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-inferencergit 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-inferencer)<a href="https://agentmods.dev/skills/eliyce/paqad-ai/module-attribution-inferencer"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/module-attribution-inferencer/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-inferencer"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/module-attribution-inferencer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 104 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00064 | $0.01462 |
| Opus 5 | $0.00032 | $0.00731 |
| Sonnet 5 | $0.00013 | $0.00292 |
| Haiku 4.5 | $0.00006 | $0.00146 |
Grade A, and why
module-attribution-inferencer 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 11d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What It Does
Runs only when the module-attribution-extractor returned extractor: no-decision-needed with zero raw candidates. Tokenises the prompt, scores each existing module's name + feature names + source paths + evidence (symbols, routes, tables) against it, weights name/slug tokens 2× over path tokens, and emits a ranked multi-choice draft. Always includes two fallback choices (new-module-fallback, no-attribution) so the Decision Pause packet is complete even when nothing scores above the floor.
Use This When
- The extractor produced zero candidates (no ticket headers, no
module:markers, nonew modulephrasing). - You need to ask the user "which module does this belong to?" before continuing planning.
Do not run this when the extractor already produced candidates — that path is owned by module-attribution-extractor.
Inputs
prompt— required. The user-supplied text.project_root— optional. Defaults tocwd. Used to loadmodule-map.yml.max_choices— optional. Cap on existing-module choices returned (default3).- Scoring details live in
runtime/base/skills/module-attribution-inferencer/references/scoring.md.
Procedure
- Resolve the project root (default
cwd). - Invoke the TS engine via the bundled wrapper:
The wrapper shells out tobash scripts/infer.sh <prompt-file> [project-root] [max-choices]paqad-ai module-decisions infer --project-root <root> --prompt-file <tmp> [--max-choices N]. - Parse the emitted JSON. Fields:
choices[](sorted byscoredesc, with fallbacks last),prompt_tokens,confident. - Surface a single Decision Pause packet to the user (one packet for the inferencer, not one-per-choice).
Decision Pause Packet Shape
- Question —
Which module does this prompt belong to? - Header —
Module attribution. - Options — one per
choicesentry. Each option label:extend-existing→Extend "<name>" (<slug>)with thereasoningshown as description.new-module-fallback→Introduce a new module(description: collect a name from the user, then hand off back tomodule-attribution-extractor).no-attribution→Skip attribution for this prompt(description: continue planning with no module-map mutation; record nothing).
What ships with it
6 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.
- 11d ago First seen · 106 lines · 64 tokens per session scan A dfef4ce4f7c2
module-attribution-inferencer is a skill published in the GitHub repository Eliyce/paqad-ai (8 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 1,462 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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