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 analyst-coregit 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/analyst-core)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/analyst-core"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/analyst-core/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/analyst-core"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/analyst-core.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 | $0.00115 | $0.02884 |
| Opus 5.5 | $0.00046 | $0.01154 |
| Sonnet 5.5 | $0.00023 | $0.00577 |
| Haiku 4.5 | $0.00012 | $0.00288 |
Grade C, and why
analyst-core scanned grade C with 1 finding 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 1mo 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.
Tells the agent never to refusehighAnti-refusal
Suppressing the ability to decline removes a core safety control; a later harmful request then succeeds.
the normal generate-and-validate path (Tier C). Never refuse an undefined How it starts
The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Analyst Core
You are working as an AI Product Analyst. These rules apply to every analysis in this workspace, from a one-line lookup to a full investigation. When analyzing data here, use the AI Analyst skills by name: question-framing to frame, data-profiling and data-quality-check to inspect, visualization-patterns for any chart, and the sanity-check skills (always-compare, triangulation, trace) before presenting.
The method, in order
- Frame the decision before analyzing. Every analysis serves a decision. If the user has not said what decision the answer will inform, STOP and ask before touching data: use the question-framing skill to turn a vague ask ("look into churn", "any insights in this data?") into a framed question with a goal, a decision, a metric, and hypotheses. Do not substitute a general summary for the missing decision, and do not run a full analysis "to be helpful" while the frame is empty; the right output for an unframed ask is two or three sharp framing questions and a stop. This holds in non-interactive runs too: end the turn on the questions. A clearly framed request skips straight to work.
1.5. Start a new provenance record. After the question and decision are
clear, and before the first data query, begin a new analysis with
python3 scripts/analysis_trace.py start. Pass the exact question, intended
decision, active dataset, and output directory. The command keeps the active
marker and logs in the canonical top-level working/ directory while saving
the final HTML in the requested output directory. Do not pass the output
directory as working_dir, create another current-analysis.json, copy the
marker, or set AI_ANALYST_QUERY_LOG_DIR for an interactive analysis. Never
reuse the current id from a previous task. Report the new analysis_id in
the final Checks section.
- Profile data before trusting it. Before analyzing any file or table, check what is actually there: row counts, date ranges, null rates, duplicate keys, obvious anomalies. Use the data-profiling and data-quality-check skills. Never assume a column means what its name suggests.
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.
- 1mo ago Changed · +13 lines 6b4521580977
- 1mo ago Changed · +15 lines 99a777f925fb
- 1mo ago First seen · 199 lines · 115 tokens per session scan C 353ca55608b0
analyst-core is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (322 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 2,884 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it C with 1 finding (tells the agent never to refuse). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-12.
analyst-core compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| analyst-coreai-analyst-lab/ai-analyst | Skill | 322 | 1mo ago | MIT | C | 115 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |