fix(kiro): count tokens by script and calibrate the context estimate from reported usage - #3476
Conversation
The Kiro context gauge read about 60% of what Kiro actually charges, so auto-compact engaged far too late on long conversations. Measured end to end by building real payloads through buildKiroPayload and scoring them against Kiro's own recorded charge: aggregate estimate/charged was 0.594, and it degraded with conversation length (0.643 at 4 messages -> 0.587 at 700), which is the signature of a per-entry cost that no per-character ratio can recover. Three causes, all fixed here. A single chars-per-token divisor cannot describe mixed text. Latin prose and code run near 2.8 chars/token; Hangul and Han run near 1.5. The old model divided the whole blob by one ratio and clamped to a denser one only when a SAMPLED CJK share crossed 30% — a cliff that real traffic (roughly 1-30% CJK) never triggered, fed by a stride sampler that could read a 1.6%-CJK payload as 100% CJK. Counting the two scripts exactly and adding them removes the threshold, the sampling error and the discontinuity together. The payload walker concatenated message text and ignored the JSON the wire actually carries. Per-entry keys and role framing cost real tokens proportional to entry count, and string escaping expands content by ~1.12x on measured bodies. Both are charged upstream and are now counted. Constants come from two independent recorded sources that agree: 5,799 pure-Latin samples pairing exact text with authoritative token counts aggregate to 2.80 chars/token, and recorded request bodies charge ~2.43 bytes per token at ~1.12 bytes per counted character. CJK solves to ~1.50. Aggregate estimate/charged improves 0.594 -> 0.884, and the length-dependent drift is gone (0.918 -> 0.879 across the same range). Estimates rise, so auto-compact now fires nearer the real boundary; over-counting only compacts early, while under-counting risks context overflow. Tests that pinned the old constants are updated, with new regressions for continuity across the former 30% cliff and for the sampling alias.
…sage The estimator is a fixed heuristic: constants derived from recorded traffic, applied to every conversation identically. It is close on average and necessarily wrong in the particular, because how densely a prompt tokenizes depends on what is in it — a Korean design discussion and a repository of minified JSON do not share a ratio. Kiro already tells us the answer. Mid-stream it reports contextUsagePercentage, which against a known window is an authoritative token count for the exact payload just sent. That number was used once, as a floor under the current turn, and then discarded — so every turn re-derived the conversation's rate from scratch and mispredicted it the same way. This keeps it. After a turn that produced both an estimate and a reported percentage, the realised charged/estimated ratio is folded into a per-conversation correction and applied to that conversation's next turn. Four properties keep it from making the gauge worse. The factor is clamped, so one anomalous or hostile reading cannot distort the estimate without bound. Observations are smoothed, so a single cache-affected turn cannot swing it. State is conversation-scoped with an eviction cap and no persistence. And the existing upstream floor is untouched: calibration only sharpens the estimate before upstream reports, never lowers a value upstream has justified. The correction is learned against the RAW heuristic output rather than the already-corrected estimate. Learning from the corrected value would make the factor measure its own residual error, closing part of the remaining gap each round and stalling short of the truth — simulated, it converged to 0.861 of the real charge instead of 1.0. Against a conversation charged 1.35x, it now reaches 1.000 from the second turn. Kept out of lib/token-estimate deliberately: that module is pure and shared by every provider. This is Kiro-specific state and lives with the Kiro adapter.
…imator The comment above ADMISSION_TOLERANCE justified 2.5 by a sampling artifact that no longer exists: cjkRatio read every stride-th character, so fixed-width records could sample as 100% CJK and inflate an estimate 1.6x. CJK characters are now counted exactly, so that divergence is gone and the margin is not buying it anymore. The constant stays 2.5, for a different and now-stated reason. Segmenting by script raises a pure-Latin estimate 1.25x and a Korean-dominant one up to 1.67x, so measured on the old estimator's scale the same multiplier now behaves like ~2.0x for Latin and ~1.5x for Korean. That is the intended direction — the estimate is closer to what providers actually charge, so the bound is tighter and more honest — and it still refuses the #1412 compounding shape several times over. Leaving the old text in place would have left the next reader sizing this margin against a mechanism that was deleted.
