agent-memory
Use when a project's CLAUDE.md has grown past what anyone reads and you want the agent to learn durable facts from its own sessions instead — or when asking why the agent keeps re-learning the same correction, why a remembered rule is wrong, or where a memory line came from. Implements a four-tier store (L0 transcripts / L1 candidates / L2 project context / L3 stable persona) where promotion is earned by recurrence across sessions and days, never by one confident statement, and nothing reaches a committed file without a human adopting it.
- License 许可
- MIT
- 最近更新
- 2026-10-28
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01 前言 Front Matter
- name
- agent-memory
- license
- MIT
- metadata
- { "version": "1.0.0", "build_pattern": "Tencent TencentDB-Agent-Memory's tiering concept rebuilt natively on Claude Code hooks; deterministic recurrence gates, no LLM, no database", "distinct_from": "llm-wiki (a vault you write on purpose; this writes itself from sessions); skillopt-sleep (replays tasks to improve a skill; this extracts facts to remember); memory-engineering (audits and prices any memory system; this IS one, and is a legitimate subject of that audit)" }
- description
- Use when a project's CLAUDE.md has grown past what anyone reads and you want the agent to learn durable facts from its own sessions instead — or when asking why the agent keeps re-learning the same correction, why a remembered rule is wrong, or where a memory line came from. Implements a four-tier store (L0 transcripts / L1 candidates / L2 project context / L3 stable persona) where promotion is earned by recurrence across sessions and days, never by one confident statement, and nothing reaches a committed file without a human adopting it.
- argument-hint
- [optional: status | why "<claim>" | a tier name]
02 资源文件 16
- 资产文件assets/memory_schema.json15.2 KB
- 参考文档CHANGELOG.md710 B
- 参考文档docs/README.md2.9 KB
- 参考文档evals/evals.json6.8 KB
- 参考文档references/backfill.md1.9 KB
- 参考文档references/edge-cases.md2.1 KB
- 参考文档references/entry-format.md3.4 KB
- 参考文档references/hook_capture_discipline.md6.8 KB
- 参考文档references/promotion_gate_design.md7.4 KB
- 参考文档references/tiered_memory_canon.md6.6 KB
- 脚本scripts/memory_core.py14.6 KB
- 脚本scripts/memory_extract.py8.1 KB
- 脚本scripts/memory_inspect.py10.7 KB
- 脚本scripts/memory_promote.py10.0 KB
- 脚本scripts/skill_versions.py14.7 KB
- 脚本scripts/validate_examples.py22.2 KB
03 SKILL.md 正文
Agent Memory — promotion is earned, not asserted
Portability: stdlib only. No database, no embeddings, no network, no LLM calls.
The problem
A project's CLAUDE.md is a memory system with one tier and no eviction: every
durable fact and every passing preference land in the same always-loaded file,
until the important lines are diluted by the incidental ones. Facts learned
mid-session vanish at teardown unless someone writes them down.
The fix is not more storage — it is a promotion ladder. A claim earns its way toward always-loaded context by recurring; a human confirms the last step.
The four tiers
Tiers are distinguished by injection policy, not storage format.
| Tier | Holds | Injected | Committed |
|---|---|---|---|
| L0 | raw session transcripts | never | no (already on disk) |
| L1 | candidate atoms | on relevance, at prompt time | no (gitignored) |
| L2 | this project's context | every session start | yes, after adopt |
| L3 | stable cross-project persona | always | yes, after adopt |
The gates
Nothing moves up because it sounded important. It moves up because it recurred.
- L0 → L1 — an explicit marker fires (a directive, a correction, a stated preference, a named lesson, a reproducible failure). Rule-based, high precision, deliberately low recall.
- L1 → L2 — ≥ 3 distinct sessions spanning ≥ 2 distinct calendar days. A claim stated outright needs 2 sessions; the distinct-day rule still applies. A verified claim promotes on one observation and is the only day-exempt path.
- L2 → L3 — held in ≥ 2 distinct projects, aged ≥ 30 days, uncontested.
Two gates refuse rather than guess. A claim whose text was altered by redaction never promotes on evidence alone — the flag firing is evidence the source was sensitive, and a lexical filter finding one secret is not proof it found all of them. A claim with an open contradiction is frozen at L1 until a human resolves it; the incumbent is never silently overwritten.
Use it
# what is remembered, and what is blocking the next promotion
python3 scripts/memory_inspect.py --tier L1
# where did this line come from — sessions, days, transcript, quoted source
python3 scripts/memory_inspect.py --why "PR base branch is dev"
# every claim with an open contradiction, both directions of the join
python3 scripts/memory_inspect.py --contested
# dry-run the promotion pass; writes nothing
python3 scripts/memory_promote.py
Three hooks run the loop unattended: SessionStart injects L2 + L3,
UserPromptSubmit recalls relevant L1 atoms, SessionEnd captures and stages.
Each is disabled independently with AGENT_MEMORY_SESSIONSTART=0,
AGENT_MEMORY_USERPROMPTSUBMIT=0, AGENT_MEMORY_SESSIONEND=0. Every hook fails
open: a broken memory system costs you memory, never a session.
Hard rules
- Redact before writing. Every atom passes the filter before it reaches disk. Anything altered is quarantined from promotion.
- Propose, never apply. Promotions land in
.memory/staged/. Only an explicit/cs:memory adopttouches aCLAUDE.md, and it backs both up first. - Cite, don't invent. Every atom carries a back-pointer to the transcript
line that produced it.
--whyresolving to ambiguous prints nothing rather than guess: a wrong citation is worse than a missing one. - Never surface a contested claim as fact. It is still injected — hiding the conflict is worse — but always tagged.
- The committed tiers carry no paths. Promotion strips the back-pointer prefix, which embeds an OS username.
Forcing questions
Walk these one at a time before trusting the store.
- Which line in your
CLAUDE.mddid you last actually read before acting? - Would you rather the agent forget a true thing, or remember a false one?
- When two remembered rules disagree, who decides — and when?
- What would make you delete
.memory/entirely?
Rationale, open decisions, field schema: ../../DESIGN.md.
04 同仓库更多 Skills
syllabus
Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.
pulse
Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.
patent
Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-to-operate / competitive landscape / acquisition diligence / litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.
notebooklm
Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio/Video Overviews, Mind Maps, Reports incl. Briefing Doc/Study Guide/FAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.
litreview
Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5/10/20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed/OpenAlex (or Consensus) query.
grants
NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.