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 QinghongLin/data2story-skill --skill analystgit clone --depth 1 https://github.com/QinghongLin/data2story-skillWrote 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/qinghonglin/data2story-skill/analyst)<a href="https://agentmods.dev/skills/qinghonglin/data2story-skill/analyst"><img src="https://agentmods.dev/badge/skills/qinghonglin/data2story-skill/analyst.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00059 | $0.02202 |
| Opus 5 | $0.00030 | $0.01101 |
| Sonnet 5 | $0.00012 | $0.00440 |
| Haiku 4.5 | $0.00006 | $0.00220 |
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 8d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyst
Your job is completeness, not curation. List every analysis this dataset can support, grounded in the context the Detective found. You are not deciding what story to tell — that is the Editor's job. You are cataloguing what the data contains.
Setup
DATA_DIR= first argumentPROJECT_DIR= second argument- Read
PROJECT_DIR/detective.jsonbefore starting — it tells you what matters in this domain - Also read
PROJECT_DIR/scout.jsonif present — surface anylive_status[]entries as display-only context (cite the dated source; never feed live status to a forecasting/training model) - Outputs:
PROJECT_DIR/code/*.py(analysis scripts),PROJECT_DIR/analyst.json
Steps
1. Dataset Profile
Run code to compute:
- File(s), format, row count, column count
- What one row represents
- Time range, geographic scope
- Missing value counts per column
- Cardinality of categorical columns
2. Field Inventory
For every column:
- Name, inferred meaning, data type
- Sample values
- Noteworthy distributions or quirks
3. All Possible Analyses
Run actual code (Python/Bash) for every applicable category below. Record the actual numbers — not descriptions of what could be computed.
Distributions — value counts for every categorical field; histogram buckets for every numeric field; null/missing rates.
Rankings — top and bottom N for every meaningful dimension; concentration (what % of outcomes does the top 10% account for?).
Group Comparisons — every categorical field as a grouping variable against every numeric/outcome field; note effect size, not just direction.
Correlations & Relationships — pairwise relationships between numeric fields; categorical interactions (e.g. A × B → outcome).
Trends & Sequences — time-based patterns if a date/order field exists; first vs. last, early vs. late.
Anomalies — values more than 2 SD from mean; unexpected zeros, near-perfect concentrations, impossible combinations.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 126 lines · 59 tokens per session scan A 8f2e77130e54
analyst is a skill published in the GitHub repository QinghongLin/data2story-skill (155 stars, last pushed 2mo ago), licensed MIT. It adds 59 tokens to every session and 2,202 once invoked, about $0.0003 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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