ||
|
|
||
| 5. If replan round also fails → Human intervention: | ||
| ```bash | ||
| gh issue edit {{issue_number}} --add-label "attention-needed" | ||
| gh issue comment {{issue_number}} --body "## Marketplace Failure | ||
|
|
||
| Both initial and replan rounds failed. Manual intervention required. | ||
|
|
||
| **Initial Failure:** [details] | ||
| **Replan Failure:** [details] | ||
|
|
||
| Check branch: \`{{branch_name}}\`" | ||
| ``` |
Contributor
There was a problem hiding this comment.
Tighten replan-round wording and numbering.
This block has repeated “again” wording and MD029 list numbering issues; also ensure blank lines around the fenced block near the human-intervention snippet. Consider rephrasing to reduce repetition (e.g., “Replan fails twice”) and normalize list numbering to 1/2/3 style.
🧰 Tools
🪛 LanguageTool
[style] ~335-~335: This sentence contains multiple usages of the word “again”. Consider removing or replacing it.
Context: ...eplan fails again: - Failed bidder again: -0.20 weight (repeated failure) -...
(REPETITION_OF_AGAIN)
🪛 markdownlint-cli2 (0.20.0)
[warning] 323-323: Ordered list item prefix
Expected: 1; Actual: 2; Style: 1/2/3
(MD029, ol-prefix)
[warning] 328-328: Ordered list item prefix
Expected: 2; Actual: 3; Style: 1/2/3
(MD029, ol-prefix)
[warning] 330-330: Ordered list item prefix
Expected: 3; Actual: 4; Style: 1/2/3
(MD029, ol-prefix)
[warning] 339-339: Ordered list item prefix
Expected: 4; Actual: 5; Style: 1/2/3
(MD029, ol-prefix)
[warning] 340-340: Fenced code blocks should be surrounded by blank lines
(MD031, blanks-around-fences)
🤖 Prompt for AI Agents
In @.claude/agents/ralph_broker.md around lines 323 - 350, Rewrite the
replan-round section to remove repetitive phrasing and normalize list numbering
to a consistent ordered style (e.g., 1./2./3.), rephrase occurrences like “fails
again” to a single concise phrase such as “replan fails twice” or “replan fails”
with context, and keep weight-change bullets short and parallel (reference the
“Failed bidder” and “If replan succeeds / If replan fails again” lines to
consolidate into “If replan succeeds” and “If replan fails twice” alternatives);
also ensure there is a blank line before and after the fenced human-intervention
code block so the snippet is separated from surrounding text and adjust the line
that penalizes resubmitting the same proposal to read clearly (e.g., “If bidder
resubmits the same proposal after failure: -0.05 weight”).
Comment on lines
22
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+26
| **/*.json | ||
| notes.md | ||
| etc/ | ||
| ./ralph/marketplace.json | ||
| ./ralph/.state_version | ||
| .scratchpad/ |
Contributor
There was a problem hiding this comment.
Unignore tracked marketplace JSON files.
Line 22 ignores all JSON, which will also ignore .ralph/config.json and .ralph/events/*.json that the marketplace docs say should be tracked. This breaks event history and config versioning.
🛠️ Proposed fix
**/*.json
+!.ralph/config.json
+!.ralph/events/*.json
./ralph/marketplace.json
./ralph/.state_version
.scratchpad/🤖 Prompt for AI Agents
In @.gitignore around lines 22 - 26, The current broad ignore "**/*.json" hides
tracked files like .ralph/config.json and .ralph/events/*.json; instead either
remove the global "**/*.json" rule and add targeted ignore patterns for only the
generated JSON files, or keep the global rule but add negation entries to
unignore the tracked files (e.g. add !.ralph/config.json and
!.ralph/events/*.json and any other tracked .ralph/*.json you need), ensuring
.ralph/marketplace.json and .ralph/events/*.json remain tracked.