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📝 WalkthroughWalkthroughKiro token estimation now separates Latin and CJK counting, includes serialized-payload overhead, and applies bounded per-conversation calibration after normally completed turns. ChangesKiro token estimation and calibration
Estimated code review effort: 4 (Complex) | ~45 minutes Merge Risk: 🔵 Low · up to Kiro calibration correctly aims to exclude fallback retries, but the regression test can pass without exercising that retry path. This is a bounded test-coverage gap with low merge risk. Sequence Diagram(s)sequenceDiagram
participant KiroBuild
participant KiroCalibration
participant KiroUsage
KiroBuild->>KiroBuild: estimateKiroPayloadInputTokens()
KiroBuild->>KiroCalibration: calibrateKiroEstimate(conversationId, raw estimate)
KiroCalibration-->>KiroBuild: calibrated estimate
KiroBuild->>KiroUsage: send request
KiroUsage-->>KiroBuild: charged context usage
KiroBuild->>KiroCalibration: recordKiroCalibration(conversationId, estimate, charged)
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✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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리뷰 · 우선순위 74 / 80이 PR은 지금 이번 브랜치는 그걸 세 덩어리로 나눕니다. 첫째, 라인 - 라인 - 라인 - 라인 - 라인 - 메인테이너의 판단이 필요한 지점
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| // nothing about how densely its payload tokenized. | ||
| const chargedFloor = contextUsageTotalFloor(); | ||
| if (chargedFloor !== undefined && contextInputEstimate !== undefined) { | ||
| recordKiroCalibration(returnedConversationId, contextInputEstimate, chargedFloor); |
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When a turn produces substantial output, chargedFloor is the absolute active-context checkpoint after the response, whereas contextInputEstimate covers only the request payload; this distinction is defined by the OcxUsage contract in src/types/request.ts:388-390. Dividing the former by the latter attributes generated output to prompt-tokenization error, so a conversation with a short prompt and long answer can learn a factor up to the 3x clamp and then greatly inflate the next request's context estimate, potentially triggering premature compaction or admission rejection. Record an input-only checkpoint, such as the floor minus the final output-token estimate, instead.
AGENTS.md reference: src/AGENTS.md:L19-L19
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Inline comments:
In `@src/adapters/kiro-calibration.ts`:
- Around line 144-149: Update calibrateKiroEstimate and recordKiroCalibration to
manage rawEstimates and factors as a single synchronized LRU conversation entry,
refreshing recency and evicting both values together at
MAX_TRACKED_CONVERSATIONS. Ensure the maps cannot retain separate or stale
conversation IDs beyond the documented capacity, and add a test covering
eviction at capacity.
- Around line 115-117: Update recordKiroCalibration so a missing previous factor
uses 1 as the baseline and always applies SMOOTHING before clamping the result.
Add a regression test covering a first-turn outlier and verify it cannot cause
the next calibration factor to jump directly to the observed value.
In `@src/adapters/kiro.ts`:
- Around line 1514-1516: Add adapter-level regression tests in the successful
and failed stream cases around recordKiroCalibration: verify a successful stream
records contextUsagePercentage under returnedConversationId and applies it to
the next request, while a failed stream records no calibration. Use the existing
tests in kiro-stream.test.ts and established request/stream helpers without
changing production behavior.
- Around line 1509-1517: Update the calibration flow around
recordKiroCalibration so it uses only a pre-generation token count for the same
payload as contextInputEstimate, not the cumulative value from
contextUsageTotalFloor(). Preserve the cumulative checkpoint for
contextTotalTokens, and skip calibration when no matching pre-request count is
available.
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The calibration divided the upstream context checkpoint by our request-payload estimate, but those measure different things. contextUsageTotalFloor is the absolute context size AFTER the response — OcxUsage.contextTotalTokens, whose contract in types/request.ts says exactly that — while contextInputEstimate covers the prompt alone. Dividing one by the other charges generated tokens to prompt-tokenization error. A conversation with a short prompt and a long answer would read as a massive under-estimate: 1000 in, 1000 correctly estimated, 2000 generated, and the factor learns 3x from a prompt we sized perfectly. Every later request in that conversation is then inflated toward the clamp, which is the premature compaction and admission rejection this work exists to prevent. Subtract the final output estimate first and only learn from a positive remainder. The regression asserts both directions: the corrected path leaves an accurate estimate untouched, and feeding the raw checkpoint in still produces the >2x inflation, so the test fails if the subtraction is ever removed. Found in review of ac927da.
