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 ai-analyst-lab/ai-analyst-plugin --skill log-correctiongit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-pluginWrote 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/ai-analyst-lab/ai-analyst-plugin/log-correction)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/log-correction"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/log-correction/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/ai-analyst-lab/ai-analyst-plugin/log-correction"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/log-correction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 23 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00096 | $0.02069 |
| Opus 5.5 | $0.00038 | $0.00828 |
| Sonnet 5.5 | $0.00019 | $0.00414 |
| Haiku 4.5 | $0.00010 | $0.00207 |
Grade A, and why
log-correction 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 2mo 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.
This is a copy
100% identical to log-correction — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Log Correction
Purpose
Record analyst mistakes, their fixes, and reusable learnings so future analyses
learn from past errors. Runs in two modes against the same store (defined in
docs/KNOWLEDGE.md): auto mode detects corrections and learnings in the
user's messages without being asked, and manual mode handles explicit
"log a correction" requests with full detail.
When to Use
- Auto: the user corrects your work ("that's wrong", "actually it's...", "you used the wrong column") or teaches a reusable rule ("always use X", "never do Y", "remember that our fiscal year starts in February") without asking you to log anything
- Manual: user says "log a correction", "save this mistake", "record this lesson", or similar
- After discovering and fixing an error mid-analysis worth preserving
Auto Mode
Watch every user message for these signals. When one fires, capture it immediately; the user never has to ask.
Correction signals (something you produced was wrong):
- "that's wrong", "that's incorrect", "actually it's...", "it should be..."
- "the column is X not Y", "you used the wrong...", "off by...", "double-counted", "that join is wrong", "missing a filter", "forgot to exclude..."
Learning signals (a reusable methodology or fact):
- "always use...", "never use...", "next time...", "prefer X over Y"
- "remember that...", "the convention here is...", "our team uses...", "going forward...", "don't forget to..."
If both match, treat it as a correction. If neither matches, do nothing and say nothing about it.
On a correction signal: run Steps 1-5 below, but never interrogate the user. Infer severity, category, dataset, and tables from context; leave fields you cannot infer as null. Acknowledge in one line ("Got it, logged as CORR-008.") and then immediately continue with the user's underlying request; logging is never the whole response.
On a learning signal: append a bullet to .knowledge/learnings/index.md
under the closest category heading (Data Patterns, Query Techniques, Business
Context, Stakeholder Preferences, Visualization Insights, Methodology Notes),
formatted - {concise learning} (source: user feedback, {YYYY-MM-DD}).
Acknowledge in one line ("Noted for future analyses.") and continue with the
user's request.
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.
- 2mo ago First seen · 175 lines · 96 tokens per session scan A 148e411a7125
log-correction is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (34 stars, last pushed 2mo ago), licensed MIT. It adds 96 tokens to every session and 2,069 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to log-correction, differing in 0 lines, and is treated as a copy.
log-correction compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| log-correctionai-analyst-lab/ai-analyst-plugin | Skill | 34 | 2mo ago | MIT | A | 96 | Claude Code |
| knowledge-ingestopenclaw/openclaw | Skill | 394k | yesterday | MIT | A | 80 | Claude Code, Codex |
| mem0-oss-to-platformmem0ai/mem0 | Skill | 68k | 2d ago | Apache-2.0 | A | 217 | Claude Code, Codex |
| forgetasgeirtj/system_prompts_leaks | Skill | 71k | 2d ago | CC0-1.0 | A | 74 | Claude Code, Codex |
| Cortexdanielmiessler/LifeOS | Skill | 20k | yesterday | MIT | A | 184 | Claude Code |
| memoryopensquilla/opensquilla | Skill | 7.1k | 6d ago | Apache-2.0 | A | 44 | Claude Code, Codex |
| ha-data-storesshiwenwen/hope-agent | Skill | 1.9k | yesterday | MIT | A | 115 | Claude Code, Codex |