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 weekly-meta-auditgit 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/weekly-meta-audit)<a href="https://agentmods.dev/skills/suryast/free-ai-agent-skills/weekly-meta-audit"><img src="https://agentmods.dev/badge/skills/suryast/free-ai-agent-skills/weekly-meta-audit/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/weekly-meta-audit"><img src="https://agentmods.dev/badge/skills/suryast/free-ai-agent-skills/weekly-meta-audit.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 | $0.00000 | $0.01648 |
| Opus 5 | $0.00000 | $0.00824 |
| Sonnet 5 | $0.00000 | $0.00330 |
| Haiku 4.5 | $0.00000 | $0.00165 |
Grade A, and why
weekly-meta-audit 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 12d 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STARTER_CHARACTER = 🔍
Weekly Meta-Audit
A structured weekly self-audit that examines your agent's operational health, identifies gaps, and generates concrete improvement actions. Inspired by Outcome Engineering principles — measure outcomes, not outputs.
Purpose
Most AI agent setups accumulate invisible debt: silent cron failures, wrong assumptions in memory, manual work that should be automated, cross-project synergies nobody pursues. This skill surfaces all of it systematically.
When to Run
- Scheduled: Sunday evening / Monday morning (end-of-week retrospective)
- On demand: After a week with notable failures or when things feel "off"
- Recommended cadence: Weekly (diminishing returns if more frequent)
Prerequisites
The audit reads from your existing workspace structure. It works best with:
- Memory files: Daily logs (
memory/YYYY-MM-DD.md) from the past 7 days - Feedback file: Shared operational rules (
memory/feedback/feedback.mdor similar) - Long-term memory:
MEMORY.mdwith project state and decisions - Cron system: OpenClaw crons (or any cron manager with
listandrunscommands)
If any of these don't exist, the audit adapts — it just has less data to work with.
Audit Process
Phase 1: Gather Context
Read these files (skip any that don't exist):
- Operating principles —
AGENTS.mdor equivalent - Current state —
MEMORY.md - Shared rules —
memory/feedback/feedback.md - Last 7 daily logs —
ls memory/2*.md | tail -7, read each - Cron list — Run
openclaw cron list(or equivalent) - Recent cron runs — Run
openclaw cron runs --limit 50
Phase 2: Analyze (All 11 Sections Mandatory)
Work through each section. Be specific, not generic — name cron IDs, script paths, project names, dates, and incidents. Every finding must have a concrete next action.
1. MISSING TOOLS/AUTOMATIONS
What broke or required manual intervention this week that should be automated?
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.
- 12d ago First seen · 196 lines · 0 tokens per session scan A 81afa102c32c
weekly-meta-audit is a skill published in the GitHub repository suryast/free-ai-agent-skills (2 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,648 tokens. 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.
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