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Inline comments:
In `@src/adapters/kiro.ts`:
- Line 1525: Update the Kiro calibration flow so recordKiroCalibration uses the
original request conversation ID associated with calibrateKiroEstimate, rather
than returnedConversationId when message_metadata replaces it; alternatively
migrate the raw estimate to the replacement key before recording. Add a
regression in kiro-stream.test.ts covering distinct request and returned IDs and
verifying the raw estimate is used.
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…ce per turn Three defects found in review of 812e6fe. The 2.8 chars/token constant was measured from Kiro's charge for Kiro's payload shape, but charsPerToken matches by prefix and claude/deepseek/qwen/glm/minimax are also routed by Cursor, Anthropic direct and Antigravity. Those consumers read this same helper to size admission ceilings, answer count_tokens, and classify overflow-vs-429, so the change silently retuned three unrelated subsystems from evidence that says nothing about them. estimateKiroTokens always prefixes "kiro/", so the Kiro ratio now applies to Kiro traffic and only Kiro traffic; the shared families keep 3.5. Calibration recorded on every clean parse, including attempts that then set needsFallback. The bounded completion retry rebuilds the payload and streams again for the SAME user turn, so one turn moved the factor twice and the second observation scored a payload the first had already inflated. The observation is now staged and committed by the caller only when the attempt is terminal. The two calibration maps could desynchronise: they evicted on different recency and each held its own 256 cap, so a conversation could be refreshed in one and dropped from the other, and recording would then silently fall back to the already-corrected estimate — the residual-error learning the raw baseline exists to prevent. They are one LRU entry now. Also: the first observation is smoothed from 1 rather than adopted outright, so a single cache-affected turn cannot set the factor; and calibration state follows a conversation id that Kiro replaces mid-stream, which otherwise orphaned the raw baseline. Adds the adapter-level test the unit suite was missing: a real stream reports a context percentage and the next request in the same conversation is estimated higher, which fails if the wiring is removed.
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Inline comments:
In `@tests/kiro-calibration.test.ts`:
- Around line 91-92: Increase the filler-entry loop in the test around
recordKiroCalibration so it creates more than MAX_TRACKED_CONVERSATIONS (256)
entries, while retaining the conv-active recency touch and subsequent
assertions. This must exercise LRU eviction and detect regressions in recency
refresh behavior.
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The LRU test created 201 entries against a 256-entry bound, so eviction never ran and the assertion held for the trivial reason that nothing was evicted. It could not have caught a regression in recency refresh. 400 filler entries now exceed the cap, and the test asserts both directions: the repeatedly touched conversation survives, and an untouched one from the same era is gone.
The adapter-level coverage asserted the positive direction only: a completed turn calibrates the next request. Nothing held the negative side, so the rule that an unfinished turn teaches nothing was documentation rather than a guarantee. Two cases now. A stream that ends in an invalid-state terminal must leave the next request estimated exactly as an uncalibrated one. And an attempt that asks for the bounded completion retry must not calibrate either: that retry rebuilds the payload and streams again for the SAME user turn, so learning from the first attempt moves the factor twice and scores the second observation against a payload the first inflated. Both were driven red to prove they are not vacuous. Removing the `!result.needsFallback` condition fails the fallback case; an earlier draft of these tests passed with the guard removed and was rewritten rather than kept.
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In `@tests/kiro-stream.test.ts`:
- Line 1715: Update the test around parseKiroStream and collectAdapterEvents to
count invocations of the mocked globalThis.fetch, then assert exactly one call
after event collection, proving the bounded completion retry executes while
preserving the expected no-calibration result.
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|
||
| resetKiroCalibration(); | ||
| const originalFetch = globalThis.fetch; | ||
| globalThis.fetch = (async () => new Response(streamOf(eventFrame({ content: "Final from fallback." })))) as typeof fetch; |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
Assert that the bounded completion retry executes.
This test passes if parseKiroStream stops issuing the fallback request. In that failure mode, no calibration is recorded, so afterEstimate still equals baseline.
Count calls to the mocked fetch and assert exactly one call after collectAdapterEvents. This makes the test prove the retry path and its no-calibration behavior.
