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 --skill historygit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/history)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/history"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/history/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/history"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/history.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 | $0.00204 | $0.01053 |
| Opus 5 | $0.00102 | $0.00526 |
| Sonnet 5 | $0.00041 | $0.00211 |
| Haiku 4.5 | $0.00020 | $0.00105 |
Grade A, and why
history 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 2d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: History
Purpose
Browse and search past analyses from the analysis archive. Helps users recall what they've analyzed before, find prior findings, and build on previous work.
When to Use
- User says
/historyor "what have I analyzed before?" - At session start, to provide context on prior work
- When framing a new question, to check if similar analysis exists
Invocation
/history — list recent analyses (last 10)
/history {id} — show full details for a specific analysis
/history search={term} — search by title, question, or tags
/history --all — list all analyses across all datasets
/history dataset={id} — filter to a specific dataset
Instructions
Step 1: Load Archive
- Read
.knowledge/analyses/index.yaml - If empty: "No analyses archived yet. Complete an analysis and it will appear here."
Step 2: Determine Active Dataset (Critical)
IMPORTANT: Before filtering results, determine the active dataset:
- Read
.knowledge/active.yamlto get the active dataset ID - If
--allflag is present: skip filtering, show all datasets - If
dataset={id}is specified: filter to that dataset - Otherwise: filter to active dataset only
This ensures users see relevant analyses for their current working context by default.
Step 3: Execute Command
List recent (/history):
- Filter to active dataset (unless
--allflag present) - Sort by date descending
- Show last 10 as a table: date, title, level, key finding count, dataset
- Show total count: "Showing 10 of {total} analyses for {dataset_name}." (or "across all datasets" if --all)
Show specific (/history {id}):
- Find entry by ID in index
- Display: title, date, question, level, all key findings, metrics used, agents used, output files, tags, confidence, recommendations
- If output files exist, offer: "Want to review the full analysis? The deck is at {path}"
Search (/history search={term}):
- Filter to active dataset first (unless
--allordataset={id}specified) - Search across: title, question, key_findings, tags (case-insensitive)
- Display matching entries as a table
- If no matches: "No analyses match '{term}' in {dataset_name}. Try broader terms or use
--allto search across all datasets."
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.
- 2d ago First seen · 84 lines · 204 tokens per session scan A cd463b167e45
history is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (298 stars, last pushed 3d ago), licensed MIT. It adds 204 tokens to every session and 1,053 once invoked, about $0.0010 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 2026-09-12.
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