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 kclemoveki/agentic-skills-eda --skill eda-reportgit clone --depth 1 https://github.com/kclemoveki/agentic-skills-edaWrote 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/kclemoveki/agentic-skills-eda/eda-report)<a href="https://agentmods.dev/skills/kclemoveki/agentic-skills-eda/eda-report"><img src="https://agentmods.dev/badge/skills/kclemoveki/agentic-skills-eda/eda-report/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/kclemoveki/agentic-skills-eda/eda-report"><img src="https://agentmods.dev/badge/skills/kclemoveki/agentic-skills-eda/eda-report.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 | $0.00062 | $0.02514 |
| Opus 5 | $0.00031 | $0.01257 |
| Sonnet 5 | $0.00012 | $0.00503 |
| Haiku 4.5 | $0.00006 | $0.00251 |
Grade A, and why
eda-report 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 9d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: EDA Report
Take the executed and annotated notebook at $ARGUMENTS and produce an executive markdown report at reports/eda_report_<dataset_stem>.md. The report is for non-technical stakeholders — managers, product owners, business leads — who need the conclusions without reading 50+ cells.
This skill closes the loop opened by /analyze-dataset (structure with placeholders), /execute-notebook (run the code) and /annotate-findings (write real observations). If the report comes before any of those three, fail with a clear hint.
Principle
A report is synthesis, not transcription. The notebook may have 30 observations across 9 sections; the report has 3-6 findings. The skill must select, rank, and rewrite — never just copy markdown cells verbatim.
Two non-negotiable rules:
- Every quantitative claim must trace back to a real cell output in the notebook. Never invent numbers (same rule as
/annotate-findings). - Zero code in the report body. Code lives in the notebook, which is linked from the Appendix.
Step 1 — Validate input
- Verify the notebook file exists and is readable.
- Verify it has been executed: at least one code cell with non-empty
outputsand a non-nullexecution_count. If pristine, fail with:Notebook not executed. Run /execute-notebook first. - Verify it has been annotated: NO markdown cell should contain the literal marker
<!-- annotate-findings: pending -->. If markers remain, fail with:Notebook has unfilled placeholders. Run /annotate-findings first.
Step 2 — Extract content from the notebook
First, ensure the output directory tree exists. Create reports/ and reports/figures/ (relative to the current working directory) if they do not already exist. Use mkdir -p reports/figures via Bash or Path("reports/figures").mkdir(parents=True, exist_ok=True) in the Python script. This is a hard prerequisite for the rest of Step 2 — without these directories, the image extraction below fails silently.
What ships with it
1 file 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.
- 9d ago First seen · 135 lines · 62 tokens per session scan A c16b3bc69c20
eda-report is a skill published in the GitHub repository kclemoveki/agentic-skills-eda (2 stars, last pushed 4mo ago), licensed MIT. It adds 62 tokens to every session and 2,514 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 2026-08-31.
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