Proposed test change
- globalThis.fetch = (async () => new Response(streamOf(eventFrame({ content: "Final from fallback." })))) as typeof fetch;
+ let fallbackCalls = 0;
+ globalThis.fetch = (async () => {
+ fallbackCalls++;
+ return new Response(streamOf(eventFrame({ content: "Final from fallback." })));
+ }) as typeof fetch;
try {
const falling = createKiroAdapter(provider);
await falling.buildRequest(sameConversation([bashTool]));
await collectAdapterEvents(falling.parseStream(new Response(streamOf(
eventFrame({ content: "I am checking." }),
eventFrame({ contextUsagePercentage: 10 }),
))));
+ expect(fallbackCalls).toBe(1);
} finally {📝 Committable suggestion
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| globalThis.fetch = (async () => new Response(streamOf(eventFrame({ content: "Final from fallback." })))) as typeof fetch; | |
| let fallbackCalls = 0; | |
| globalThis.fetch = (async () => { | |
| fallbackCalls++; | |
| return new Response(streamOf(eventFrame({ content: "Final from fallback." }))); | |
| }) as typeof fetch; | |
| try { | |
| const falling = createKiroAdapter(provider); | |
| await falling.buildRequest(sameConversation([bashTool])); | |
| await collectAdapterEvents(falling.parseStream(new Response(streamOf( | |
| eventFrame({ content: "I am checking." }), | |
| eventFrame({ contextUsagePercentage: 10 }), | |
| )))); | |
| expect(fallbackCalls).toBe(1); | |
| } finally { |
🤖 Prompt for AI Agents
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In `@tests/kiro-stream.test.ts` at line 1715, Update the test around
parseKiroStream and collectAdapterEvents to count invocations of the mocked
globalThis.fetch, then assert exactly one call after event collection, proving
the bounded completion retry executes while preserving the expected
no-calibration result.
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The estimator and calibration fixes on exact head b1b18771a0dd2c947b5eef40b035614d7a89da19 are substantially improved and exact-head CI is green, but the new terminal-only calibration regression still has one proof gap.
In tests/kiro-stream.test.ts around line 1715, the test named an attempt that falls back to the completion retry does not calibrate never asserts that the mocked globalThis.fetch was called. If the bounded completion retry stops executing entirely, the test still passes: no terminal calibration is recorded, so afterEstimate remains equal to baseline. That means the test does not currently distinguish the intended fallback-without-first-attempt-learning behavior from a broken no-fallback path.
Please count the mocked fetch invocations and assert exactly one call after collecting the adapter events, while retaining the existing no-calibration assertion. Then the regression will prove both halves of its stated contract. No production redesign is requested here; this is the focused test assertion already identified on the current head.
…from reported usage (lidge-jun#3476) * fix(kiro): count tokens by script instead of one blended ratio The Kiro context gauge read about 60% of what Kiro actually charges, so auto-compact engaged far too late on long conversations. Measured end to end by building real payloads through buildKiroPayload and scoring them against Kiro's own recorded charge: aggregate estimate/charged was 0.594, and it degraded with conversation length (0.643 at 4 messages -> 0.587 at 700), which is the signature of a per-entry cost that no per-character ratio can recover. Three causes, all fixed here. A single chars-per-token divisor cannot describe mixed text. Latin prose and code run near 2.8 chars/token; Hangul and Han run near 1.5. The old model divided the whole blob by one ratio and clamped to a denser one only when a SAMPLED CJK share crossed 30% — a cliff that real traffic (roughly 1-30% CJK) never triggered, fed by a stride sampler that could read a 1.6%-CJK payload as 100% CJK. Counting the two scripts exactly and adding them removes the threshold, the sampling error and the discontinuity together. The payload walker concatenated message text and ignored the JSON the wire actually carries. Per-entry keys and role framing cost real tokens proportional to entry count, and string escaping expands content by ~1.12x on measured bodies. Both are charged upstream and are now counted. Constants come from two independent recorded sources that agree: 5,799 pure-Latin samples pairing exact text with authoritative token counts aggregate to 2.80 chars/token, and recorded request bodies charge ~2.43 bytes per token at ~1.12 bytes per counted character. CJK solves to ~1.50. Aggregate estimate/charged improves 0.594 -> 0.884, and the length-dependent drift is gone (0.918 -> 0.879 across the same range). Estimates rise, so auto-compact now fires nearer the real boundary; over-counting only compacts early, while under-counting risks context overflow. Tests that pinned the old constants are updated, with new regressions for continuity across the former 30% cliff and for the sampling alias. * feat(kiro): calibrate the context estimate from Kiro's own reported usage The estimator is a fixed heuristic: constants derived from recorded traffic, applied to every conversation identically. It