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 suryast/free-ai-agent-skills --skill daily-archivistgit clone --depth 1 https://github.com/suryast/free-ai-agent-skillsWrote 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/suryast/free-ai-agent-skills/daily-archivist)<a href="https://agentmods.dev/skills/suryast/free-ai-agent-skills/daily-archivist"><img src="https://agentmods.dev/badge/skills/suryast/free-ai-agent-skills/daily-archivist/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/suryast/free-ai-agent-skills/daily-archivist"><img src="https://agentmods.dev/badge/skills/suryast/free-ai-agent-skills/daily-archivist.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 | $0.00066 | $0.01643 |
| Opus 5.5 | $0.00026 | $0.00657 |
| Sonnet 5.5 | $0.00013 | $0.00329 |
| Haiku 4.5 | $0.00007 | $0.00164 |
Grade A, and why
daily-archivist 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 18d 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Uses the Agent Skills package format. Execution depends on the host tools and permissions; consult the frontmatter compatibility requirements where provided.
Daily Archivist
Audit knowledge quality. Fix what's safe. Flag what's not. Route findings to the right agent.
Inspired by the "daily-archivist" pattern: a scheduled agent that vets all knowledge files, fact-checks claims, fills gaps, cleans up quality — and leaves notes for other agents when it finds issues they should handle.
What It Does
- Fact-check — Verify claims in memory files against actual state (file counts, project status, config values)
- Gap-fill — Find undocumented decisions, orphan references, missing rationale
- Clean up — Fix formatting, remove duplicates, merge overlapping entries, archive stale content
- Route findings — Leave notes in
memory/inbox/<agent>.mdfor the appropriate agent - Bug hunt — Spot broken references, missing files, config drift, stale data
Setup
1. Create the inbox directory
mkdir -p memory/inbox
# Create inbox files for each agent in your team
for agent in main coder reviewer researcher; do
cat > memory/inbox/${agent}.md << 'EOF'
# Agent Inbox
<!-- Archivist leaves notes here. Read at session start, delete after acting. -->
EOF
done
2. Add inbox reading to your agent startup
Add this to your AGENTS.md or equivalent configuration:
## Every Session
1. Read memory files and feedback
2. **Check inbox:** Read `memory/inbox/<your-agent>.md` — act on pending items, delete after handling
3. Configure the verification script
Copy scripts/verify-facts.sh and customize the checks for your project. See comments in the script for examples.
4. Schedule the cron
# Example for OpenClaw
openclaw cron add \
--name daily-archivist \
--cron "0 3 * * *" \
--agent janitor \
--model claude-haiku-4-5 \
--timeout-seconds 300 \
--announce \
--message 'Daily knowledge audit. Run verify-facts.sh, scan changes, fix what is safe, route findings to agent inboxes.'
What ships with it
3 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.
- 18d ago Changed · -3 lines 03dd2848a90e
- 1mo ago First seen · 188 lines · 66 tokens per session scan A 708fd2c13052
daily-archivist is a skill published in the GitHub repository suryast/free-ai-agent-skills (2 stars, last pushed 5d ago), licensed MIT. It adds 66 tokens to every session and 1,643 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
context-canvas-checkpoint
Maintain an optional session task map and explicit retrievable references in Context Canvas Codex. Use when navigation or long-context offload would materially help with goals, decisions, progress, dependencies, blockers, next steps, exploration summaries, large textual tool results, or selective historical…
purmemo
Persistent AI conversation memory. Recall context at session start, save progress at session end. Essential for project continuity across sessions.
bootstrap
This skill guides you through setting up your Agent Recall persona. It runs automatically on first use or when invoked with /bootstrap.
mem-search
Search Agent Recall's persistent cross-session memory database. Use when user asks "did we already solve this?", "how did we do X last time?", or needs work from previous sessions.
doctor
A health audit for the Agent Recall system, which stores information for an AI agent. It runs when you ask to check or diagnose that system.
recall
Search your past work across sessions using temporal or topic-based recall.