Comment on lines
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+14
| ``` | ||
| .ralph/ | ||
| ├── config.json # Marketplace configuration (tracked in git) | ||
| ├── marketplace.json # Cached state (gitignored, regenerated from events) | ||
| ├── .state_version # Event count for cache invalidation (gitignored) | ||
| └── events/ # Append-only event log (tracked in git) | ||
| └── [timestamp]-[bot_id]-[outcome].json | ||
| ``` |
Contributor
There was a problem hiding this comment.
Fix fenced code blocks to satisfy markdownlint (MD031/MD040).
Several fences are missing blank lines and some are missing language identifiers. Please update all affected blocks (directory tree, event filename, commands, troubleshooting).
✅ Example fixes (apply similarly elsewhere)
-```
+.
+```text
.ralph/
├── config.json # Marketplace configuration (tracked in git)
├── marketplace.json # Cached state (gitignored, regenerated from events)
├── .state_version # Event count for cache invalidation (gitignored)
└── events/ # Append-only event log (tracked in git)
└── [timestamp]-[bot_id]-[outcome].json
-```
+```-```
-[timestamp]-[bot_id]-[outcome].json
-```
+```text
+[timestamp]-[bot_id]-[outcome].json
+```Also applies to: 46-54, 97-120, 135-143, 149-165
🧰 Tools
🪛 markdownlint-cli2 (0.20.0)
[warning] 7-7: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
🤖 Prompt for AI Agents
In @.ralph/marketplace.md around lines 7 - 14, Update the fenced code blocks in
.ralph/marketplace.md (the directory tree block, the
“[timestamp]-[bot_id]-[outcome].json” filename block, and the other affected
blocks at the ranges noted) by ensuring each triple-backtick fence has a blank
line before and after the code block and include a language identifier (use
"text") after the opening ```; specifically modify the directory tree block, the
standalone filename block, and the other code sections referenced (around lines
46-54, 97-120, 135-143, 149-165) so they follow the pattern ```text\n<code>\n```
with blank lines separating the fences from surrounding paragraphs.
| Handles iteration budget allocation across bidders based on weights and efficiency. | ||
| """ | ||
|
|
||
| from ralph_marketplace_state import MarketplaceState |
Contributor
There was a problem hiding this comment.
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fd -t f "ralph_marketplace" --type f
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# Check the structure of tools directory
ls -la tools/ | head -20
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# Look for marketplace loop and python -c usage
rg -n "python -c" --type md --type py --type txt -A2 -B2Repository: oscmcompany/fund
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rg "^from |^import " tools/ralph_marketplace_budget.py -n
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# Check imports in ralph_marketplace_state.py
rg "^from |^import " tools/ralph_marketplace_state.py -n
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rg "^from |^import " tools/ralph_marketplace_orchestrator.py tools/ralph_marketplace_weights.py -nRepository: oscmcompany/fund
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🏁 Script executed:
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# Get more context from maskfile.md around the python -c call
rg -B5 -A10 "from tools.ralph_marketplace_budget import allocate_budgets" maskfile.mdRepository: oscmcompany/fund
Length of output: 518
Fix module import to support both bare and tools-prefixed imports.
When this module is imported as tools.ralph_marketplace_budget (in the marketplace loop's python -c snippet), from ralph_marketplace_state import MarketplaceState fails because there is no top-level module with that name. Add a fallback import:
Suggested fix
-from ralph_marketplace_state import MarketplaceState
+try:
+ from ralph_marketplace_state import MarketplaceState
+except ModuleNotFoundError:
+ from tools.ralph_marketplace_state import MarketplaceState📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| from ralph_marketplace_state import MarketplaceState | |
| try: | |
| from ralph_marketplace_state import MarketplaceState | |
| except ModuleNotFoundError: | |
| from tools.ralph_marketplace_state import MarketplaceState |
🤖 Prompt for AI Agents
In `@tools/ralph_marketplace_budget.py` at line 6, The bare import line fails when
this file is loaded as tools.ralph_marketplace_budget; update the import for
MarketplaceState to try the top-level import first and fall back to the
package-relative import. Replace the single-import in
tools.ralph_marketplace_budget (the line importing MarketplaceState) with a
try/except that attempts "from ralph_marketplace_state import MarketplaceState"
and on ImportError uses "from .ralph_marketplace_state import MarketplaceState"
so both bare and tools-prefixed imports work.