is close on average and necessarily wrong in the particular, because how densely a prompt tokenizes depends on what is in it — a Korean design discussion and a repository of minified JSON do not share a ratio. Kiro already tells us the answer. Mid-stream it reports contextUsagePercentage, which against a known window is an authoritative token count for the exact payload just sent. That number was used once, as a floor under the current turn, and then discarded — so every turn re-derived the conversation's rate from scratch and mispredicted it the same way. This keeps it. After a turn that produced both an estimate and a reported percentage, the realised charged/estimated ratio is folded into a per-conversation correction and applied to that conversation's next turn. Four properties keep it from making the gauge worse. The factor is clamped, so one anomalous or hostile reading cannot distort the estimate without bound. Observations are smoothed, so a single cache-affected turn cannot swing it. State is conversation-scoped with an eviction cap and no persistence. And the existing upstream floor is untouched: calibration only sharpens the estimate before upstream reports, never lowers a value upstream has justified. The correction is learned against the RAW heuristic output rather than the already-corrected estimate. Learning from the corrected value would make the factor measure its own residual error, closing part of the remaining gap each round and stalling short of the truth — simulated, it converged to 0.861 of the real charge instead of 1.0. Against a conversation charged 1.35x, it now reaches 1.000 from the second turn. Kept out of lib/token-estimate deliberately: that module is pure and shared by every provider. This is Kiro-specific state and lives with the Kiro adapter. * docs(admission): re-ground the tolerance rationale on the current estimator The comment above ADMISSION_TOLERANCE justified 2.5 by a sampling artifact that no longer exists: cjkRatio read every stride-th character, so fixed-width records could sample as 100% CJK and inflate an estimate 1.6x. CJK characters are now counted exactly, so that divergence is gone and the margin is not buying it anymore. The constant stays 2.5, for a different and now-stated reason. Segmenting by script raises a pure-Latin estimate 1.25x and a Korean-dominant one up to 1.67x, so measured on the old estimator's scale the same multiplier now behaves like ~2.0x for Latin and ~1.5x for Korean. That is the intended direction — the estimate is closer to what providers actually charge, so the bound is tighter and more honest — and it still refuses the lidge-jun#1412 compounding shape several times over. Leaving the old text in place would have left the next reader sizing this margin against a mechanism that was deleted. * fix(kiro): subtract output before learning the input calibration The calibration divided the upstream context checkpoint by our request-payload estimate, but those measure different things. contextUsageTotalFloor is the absolute context size AFTER the response — OcxUsage.contextTotalTokens, whose contract in types/request.ts says exactly that — while contextInputEstimate covers the prompt alone. Dividing one by the other charges generated tokens to prompt-tokenization error. A conversation with a short prompt and a long answer would read as a massive under-estimate: 1000 in, 1000 correctly estimated, 2000 generated, and the factor learns 3x from a prompt we sized perfectly. Every later request in that conversation is then inflated toward the clamp, which is the premature compaction and admission rejection this work exists to prevent. Subtract the final output estimate first and only learn from a positive remainder. The regression asserts both directions: the corrected path leaves an accurate estimate untouched, and feeding the raw checkpoint in still produces the >2x inflation, so the test fails if the subtraction is ever removed. Found in review of ac927da. * fix(kiro): scope the measured ratio to Kiro and record calibration once per turn Three defects found in review of 812e6fe. The 2.8 chars/token constant was measured from Kiro's charge for Kiro's payload shape, but charsPerToken matches by prefix and claude/deepseek/qwen/glm/minimax are also routed by Cursor, Anthropic direct and Antigravity. Those consumers read this same helper to size admission ceilings, answer count_tokens, and classify overflow-vs-429, so the change silently retuned three unrelated subsystems from evidence that says nothing about them. estimateKiroTokens always prefixes "kiro/", so the Kiro ratio now applies to Kiro traffic and only Kiro traffic; the shared families keep 3.5. Calibration recorded on every clean parse, including attempts that then set needsFallback. The bounded completion retry rebuilds the payload and streams again for the SAME user turn, so one turn moved the factor twice and the second observation scored a payload the first had already inflated. The observation is now staged and committed by the caller only when the attempt is terminal. The two calibration maps could desynchronise: they evicted on different recency and each held its own 256 cap, so a conversation could be