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 munhq/chat-recall --skill chat-recall-resumegit clone --depth 1 https://github.com/munhq/chat-recallWrote 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/munhq/chat-recall/chat-recall-resume)<a href="https://agentmods.dev/skills/munhq/chat-recall/chat-recall-resume"><img src="https://agentmods.dev/badge/skills/munhq/chat-recall/chat-recall-resume/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/munhq/chat-recall/chat-recall-resume"><img src="https://agentmods.dev/badge/skills/munhq/chat-recall/chat-recall-resume.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 | $0.00085 | $0.00780 |
| Opus 5.5 | $0.00034 | $0.00312 |
| Sonnet 5.5 | $0.00017 | $0.00156 |
| Haiku 4.5 | $0.00009 | $0.00078 |
Grade A, and why
chat-recall-resume 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 29d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 29d ago Changed · +2 lines 14451763190e
- 1mo ago Changed · +2 lines 38d0a67612ab
- 1mo ago Changed ca60e9da4cd6
- 1mo ago Changed 5eb14e368f61
- 1mo ago First seen · 63 lines · 85 tokens per session scan A e20129cc49a9
chat-recall-resume is a skill published in the GitHub repository munhq/chat-recall (2 stars, last pushed 2d ago), with no licence file. It adds 85 tokens to every session and 780 once invoked, about $0.0003 per session on Opus 5.5. 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
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memory-distill
Destilliert rohe Staging-Inbox-Bullets (staging/captured.md) in bleibende, getypte Memory-Dateien. Verwende diesen Skill, wenn der User 'distill', 'Staging aufräumen', 'Inbox abarbeiten', 'Memory verdichten' sagt oder nach einem Ingest-Lauf oder Staging-Nag.
onboard
Use when installing, configuring, updating, or diagnosing On Board for a project, or starting work in a project already using On Board shared memory.
memory-sync
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agent-memory-discipline
Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
memory-system
A guide to giving an AI agent persistent, user-specific memory across conversations. It covers what to remember, how to store it, how to update or compress it, and when to use memory instead of a shared knowledge base.