Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/codenamev/claude_memory/study-reponpx skills add codenamev/claude_memory --skill study-repogit clone --depth 1 https://github.com/codenamev/claude_memoryWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00031 | $0.02596 |
| Opus 5 | $0.00015 | $0.01298 |
| Sonnet 5 | $0.00006 | $0.00519 |
| Haiku 4.5 | $0.00003 | $0.00260 |
Grade A, and why
study-repo scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository Study Skill
Deep analysis of external repositories to identify patterns, architecture decisions, and opportunities for adoption in ClaudeMemory.
Prerequisites
Before running this skill, clone the repository you want to study:
# For public repos
git clone https://github.com/user/project /tmp/study-repos/project-name
# For private repos (requires gh CLI)
gh repo clone user/project /tmp/study-repos/project-name
# Use --depth 1 for speed (recommended)
git clone --depth 1 https://github.com/user/project /tmp/study-repos/project-name
Then invoke: /study-repo /tmp/study-repos/project-name
Optional Focus Mode: Narrow analysis to specific aspect
/study-repo /tmp/study-repos/project-name --focus="MCP implementation"
/study-repo /tmp/study-repos/project-name --focus="testing strategy"
See .claude/skills/study-repo/focus-examples.md for more examples.
CRITICAL: Memory Discipline (no external-tech misattribution)
When studying an external repo you will read its README, gemspec, and source — and you will see things like "uses Postgres", "runs on AWS", "built with Rails". These are facts about the external project, not about this project.
Do NOT call memory.store_extraction with the external project's tech stack as uses_database / uses_framework / uses_language / deployment_platform / auth_method predicates. That misattribution caused 27 facts to be stored about ClaudeMemory in the 2026-04-23/24 window that all had to be hand-rejected (see improvements.md #61, quality_review.md 2026-04-30 note). The corpus damage was real even though the cleanup worked — every misattributed fact takes a round trip through the database, conflict-detection, and the user's claude-memory reject queue.
The rule. While /study-repo is running, the only memory.store_extraction calls allowed are:
predicate=referencefor descriptions of the external project ("X is a plugin/library/CLI that…"). The dashboard's Knowledge → References panel is the right home for these.- Facts genuinely about this project ClaudeMemory that you derive from contrast with the studied repo (e.g., a decision: "Adopt RRF fusion from QMD because…"). These belong as
decision/convention/architecturewithsubject=repoorsubject=claude_memoryAND a reason clause embedded.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 323 lines · 0 tokens per session scan A e125fc57c6ab
study-repo is a skill published in the GitHub repository codenamev/claude_memory (24 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 2,596 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
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auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…