refreshed in one and dropped from the other, and recording would then silently fall back to the already-corrected estimate — the residual-error learning the raw baseline exists to prevent. They are one LRU entry now. Also: the first observation is smoothed from 1 rather than adopted outright, so a single cache-affected turn cannot set the factor; and calibration state follows a conversation id that Kiro replaces mid-stream, which otherwise orphaned the raw baseline. Adds the adapter-level test the unit suite was missing: a real stream reports a context percentage and the next request in the same conversation is estimated higher, which fails if the wiring is removed. * test(kiro): push the calibration eviction test past the cap The LRU test created 201 entries against a 256-entry bound, so eviction never ran and the assertion held for the trivial reason that nothing was evicted. It could not have caught a regression in recency refresh. 400 filler entries now exceed the cap, and the test asserts both directions: the repeatedly touched conversation survives, and an untouched one from the same era is gone. * test(kiro): prove the terminal-only calibration rule, both halves The adapter-level coverage asserted the positive direction only: a completed turn calibrates the next request. Nothing held the negative side, so the rule that an unfinished turn teaches nothing was documentation rather than a guarantee. Two cases now. A stream that ends in an invalid-state terminal must leave the next request estimated exactly as an uncalibrated one. And an attempt that asks for the bounded completion retry must not calibrate either: that retry rebuilds the payload and streams again for the SAME user turn, so learning from the first attempt moves the factor twice and scores the second observation against a payload the first inflated. Both were driven red to prove they are not vacuous. Removing the `!result.needsFallback` condition fails the fallback case; an earlier draft of these tests passed with the guard removed and was rewritten rather than kept. --------- Co-authored-by: jun <jun@lidge.dev>
2 participants
Summary
The Kiro context gauge read about 60% of what Kiro actually charges, so auto-compact engaged far too late on long conversations.
Measured end to end by building real payloads through
buildKiroPayloadand scoring them against Kiro's own recorded charge: aggregate estimate/charged was 0.594, and it degraded with conversation length (0.643 at 4 messages to 0.587 at 700). That decay is the signature of a per-entry cost no per-character ratio can recover.Three causes, all fixed here.
One divisor for two scripts. Latin prose and code run near 2.8 chars/token; Hangul and Han near 1.5. The previous model divided the whole blob by one ratio and clamped to a denser one only when a sampled CJK share crossed 30% — a cliff real traffic (roughly 1-30% CJK) never triggered, fed by a stride sampler that could read a 1.6%-CJK payload as 100% CJK. Counting the two scripts exactly and adding them removes the threshold, the sampling error and the discontinuity together.
Framing was free. The payload walker concatenated message text and ignored the JSON the wire carries. Per-entry keys and role framing cost tokens proportional to entry count.
Escaping was free. Newlines and quotes occupy two characters on the wire and one in the walked string; measured bodies run ~1.12x the counted characters.
Constants come from two independent sources that agree: 5,799 pure-Latin samples pairing exact text with authoritative token counts aggregate to 2.80 chars/token, and recorded request bodies charge ~2.43 bytes/token at ~1.12 bytes per counted character, implying ~2.17. CJK solves to ~1.50.
Aggregate estimate/charged improves 0.594 to 0.884, and the length-dependent drift is gone (0.918 to 0.879 across the same range).
The second commit makes it self-correcting. Kiro reports
contextUsagePercentagemid-stream, which against a known window is an authoritative count for the payload just sent; it was used once as a floor and discarded, so every turn re-derived the conversation's rate from scratch. It is now folded into a bounded, smoothed, evicted per-conversation correction applied to that conversation's next turn. The correction is learned against the RAW heuristic output — learning from the corrected value makes the factor measure its own residual, which converged to 0.861 of truth instead of 1.0 while appearing to work.The third commit re-grounds the
ADMISSION_TOLERANCErationale, which justified its margin by the stride sampler this work deletes.Estimates rise, so auto-compact now fires nearer the real boundary. Over-counting only compacts early; under-counting risks context overflow.
Verification
bun test tests/token-estimate.test.ts tests/kiro-stream.test.ts tests/kiro-adapter.test.ts tests/kiro-calibration.test.ts tests/input-admission.test.ts tests/core-lab-boundary.test.ts— 254 pass, 0 failbun x tsc --noEmit— cleanbun run privacy:scan— passedThe repository-wide suite was not run locally; CI covers it. No
gui/file is touched, so no screenshot applies.Checklist
ADMISSION_TOLERANCEcomment is the documentation this touches.)Summary by CodeRabbit
Improvements
Tests