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 annotate-findingsgit 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/annotate-findings)<a href="https://agentmods.dev/skills/kclemoveki/agentic-skills-eda/annotate-findings"><img src="https://agentmods.dev/badge/skills/kclemoveki/agentic-skills-eda/annotate-findings/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/annotate-findings"><img src="https://agentmods.dev/badge/skills/kclemoveki/agentic-skills-eda/annotate-findings.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 | $0.00058 | $0.01211 |
| Opus 5 | $0.00029 | $0.00606 |
| Sonnet 5 | $0.00012 | $0.00242 |
| Haiku 4.5 | $0.00006 | $0.00121 |
Grade A, and why
annotate-findings 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 10d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Annotate Findings
Take the notebook at $ARGUMENTS (which must already have been executed) and replace every placeholder observation cell with real findings derived from the executed outputs of the surrounding code cells.
This skill closes the loop opened by /analyze-dataset (which writes placeholders) and /execute-notebook (which produces real outputs). It eliminates the most common failure of generated notebooks: markdown that claims results that the code never actually proved.
Principle
A finding is only valid if it traces back to an executed output. This skill never invents numbers. If the preceding code cell has no output, or the output cannot be interpreted (binary blobs, image data only), the placeholder gets a clear note saying so — never fabrication.
Step 1 — Validate input
- Verify the notebook file exists.
- Verify it has been executed: at least one code cell must have non-empty
outputsand a non-nullexecution_count. If the notebook looks pristine (no executions), fail with:Notebook does not appear to have been executed. Run /execute-notebook first. - Locate all markdown cells containing the literal marker
<!-- annotate-findings: pending -->. If none are found, reportNo placeholder cells found — nothing to annotate.and exit cleanly.
Step 2 — For each placeholder, gather context
For each placeholder markdown cell at index i:
- Extract the "Qué buscar" bullets from the placeholder (they list what to inspect — use them as the question prompt).
- Walk backwards from cell
icollecting code cells until you hit either a section header (## N.) or another placeholder. These are the code cells whose outputs feed this observation. - From each gathered code cell, extract:
- The cell's
source(so the skill can read what was computed). - All
outputsof typesstream(stdout/stderr text),execute_result(text/plain representation of last expression), anddisplay_data(descriptions of figures — read thetext/plainfallback if present, otherwise note "figure produced").
- The cell's
- Concatenate sources + outputs into a context block per placeholder.
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.
- 10d ago First seen · 90 lines · 0 tokens per session scan A b0455fd47150
annotate-findings is a skill published in the GitHub repository kclemoveki/agentic-skills-eda (2 stars, last pushed 4mo ago), licensed MIT. It adds 58 tokens to every session and 1,211 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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