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/jonathanmr22/pact/pact-recallnpx skills add jonathanmr22/pact --skill pact-recallgit clone --depth 1 https://github.com/jonathanmr22/pactWrote 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/jonathanmr22/pact/pact-recall)<a href="https://agentmods.dev/skills/jonathanmr22/pact/pact-recall"><img src="https://agentmods.dev/badge/skills/jonathanmr22/pact/pact-recall.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 5.1 | $0.00016 | $0.00289 |
| Opus 5 | $0.00008 | $0.00144 |
| Sonnet 5 | $0.00003 | $0.00058 |
| Haiku 4.5 | $0.00002 | $0.00029 |
Grade A, and why
pact-recall 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 6d 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
Search the PACT vector memory for knowledge relevant to the user's query. This uses semantic similarity to find matching bugs, graduated solutions, research synthesis, and task feedback across all indexed projects.
How to use: The user provides a description of what they're looking for — a symptom, a question, a topic. You run the query and present the results.
Run this command to search:
python ~/.claude/hooks/pact-memory.py query "{user's query}" --top 5 --json
If pact-memory.py isn't at that path, check:
.claude/hooks/pact-memory.py(project-local)- The PACT templates/memory directory
Present the results to the user as:
## PACT Recall: "{query}"
Found {N} relevant results:
1. **[{score}%] [{type}] {id}**
{text preview}
→ {file path}
2. ...
If the score is above 40%, the result is likely relevant. Below 30% is weak — mention it's a loose match.
If no results are found, suggest the user run python pact-migrate.py /path/to/project to index their knowledge files, or note that the vector index may not have been built yet.
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.
- 6d ago First seen · 37 lines · 16 tokens per session scan A d31d87450e38
pact-recall is a skill published in the GitHub repository jonathanmr22/pact (15 stars, last pushed 2mo ago), licensed MIT. It adds 16 tokens to every session and 289 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.
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