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 agentmods add commands/ai-analyst-lab/ai-analyst-plugin/analystgit 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/commands/ai-analyst-lab/ai-analyst-plugin/analyst)<a href="https://agentmods.dev/commands/ai-analyst-lab/ai-analyst-plugin/analyst"><img src="https://agentmods.dev/badge/commands/ai-analyst-lab/ai-analyst-plugin/analyst.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 | $0.00020 | $0.00388 |
| Opus 5 | $0.00010 | $0.00194 |
| Sonnet 5 | $0.00004 | $0.00078 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
analyst 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 3d 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.
What it actually says
Start a data analysis using the ai-analyst-plus method. The user's request: $ARGUMENTS
Follow the analyst-core skill's rules for the whole session. Concretely:
-
Frame first. If $ARGUMENTS is empty or does not state the decision the analysis will inform, ask for it before touching data. Use the question-framing skill to establish goal, decision, metric, and hypotheses. If the request is already clearly framed, confirm the framing in one or two sentences and proceed.
-
Load context. Check for a
.knowledge/folder in the working folder. If present, read the dataset notes, quirks, and logged corrections before any query. If absent, offer to bootstrap it with the knowledge-bootstrap skill. -
Profile the data. Run the data-profiling and data-quality-check skills on the files or tables involved: row counts, date ranges, nulls, duplicate keys, anomalies. Report what you found before analyzing.
-
Analyze. Do the comparison the question needs (segment, funnel, trend, decomposition). Every number carries a comparison per the always-compare skill. Build any chart with the visualization-patterns skill.
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Validate. Trace every headline number to its source rows. Sum parts back to totals. Cross-check with the triangulation and trace skills.
-
Deliver. Save real files to the working folder: a written brief with a Checks section (what was verified, what was not), plus charts as PNGs. Log any correction the user makes to
.knowledge/corrections/.
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.
- 3d ago First seen · 35 lines · 20 tokens per session scan A 5f916085ec71
analyst is a command published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 7d ago), licensed MIT. It adds 20 tokens to every session and 388 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 commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.