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 Eliyce/paqad-ai --skill module-attribution-extractorgit clone --depth 1 https://github.com/Eliyce/paqad-aiWrote 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/eliyce/paqad-ai/module-attribution-extractor)<a href="https://agentmods.dev/skills/eliyce/paqad-ai/module-attribution-extractor"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/module-attribution-extractor/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/eliyce/paqad-ai/module-attribution-extractor"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/module-attribution-extractor.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00048 | $0.01421 |
| Opus 5 | $0.00024 | $0.00711 |
| Sonnet 5 | $0.00010 | $0.00284 |
| Haiku 4.5 | $0.00005 | $0.00142 |
Grade A, and why
module-attribution-extractor 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What It Does
Applies the framework-owned pattern set (Module: / Component: / Area: / Subsystem: ticket headers; module: <slug>; new module <Name>; in the <name> module) to the prompt. Emits one candidate per distinct slug, classified as exact-match, near-collision (Levenshtein ≤ 2 against an existing slug), or unknown. Multiple modules in a single prompt produce one MD-XXXX draft each (spec AC #9).
Use This When
- Running the Attribution Gate at the start of
feature-development.planning. - A user pastes a ticket and you need a deterministic first pass before invoking the inferencer.
- A user prompt explicitly names a module (
module: billing,Subsystem: Reporting).
Inputs
prompt— required. The user-supplied text (typically a ticket body or feature request).project_root— optional. Used to resolvemodule-map.ymlfor collision detection. Defaults tocwd.- The pattern set itself is closed and lives in
runtime/base/skills/module-attribution-extractor/references/pattern-set.md.
Procedure
- Resolve the project root (default
cwd). - Invoke the TS engine via the bundled wrapper:
The wrapper shells out tobash scripts/extract.sh <prompt-file> [project-root]paqad-ai module-decisions extract --project-root <root> --prompt-file <tmp>. Prefer--prompt-file(the wrapper always uses it) so multiline ticket text survives the shell. - Parse the emitted JSON. Each entry in
candidateshas:slug,display_name,kind,collision_with,pattern,excerpt. - If
needs_decisionis empty, the extractor either found nothing or every hit was anexact-match— exit with the literal statusextractor: no-decision-neededso the caller can fall through to the inferencer (the empty case) or continue planning (the exact-match case). - Otherwise, for each candidate in
needs_decision, surface a Decision Pause packet viaAskUserQuestion(or the active adapter's Decision Pause Contract entry point).
What ships with it
7 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.
- 9d ago First seen · 99 lines · 48 tokens per session scan A dd6c4742982d
module-attribution-extractor is a skill published in the GitHub repository Eliyce/paqad-ai (8 stars, last pushed 2d ago), licensed MIT. It adds 48 tokens to every session and 1,421 once invoked, about $0.0002 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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