Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add haabe/mycelium/plugin install myceliumWrote 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/haabe/mycelium/corrections-audit)<a href="https://agentmods.dev/skills/haabe/mycelium/corrections-audit"><img src="https://agentmods.dev/badge/skills/haabe/mycelium/corrections-audit.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 | $0.00028 | $0.03699 |
| Opus 5 | $0.00014 | $0.01850 |
| Sonnet 5 | $0.00006 | $0.00740 |
| Haiku 4.5 | $0.00003 | $0.00370 |
Grade A, and why
corrections-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Corrections Audit Skill
Analyze corrections.md for trends, recurring patterns, and actionable insights.
Attribution: run the script, do not count by hand
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/check_correction_attribution.py" --root . --snapshot
Added 2026-08-03. This audit's headline number — who caught the mistake — was computed by hand and had been produced exactly twice, six weeks apart, each time because a human remembered to look. It is the only number that answers the question the correction loop exists to answer: more harness, or more context.
The script reports the escape rate and its denominator, every time. At the
time of writing 14 of 72 entries carry a catcher, so the rate covers 19% of the
corpus — quoting it without that is a claim about the whole wearing borrowed
clothes, which is the failure this repo spent 2026-08-02/03 removing from its own
checks. If it prints NO RATE AVAILABLE, that is not 0%: it means nothing in the
corpus says who caught anything.
--snapshot appends the reading to .claude/evals/metrics/corrections/<date>.json, the same layout the other metric adapters use. The level is not the signal. Trend caught_by_hook_or_check rising in absolute terms; the ratio can also be improved by logging fewer user-caught mistakes, which is why this script never gates on it.
Attribution is a HARD RULE at write time, not a suggestion — see engine/agent-operating-contract.md. It was advisory until 2026-08-03 and 72 of 100 entries carry no catcher as a result. Do not backfill those in bulk: who caught a mistake six weeks ago is not recoverable by inference, and a guessed catcher corrupts the only number this loop produces.
For entries written before the rule, add one phrase per new entry — caught by user,
caught by hook, caught by review, self-caught. The parser reads the prose
forms already present rather than requiring a new field, so nothing existing
needs rewriting.
When to Use
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 · 194 lines · 28 tokens per session scan A b673f97a3381
corrections-audit is a skill published in the GitHub repository haabe/mycelium (45 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 3,699 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 skills, from other repositories
skill-finder
Find the right Affitor skill for your goal. Triggers on: "which skill should I use", "find me a skill", "what skills are available", "help me choose a skill", "skill for SEO", "skill for email", "explore skills", "I'm new to Affitor", "what can Affitor do", "search skills", "skill for blog writing", "skill for landing…
meraki-network-ops
Cisco Meraki organization and network operations (read-only) — org discovery, network inventory, device inventory, clients, group policies, admins, licensing, alert settings via Cisco's official Meraki MCP. Use as the entry point for any Meraki question, to discover org and network IDs, or to inventory Meraki networks…
auto
Intent-classified router, the front door to OrchestKit and the DEFAULT entry point for any goal-shaped request. Classifies a plain-English goal and routes it to the right specialist skill. Routing is never overhead, so use it even when the target skill seems obvious; skip only when already executing inside another…
gingiris-user-interview
🇺🇸 User Interview & Cold-Start Operations Playbook — Battle-tested framework from HeyGen's 937 interviews to PMF. Complete SOP for user screening, interview execution, Beta testing methodology, cold-start user operations, churn analysis, and user grading systems. 🇨🇳 用户访谈与冷启动运营实战手册 — 基于 HeyGen 937 场访谈验证的 PMF…
tao-setup
One-time session setup and orchestration map for the TAO skill bank. Run this first when the TAO skills were installed individually (e.g. from a public skills catalog) so the session gets the cross-skill discovery flow, credential checks, and host preflight that the bundled plugin hook would otherwise inject…
tao-list-capabilities
Answer what the TAO Skill Bank plugin can do by generating the response from packaged application, data, model, AutoML, and platform manifests. Use when the user asks "what can TAO Skill Bank do", "list TAO models", "which TAO workflows are available", or "what supports AutoML".