Comment on lines
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| if total_combined == 0: | ||
| # All bots have zero score, distribute equally | ||
| equal_budget = total_budget / len(state.bots) | ||
| return {bot_id: int(equal_budget) for bot_id in state.bots} | ||
|
|
Contributor
There was a problem hiding this comment.
Equal-score path under-allocates the total pool.
Casting equal_budget to int truncates the remainder, so total allocated can be less than total_budget (e.g., 10/3 → 9). This violates the fixed-pool guarantee.
🛠️ Suggested fix
- equal_budget = total_budget / len(state.bots)
- return {bot_id: int(equal_budget) for bot_id in state.bots}
+ bot_ids = sorted(state.bots)
+ equal_budget = total_budget // len(bot_ids)
+ remainder = total_budget % len(bot_ids)
+ allocations = {bot_id: equal_budget for bot_id in bot_ids}
+ for bot_id in bot_ids[:remainder]:
+ allocations[bot_id] += 1
+ return allocations📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| if total_combined == 0: | |
| # All bots have zero score, distribute equally | |
| equal_budget = total_budget / len(state.bots) | |
| return {bot_id: int(equal_budget) for bot_id in state.bots} | |
| if total_combined == 0: | |
| # All bots have zero score, distribute equally | |
| bot_ids = sorted(state.bots) | |
| equal_budget = total_budget // len(bot_ids) | |
| remainder = total_budget % len(bot_ids) | |
| allocations = {bot_id: equal_budget for bot_id in bot_ids} | |
| for bot_id in bot_ids[:remainder]: | |
| allocations[bot_id] += 1 | |
| return allocations |
🤖 Prompt for AI Agents
In `@tools/ralph_marketplace_budget.py` around lines 31 - 35, The equal-score
branch currently casts equal_budget to int which truncates remainders and
under-allocates the pool; change the logic when total_combined == 0 to compute
an integer base = total_budget // len(state.bots) and remainder = total_budget -
base * len(state.bots), then return a mapping that gives base to every bot_id in
state.bots and adds +1 to the first `remainder` bot_ids (use a deterministic
order such as the iteration order of state.bots or sorted(state.bots)) so all
values are ints and sum exactly to total_budget; update any references to
`equal_budget`, `total_combined`, and `state.bots` accordingly.
Comment on lines
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| for event_file in self.events_dir.glob("*.json"): | ||
| try: | ||
| with open(event_file, encoding="utf-8") as f: | ||
| event = json.load(f) | ||
| events.append(event) | ||
| except json.JSONDecodeError as e: | ||
| # Log warning and skip corrupted file | ||
| print(f"Warning: Skipping corrupted event file {event_file.name}: {e}") | ||
| continue |
Contributor
There was a problem hiding this comment.
🧹 Nitpick | 🔵 Trivial
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Use structlog's logger.exception() for JSON parse failures.
Printing in an exception handler drops the stack trace and violates the logging guideline. Prefer a structlog logger with exception():
🧰 Suggested fix
import json
from dataclasses import dataclass
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
+import structlog
+
+logger = structlog.get_logger()
...
- except json.JSONDecodeError as e:
- # Log warning and skip corrupted file
- print(f"Warning: Skipping corrupted event file {event_file.name}: {e}")
+ except json.JSONDecodeError:
+ logger.exception(
+ "Skipping corrupted event file",
+ event_file=event_file.name,
+ )
continueAs per coding guidelines: When logging after an exception, use logger.exception() to capture stack trace with the structlog package. (Use structlog.get_logger() without arguments to match the established pattern in this codebase.)
