wrong pr: scientific agent - #22
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CTO 第 8 次失误修复 — 5 天 CTO 工作全部未 git commit,由 pmagent 团队应用 ADR v5 langchain-ai#22 audit CTO sync claim 时 catch("我昨天已 sync" vs git status untracked)。 核心架构决策档案 - decisions/0002-fork-customization-strategy.md (ADR-0002 v3) — Plan H+ 三方签字 ACCEPTED - 含 ADR 评审 checklist v1+v2+v3+v4+v5(langchain-ai#1-langchain-ai#22 完整体) - v5 langchain-ai#22 fact-check 必须含对照实验(CTO 第 6 次失误 + pmagent 5 次同型失误沉淀) - v5 langchain-ai#20 角色权限边界 + langchain-ai#21 governance 边界 - 含 CTO 6 次失误谱系 + 元层观察(langchain-ai#22 不强制纳入) 主设计文档 - 2026-05-02-plan-h-plus-final.md — Plan H+ v3 完整设计(含 src/agent_assembly/ namespace v3 修订) 历史方案档案(HISTORICAL/SUPERSEDED) - 2026-04-29 ~ 2026-05-02 v1 → v4 → Plan H+ 演进 8 次方向探索 - spike/ 步骤 1+2 reference template 应用 ADR v5 langchain-ai#16 fact-check + langchain-ai#22 实证: - git status untracked 实证已确认(pmagent v1.6 audit catch) - 此 commit 修复 5 天工作未 commit 严重失误 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…6 验收 + Phase 1.7+P2 CTO recantation CTO 第 8 次失误修复批次 3/3 — D-1/D-2 设计 + 验收报告 + CTO 自查档案。 D-1 Fork 归档 SOP - 2026-05-03-d1-fork-archive-sop.md - 4 阶段执行(仓库元数据 + CI 停用 + GitHub Archive + pmagent 侧扫描) - 含回滚预案矩阵 + 6 月评估 SOP D-2 SubAgent Telemetry Hook 设计 v2 - 2026-05-03-d2-subagent-telemetry-hook-design.md - v1 → v2 重大修订(CTO 第 1 次失误修复) - 0 deepagents 修改 + 0 monkey-patch - 完全用 langchain wrap_tool_call 标准 hook + pmagent additive subclass 阶段验收报告 - 2026-05-03-phase-1-5-acceptance-report.md(Phase 1.5 业务适配 e2e PASS) - 2026-05-03-phase-1-6-acceptance-report.md(含 CTO 接受三方专家校正 3 处) CTO 自查档案 - 2026-05-03-phase-1-7-p2-cto-recantation.md - CTO 第 2 次失误(Phase 1.7+P2 PASS 错判)→ 撤回 PASS 改判 REJECT/CONDITIONAL - ADR checklist v4 langchain-ai#16-langchain-ai#19 增补建议(已落地 ADR-0002 v3) CTO 第 8 次失误说明(本 commit 修复的失误): - "我昨天已 sync deepagents 主档案" claim vs git status untracked - 整个 docs/architecture/decisions/ 5 天未 commit - pmagent v1.6 audit 应用 ADR v5 langchain-ai#22 准则发现 - 失误模式:内部一致性 cross-validate("我以为 commit 了") vs fidelity 量化(git log 实证) - 应用 ADR v5 langchain-ai#22:fact-check 必须实测,不能依赖记忆 / 假设 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
… exFAT resource forks 排除 Item A: ADR v5 langchain-ai#23 sync 主档案 - pmagent 接受副本 commit b246404 (2026-05-04) 已落地 langchain-ai#23 - 主档案 ADR-0002 v3 §0 v5 增补区同步增补 langchain-ai#23 完整 entry - langchain-ai#23: 设计文档(spec / SOP / 装配支援包 / 决策记录)必须含 verification 章节 - 4 项要素: (1) 客观判定标准 (2) 验证方法(含对照实验设计)(3) 失败可观察信号 (4) 谁负责跑 verification - 与 langchain-ai#22 区别: langchain-ai#22 防实验设计 design flaw(实验维度),langchain-ai#23 防文档设计 design flaw(spec 维度) - 沉淀来源: CTO 自 audit 3 findings (D2-A1/fork-diff-A1/H3-A1) 共同模式 + 三方专家 v1.6 review APPROVE Item B: .gitignore 加 macOS exFAT resource forks 排除 - 新增 **/._* 防御 docs/architecture/ 等目录出现 ?? docs/.../._*.md untracked noise - 与 CLAUDE.md memory "外部 volume /Volumes/0-/ creates ._* resource fork files" 一致 - 也是 CTO 第 8 次失误的 process 改进(之前 git status 含大量 ._* 噪音影响审视清晰度) 应用 ADR v5 langchain-ai#16/langchain-ai#17/langchain-ai#22/langchain-ai#23(避免重复 CTO 第 8 次失误): - langchain-ai#16: 跑 git status / git diff / git log 实证(不依赖记忆) - langchain-ai#17: fidelity 量化(git ls-files 实证 vs 内部"我已 sync" claim) - langchain-ai#22: 实证而非记忆 - langchain-ai#23: 本 commit message 本身含 verification 路径(实证命令 + 对照标准) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
