memory-review
Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics. Use when the user runs /si:memory-review or asks what has been learned and what should be promoted or pruned.
- License 许可
- MIT
- 最近更新
- 2026-08-30
- Stars
- ★ 1
- Forks
- 0
01 前言 Front Matter
- name
- memory-review
- description
- Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics. Use when the user runs /si:memory-review or asks what has been learned and what should be promoted or pruned.
03 SKILL.md 正文
/si:memory-review — Analyze Auto-Memory
Performs a comprehensive audit of Claude Code's auto-memory and produces actionable recommendations.
Usage
/si:memory-review # Full review
/si:memory-review --quick # Summary only (counts + top 3 candidates)
/si:memory-review --stale # Focus on stale/outdated entries
/si:memory-review --candidates # Show only promotion candidates
What It Does
Step 1: Locate memory directory
# Find the project's auto-memory directory
MEMORY_DIR="$HOME/.claude/projects/$(pwd | sed 's|/|%2F|g; s|%2F|/|; s|^/||')/memory"
# Fallback: check common path patterns
# ~/.claude/projects/<user>/<project>/memory/
# ~/.claude/projects/<absolute-path>/memory/
# List all memory files
ls -la "$MEMORY_DIR"/
If memory directory doesn't exist, report that auto-memory may be disabled. Suggest checking with /memory.
Step 2: Read and analyze MEMORY.md
Read the full MEMORY.md file. Count lines and check against the 200-line startup limit.
Analyze each entry for:
-
Recurrence indicators
- Same concept appears multiple times (different wording)
- References to "again" or "still" or "keeps happening"
- Similar entries across topic files
-
Staleness indicators
- References files that no longer exist (
findto verify) - Mentions outdated tools, versions, or commands
- Contradicts current CLAUDE.md rules
- References files that no longer exist (
-
Consolidation opportunities
- Multiple entries about the same topic (e.g., three lines about testing)
- Entries that could merge into one concise rule
-
Promotion candidates — entries that meet ALL criteria:
- Appeared in 2+ sessions (check wording patterns)
- Not project-specific trivia (broadly useful)
- Actionable (can be written as a concrete rule)
- Not already in CLAUDE.md or
.claude/rules/
Step 3: Read topic files
If MEMORY.md references or the directory contains additional files (debugging.md, patterns.md, etc.):
- Read each one
- Cross-reference with MEMORY.md for duplicates
- Check for entries that belong in the main file (high value) vs. topic files (details)
Step 4: Cross-reference with CLAUDE.md
Read the project's CLAUDE.md (if it exists) and compare:
- Are there MEMORY.md entries that duplicate CLAUDE.md rules? (→ remove from memory)
- Are there MEMORY.md entries that contradict CLAUDE.md? (→ flag conflict)
- Are there MEMORY.md patterns not yet in CLAUDE.md that should be? (→ promotion candidate)
Also check .claude/rules/ directory for existing scoped rules.
Step 5: Generate report
Output format:
📊 Auto-Memory Review
Memory Health:
MEMORY.md: {{lines}}/200 lines ({{percent}}%)
Topic files: {{count}} ({{names}})
CLAUDE.md: {{lines}} lines
Rules: {{count}} files in .claude/rules/
🎯 Promotion Candidates ({{count}}):
1. "{{pattern}}" — seen {{n}}x, applies broadly
→ Suggest: {{target}} (CLAUDE.md / .claude/rules/{{name}}.md)
2. ...
🗑️ Stale Entries ({{count}}):
1. Line {{n}}: "{{entry}}" — {{reason}}
2. ...
🔄 Consolidation ({{count}} groups):
1. Lines {{a}}, {{b}}, {{c}} all about {{topic}} → merge into 1 entry
2. ...
⚠️ Conflicts ({{count}}):
1. MEMORY.md line {{n}} contradicts CLAUDE.md: {{detail}}
💡 Recommendations:
- {{actionable suggestion}}
- {{actionable suggestion}}
When to Use
- After completing a major feature or debugging session
- When
/si:memory-statusshows MEMORY.md is over 150 lines - Weekly during active development
- Before starting a new project phase
- After onboarding a new team member (review what Claude learned)
Tips
- Run
/si:memory-review --quickfrequently (low overhead) - Full review is most valuable when MEMORY.md is getting crowded
- Act on promotion candidates promptly — they're proven patterns
- Don't hesitate to delete stale entries — auto-memory will re-learn if needed
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.
research
Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends/sentiment, grants for NIH funding, litreview for academic literature, syllabus for course reading, patent for prior-art + IP landscape, dossier for entity research, deepread for evidence-first reading of supplied documents) or runs its own plan-decompose-multi-source-search-synthesize-cite fallback workflow when no specialist matches. Always surfaces the routing decision so users can override. Use when the user makes any research request that doesn't obviously match a more-specific specialist skill (e.g., "research [topic]", "look into [topic]", "what do we know about [topic]", "investigate [topic]", "find me information on [topic]", "do some research on [topic]", "I need to understand [topic]"). Output is a markdown briefing (default) or .docx document (on request) with full citations and an audit log.
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.