🤖 Prompt for AI Agents
In `@tools/ralph_marketplace_state.py` around lines 98 - 106, Replace the
print-based warning in the JSONDecodeError handler with structlog's exception
logging: obtain a logger via structlog.get_logger() (no args) and call
logger.exception(...) inside the except block for json.JSONDecodeError to log
the failure and stack trace when reading from self.events_dir.glob("*.json")
(refer to the try/except around event = json.load(f) and the events list
append). Ensure the log message includes the event_file.name and the exception
context so the stack trace is recorded instead of printing.
Comment on lines
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| # Apply weight constraints with iterative adjustment to maintain sum=1.0 | ||
| min_weight = config["weight_constraints"]["min"] | ||
| max_weight = config["weight_constraints"]["max"] | ||
|
|
||
| # Iteratively enforce constraints while maintaining normalized sum | ||
| max_iterations = 10 | ||
| for _ in range(max_iterations): | ||
| # Apply constraints | ||
| clamped = {} | ||
| excess = 0.0 | ||
| deficit = 0.0 | ||
|
|
||
| for bot_id, bot in bots.items(): | ||
| original = bot.weight | ||
| clamped[bot_id] = max(min_weight, min(max_weight, original)) | ||
|
|
||
| if clamped[bot_id] > original: | ||
| deficit += clamped[bot_id] - original | ||
| elif clamped[bot_id] < original: | ||
| excess += original - clamped[bot_id] | ||
|
|
||
| # If no changes needed, we're done | ||
| if excess == 0 and deficit == 0: | ||
| break | ||
|
|
||
| # Distribute excess to bots that need it (below min) or have room (below max) | ||
| if deficit > 0: | ||
| # Find bots that can absorb the deficit (not at max constraint) | ||
| can_absorb = [ | ||
| (bot_id, bot) | ||
| for bot_id, bot in bots.items() | ||
| if clamped[bot_id] < max_weight | ||
| ] | ||
|
|
||
| if can_absorb: | ||
| # Distribute proportionally among bots that can absorb | ||
| absorb_weights = {bot_id: bot.weight for bot_id, bot in can_absorb} | ||
| total_absorb = sum(absorb_weights.values()) | ||
|
|
||
| if total_absorb > 0: | ||
| for bot_id, _ in can_absorb: | ||
| proportion = absorb_weights[bot_id] / total_absorb | ||
| clamped[bot_id] = min( | ||
| max_weight, clamped[bot_id] - deficit * proportion | ||
| ) | ||
|
|
||
| # Apply clamped values | ||
| for bot_id, bot in bots.items(): | ||
| bot.weight = clamped[bot_id] | ||
|
|
||
| # Check if sum is close enough to 1.0 | ||
| total = sum(bot.weight for bot in bots.values()) | ||
| if abs(total - 1.0) < 1e-10: | ||
| break | ||
|
|
||
| # Final normalization to ensure exact sum of 1.0 |
Contributor
There was a problem hiding this comment.
Weight constraint redistribution can violate min/max bounds.
