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 requirement-enrichmentgit 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/requirement-enrichment)<a href="https://agentmods.dev/skills/eliyce/paqad-ai/requirement-enrichment"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/requirement-enrichment/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/requirement-enrichment"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/requirement-enrichment.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 | $0.00019 | $0.01081 |
| Opus 5 | $0.00010 | $0.00541 |
| Sonnet 5 | $0.00004 | $0.00216 |
| Haiku 4.5 | $0.00002 | $0.00108 |
Grade A, and why
requirement-enrichment 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 6d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What It Does
Turns the open decisions an incomplete request leaves behind into the pipeline's S2 clarification batch: a set of FR-4 two-layer question objects, each phrased in the project's own words so a non-technical owner can answer it. This is the S2 question-phrasing step of the spec pipeline (issue #512, B.5.2) — it phrases, it does not judge clarity and does not re-run the plain-language check.
Use This When
Use this when the spec pipeline's label step rated the request vague or okay and unlocked a question budget, and the touched area has been grounded (S0). It is for phrasing genuine ambiguities as answerable questions, not for restating a request that is already clear.
Inputs
- Read the S0 grounding terms and references for the touched area — they are the first-priority vocabulary.
- Read the raw request, tracker notes, and any linked issue or spec — the user's own wording is the second-priority vocabulary.
- Read
references/enrichment-checklist.mdand walkassets/operational-checklist.txtto find the dimensions the request leaves undecided.
Procedure
- Walk
assets/operational-checklist.txt(permissions, auditability, rollback, data-retention, i18n, a11y, observability, docs, feature-flags) andreferences/enrichment-checklist.md; for every dimension the request touches but leaves undecided, hold a candidate question. - Keep only genuine ambiguities, within the question budget the
labelstep unlocked. Drop anything the grounding or the prompt already answers. - Phrase each question as a two-layer object.
business_textand everyoptions[]entry draw vocabulary in priority order: (1) the S0 grounding terms, (2) the user's own prompt, (3) plain English. Given a documented term ("archived invoices"), use it — never a from-the-model synonym ("soft-deleted records"). - Phrase
options[]as OUTCOMES, never mechanisms: "keep trying quietly for an hour, then notify someone", never "exponential backoff". Give at least two options. - Where current behaviour is documented, cite it in the question ("Today, exports include hidden columns — keep that, or leave them out?").
- Write one plain sentence of
why_it_matters. Setgrounded_into the doc/glossary ref the wording came from, ornullwhen it cannot be tied to project evidence. Put any internal mechanism note intechnical_note(never shown to the user). - Emit the batch per
assets/output.template.md; validate the shape withscripts/lint-output.sh(exit 0). Hand the file topaqad-ai spec pipeline record questions <file>, which runs the ledger auto-answer pre-step and persists the surviving batch.
What ships with it
5 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.
- 6d ago Changed · +7 lines · +6 tokens per session d7ffd32ef70d
- 12d ago First seen · 65 lines · 13 tokens per session scan A 61cc4eaa8605
requirement-enrichment is a skill published in the GitHub repository Eliyce/paqad-ai (8 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 1,081 once invoked, about $0.0001 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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