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 skills add cmg8431/claude-dejavu --skill dejavugit clone --depth 1 https://github.com/cmg8431/claude-dejavuWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cmg8431/claude-dejavu/dejavu)<a href="https://agentmods.dev/skills/cmg8431/claude-dejavu/dejavu"><img src="https://agentmods.dev/badge/skills/cmg8431/claude-dejavu/dejavu/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cmg8431/claude-dejavu/dejavu"><img src="https://agentmods.dev/badge/skills/cmg8431/claude-dejavu/dejavu.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00051 | $0.00441 |
| Opus 5 | $0.00026 | $0.00220 |
| Sonnet 5 | $0.00010 | $0.00088 |
| Haiku 4.5 | $0.00005 | $0.00044 |
Grade A, and why
dejavu scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
claude-dejavu: Review Learned Rules
You are helping the user review antipattern rules that claude-dejavu has detected from past sessions.
Steps
- First, run the dejavu binary to get current status:
claude-dejavu stats --path "$(pwd)"
- Then list all rules:
claude-dejavu list --path "$(pwd)"
- Present the rules to the user in a clear format:
For each rule, show:
- Rule ID and status (active/proposed/dead)
- Rule text — what Claude should do differently
- Detector type — how it was discovered (revert cycle / repeated error / silent fix)
- Confidence and fire count
- Action options: keep, edit, or remove
- If there are proposed rules, ask the user to approve them:
┌─ Proposed rule r-001 ─ [repeated error] ─────────┐
│ "This project uses pnpm, not npm." │
│ Evidence: 4 sessions, last 2 weeks │
│ Confidence: 0.91 │
│ Apply? [y/n/edit] │
└───────────────────────────────────────────────────┘
- If the user approves, apply rules to CLAUDE.md:
claude-dejavu scan --path "$(pwd)"
- After applying, show a summary of what changed in CLAUDE.md.
Important
- Never modify rules without user confirmation
- Dead rules (no fires in 14+ days) should be suggested for removal
- Rules with high fire counts are valuable — highlight them
- If no rules exist yet, explain that dejavu needs more sessions to detect patterns
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.
- 11d ago First seen · 58 lines · 51 tokens per session scan A 2db341985df1
dejavu is a skill published in the GitHub repository cmg8431/claude-dejavu (2 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 441 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
ccs-align
Run the CCS Align seat's hourly breathing cycle — prove the local claude-mem worker is healthy, pull needle observations through search → timeline → getobservations, land them in a seat-owned middle cache via atomic grab → append → filter exclude-marks → replace, manage exclude marks, and walk house → project → seat…
weekly-digests
Generate a serial week-by-week narrative digest of a project's full claude-mem timeline. Splits the timeline into per-ISO-week files, then runs one consecutive subagent per week — each receiving the prior week's carry-forward block — to produce one chapter per ISO week of data. Use when asked for "weekly digests"…
mode-creator
Interactively create, install, activate, and verify custom claude-mem modes, including domain-specific observation types, concept tags, optional Telegram alerts, bot setup, worker restart, and startup-context verification. Use this whenever someone asks to customize what claude-mem remembers, create or change a mode…
cloud-sync
Set up or check claude-mem cloud sync with cmem.ai Pro. Use when the user says "set up cloud sync", "sync my memories", "cmem pro", "cloud backup", "sync status", or wants their memory database backed up or synced to their cmem.ai account.
knowledge-agent
Build and query AI-powered knowledge bases from claude-mem observations. Use when users want to create focused "brains" from their observation history, ask questions about past work patterns, or compile expertise on specific topics.
claude-mem-install
Use this when setting up claude-mem on Cursor: local or remote worker, local host-login observer or remote cmem.ai inference.