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 skills/ai-analyst-lab/ai-analyst-plugin/analyst-corenpx skills add ai-analyst-lab/ai-analyst-plugin --skill analyst-coregit 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/analyst-core)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/analyst-core"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/analyst-core.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 | $0.00115 | $0.01252 |
| Opus 5 | $0.00057 | $0.00626 |
| Sonnet 5 | $0.00023 | $0.00250 |
| Haiku 4.5 | $0.00012 | $0.00125 |
Grade A, and why
analyst-core 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 — 108 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-plus 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
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Frame the decision before analyzing. Every analysis serves a decision. If the user has not said what decision the answer will inform, ask before touching data. Use the question-framing skill to turn a vague ask ("look into churn") into a framed question with a goal, a decision, a metric, and hypotheses. A clearly framed request can skip straight to work.
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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.
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Every number gets a comparison. A metric alone is trivia. Pair every number with a prior period, a benchmark, or a segment comparison, or say explicitly that no comparison is available. The always-compare skill defines the standard.
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Trace numbers to source. Every finding cites which file or table, which columns, which filter, and which time range it came from. If you cannot trace a headline number back to specific rows, do not present it.
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Parts must sum to totals. When you break a total into segments, add the segments back up. A mismatch means double counting, dropped rows, or a bad join, and it must be resolved before the breakdown ships.
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State what was not checked. Findings are hypotheses until validated. End every analysis with a short Checks section: what was verified, what was not, and what could change the conclusion. Say "the data suggests", not "the data proves", unless validation backs it.
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 · 108 lines · 115 tokens per session scan A 43e14d0cb7b0
analyst-core is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 10d ago), licensed MIT. It adds 115 tokens to every session and 1,252 once invoked, about $0.0006 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.
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