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 commands/vshulcz/deja-vu/dejagit clone --depth 1 https://github.com/vshulcz/deja-vuWrote 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/commands/vshulcz/deja-vu/deja)<a href="https://agentmods.dev/commands/vshulcz/deja-vu/deja"><img src="https://agentmods.dev/badge/commands/vshulcz/deja-vu/deja.svg" alt="Measured on agentmods" 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 | $0.00017 | $0.00174 |
| Opus 5 | $0.00009 | $0.00087 |
| Sonnet 5 | $0.00003 | $0.00035 |
| Haiku 4.5 | $0.00002 | $0.00017 |
Grade A, and why
deja scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- recall — 97% identical, 2 lines differ
What it actually says
Search the user's own past sessions across every AI coding tool on this machine, then answer from what you find.
Run the recall tool with the user's words as the query — the most specific tokens win (an exact error string, a function name, a file path, a flag). If a result looks right but is too short to act on, follow up with recall_context using a term from it.
If the deja MCP tools are unavailable, fall back to the CLI:
deja search -- "$ARGUMENTS"
Answer with what actually happened in those sessions — when it was, which project and tool, what was decided or fixed. Say plainly if nothing matched rather than filling the gap from general knowledge.
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.
- yesterday Changed 35d647ce2585
- 5d ago First seen · 23 lines · 17 tokens per session scan A 8ba5452f405d
deja is a command published in the GitHub repository vshulcz/deja-vu (776 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 174 once invoked, about $0.0001 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 commands, from other repositories
session
Complete reference for all session-related commands — start, end, list, and compact.
save
Save a Wingman handoff for the next tool or machine.
tree-ring-update
Check for or install a verified Tree Ring Memory CLI update without changing installation scope.
fd-resume
Restore from checkpoint.json (falling back to STATE.md) — brief the user, PAUSE for confirmation, then continue from the recorded command and stage.
cierre
A sales call just ended: turn its transcript into the full follow-up (CRM, tasks, email draft, reminder, coaching).
insights
Weekly or monthly journal insights with pattern recognition and panel feedback.