The current loop only redistributes when deficit > 0 and ignores pure “excess” cases (weights clamped down to max). That can leave totals < 1.0 and the final normalization may push weights above max_weight. Consider redistributing both deficit and excess based on available headroom:
🛠️ Suggested fix
- # Iteratively enforce constraints while maintaining normalized sum
+ # Iteratively enforce constraints while maintaining normalized sum
max_iterations = 10
for _ in range(max_iterations):
# Apply constraints
clamped = {}
excess = 0.0
deficit = 0.0
@@
# If no changes needed, we're done
if excess == 0 and deficit == 0:
break
- # Distribute excess to bots that need it (below min) or have room (below max)
+ # If some weights were increased to min, reduce others with room above min
if deficit > 0:
- # Find bots that can absorb the deficit (not at max constraint)
- can_absorb = [
- (bot_id, bot)
- for bot_id, bot in bots.items()
- if clamped[bot_id] < max_weight
- ]
-
- if can_absorb:
- # Distribute proportionally among bots that can absorb
- absorb_weights = {bot_id: bot.weight for bot_id, bot in can_absorb}
- total_absorb = sum(absorb_weights.values())
-
- if total_absorb > 0:
- for bot_id, _ in can_absorb:
- proportion = absorb_weights[bot_id] / total_absorb
- clamped[bot_id] = min(
- max_weight, clamped[bot_id] - deficit * proportion
- )
+ donors = {
+ bot_id: clamped[bot_id] - min_weight
+ for bot_id in clamped
+ if clamped[bot_id] > min_weight
+ }
+ donor_total = sum(donors.values())
+ if donor_total > 0:
+ for bot_id, room in donors.items():
+ reduction = deficit * (room / donor_total)
+ clamped[bot_id] = max(min_weight, clamped[bot_id] - reduction)
+
+ # If some weights were reduced to max, distribute excess to those below max
+ if excess > 0:
+ receivers = {
+ bot_id: max_weight - clamped[bot_id]
+ for bot_id in clamped
+ if clamped[bot_id] < max_weight
+ }
+ receiver_total = sum(receivers.values())
+ if receiver_total > 0:
+ for bot_id, room in receivers.items():
+ increase = excess * (room / receiver_total)
+ clamped[bot_id] = min(max_weight, clamped[bot_id] + increase)🤖 Prompt for AI Agents
In `@tools/ralph_marketplace_state.py` around lines 192 - 247, The redistribution
only handles deficit but not excess, and it uses the wrong sign when applying
deficit; update the iterative clamp logic (the loop that builds clamped,
computes excess/deficit and then applies redistribution) to handle both cases:
when deficit>0, compute each absorber's headroom = max_weight - clamped[bot_id],
sum headroom and distribute min(deficit, total_headroom) proportionally by
headroom (increase clamped[bot_id] by allocated amount, capped at max_weight);
when excess>0, compute each donor's reducible = clamped[bot_id] - min_weight,
sum reducible and reduce clamped[bot_id] proportionally by min(excess,
total_reducible) (decrease toward min_weight); ensure you use bots and clamped
keys consistently and recompute totals each iteration so clamped always stays
within [min_weight,max_weight] before assigning back to bot.weight and then
perform the final normalization step.
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Author
|
An interesting addition to this might be to grow the reward pool slowly on each successful implementation. Basically incrementing it by |
forstmeier
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Feb 3, 2026
This commit clarifies the todos.md workflow and removes obsolete temporary files: 1. Updated Task Management section in CLAUDE.md to clarify that todos.md is for general task tracking, not command-specific workflows. Commands like /update-pull-request use plan mode instead. Added note about reading file before writing to avoid errors. 2. Removed 7 temporary files from /tmp/: - 5 JSON files from PR #740 execution (pr_data.json, pr_metadata.json, review_threads.json, thread_responses.txt, pr740.json) - 2 obsolete shell scripts from PR #739 (reply_to_comments.sh, resolve_comments.sh) that used old REST API approach All PR work is committed and future commands will use scratchpad directory instead of /tmp/. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Collaborator
Author
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Closing this for now but we can address it later if we want to re-incorporate it in some form. |
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Overview
Changes
marketplaceMask commandstools/Context
This is an alternative proposal to what you brought up in #734, @chrisaddy. Basically, it's a "marketplace" pattern where "smart" bots use "dumb" bots to build proposals that are ranked and rewarded. I have not tested this out yet.
I'll leave it in draft state for now because even if this is broadly the directly we go, there's a bunch of cleanup I want to do on it.
proposal_transcript.md
proposal_followups.md
Summary by CodeRabbit
New Features
Refactor
Documentation
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