… v5 langchain-ai#22 + langchain-ai#23) CTO 自 audit 3 findings (D2-A1/fork-diff-A1/H3-A1) 修订完整闭环。本 commit 是 §6.1 A 组 3 项 CTO domain spec 修订(A1+, A4, A5)。 A1+ H-3 §3.2.5 反向测试要求(应用 langchain-ai#22) - Phase 1 装配支援包 H-3 §3.2 增"反向测试要求"section - 每项 invariant 必须配反向验证(破坏 invariant 看是否 FAIL) - 含 T-3 反向验证模板供 pmagent 复制到其他 12 项 - 触发: pmagent v1.6 audit MEDIUM langchain-ai#2 (仅 T-3 反向,T-1~T-13 单向 assertion 风险) A4 D-2 v2 §6 R-3 对照测试覆盖(应用 langchain-ai#22) - 单元测试 spec 增 3 项对照测试要求: ① 嵌套 SubAgent (subagent 调用 subagent) hook 触发对照 ② async dispatch vs sync dispatch 双路径对照 ③ 多 hook 订阅者 firing 顺序对照 - 触发: CTO 自 audit D2-A1 finding (设计未要求实施时做对照测试) A5 fork-vs-pypi-diff-evaluation.md spec(应用 langchain-ai#23) - 创建独立 spec 文档(之前仅 message 形式,不在 git tracking) - §3.3 测试驱动评估方法(替代主观"是否仍依赖"判断) - §3.2 失败 → V2 子类化决策树 - §5 完整 verification 章节(应用 ADR v5 langchain-ai#23 SOP) - 估时 2-3 h(绿灯)/ +1-3 d V2 子类化(黄灯)/ 翻盘 ADR(红灯) - 触发: CTO 自 audit fork-diff-A1 finding (缺可执行评估方法) 应用 ADR v5 全套 (langchain-ai#16-langchain-ai#23): - langchain-ai#16: 所有 finding 配具体行号引用 + commit hash - langchain-ai#17: fidelity 量化 (fork diff 数据来自 1c3a85a 实证 +3,798 行) - langchain-ai#22: 三个 spec 修订全部含对照实验要求 - langchain-ai#23: A5 创建独立 spec 文档含 verification 章节(CTO 第 8 次失误教训:spec 必须 git tracked,不依赖记忆 sync) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…sistence Implements a pure-Python Telegram Bot API channel adapter for the talon host, following the same patterns as the existing WhatsApp channel. **Telegram channel adapter** (): - with env-var mapping ( prefix) and alias - using — no bridge subprocess needed - Long-polling via with configurable timeout and interval - MarkdownV2 message formatting with automatic plain-text fallback - for images, for other media types - typing indicator - for inbound media downloads - Operator auto-capture from first inbound message - Configurable exposure modes (default, allowlist, open) **Offset persistence** (ticket 23): - in the session directory - Atomic writes via - Loads persisted offset on startup, saves after each polling cycle - Graceful handling of missing/corrupt/invalid offset files **CLI wiring** (): - flag to enable the Telegram channel - Updated factory to accept both and - Both channels can coexist simultaneously **Config** (): - Added to - Added to for alias capture **Tests**: - 36 unit tests covering config, MarkdownV2 escaping, polling, exposure, outbound text/media, edit, typing, inbound media, status, offset persistence - 7 integration tests for factory and simultaneous channel coexistence Closes #21, #22, #23
Adding a scientific deep agent to use for search through SemanticScholar, Arxiv and PubMed