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 yogsoth-ai/de-anthropocentric-research-engine --skill ahrq-picme-assessmentgit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote 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/yogsoth-ai/de-anthropocentric-research-engine/ahrq-picme-assessment)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/ahrq-picme-assessment"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/ahrq-picme-assessment/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/yogsoth-ai/de-anthropocentric-research-engine/ahrq-picme-assessment"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/ahrq-picme-assessment.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 | $0.00030 | $0.00675 |
| Opus 5 | $0.00015 | $0.00338 |
| Sonnet 5 | $0.00006 | $0.00135 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
ahrq-picme-assessment 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AHRQ PiCMe Assessment
Use the AHRQ PiCMe framework to systematically assess a research gap across 6 dimensions.
HARD-GATE
Pipeline
- Precondition check: verify completeness of the input GapRecord; confirm the domain field is valid
- Population (P): identify the target population/system/dataset the gap concerns; assess clarity of definition (1-5)
- Intervention (I): identify the proposed intervention/method/solution; assess operationalizability (1-5)
- Comparator (C): identify the comparison baseline (existing SOTA, no intervention, alternative approach); assess baseline reasonableness (1-5)
- Metrics (M): identify the evaluation metrics; assess their measurability and relevance (1-5)
- Evidence (E): assess the strength of existing evidence supporting the existence of the gap (1-5)
- Overall verdict: judge overall quality from the mean of the 5 dimensions (strong ≥ 3.5 / moderate 2.5-3.4 / weak < 2.5); generate a research question draft
- Output: return the PiCMeAssessment object
Output Format
{
"gap_id": "gap_001",
"dimensions": {
"population": { "score": 4, "description": "Target population description", "rationale": "..." },
"intervention": { "score": 3, "description": "Intervention/method description", "rationale": "..." },
"comparator": { "score": 3, "description": "Comparison baseline description", "rationale": "..." },
"metrics": { "score": 4, "description": "Evaluation metric description", "rationale": "..." },
"evidence": { "score": 4, "description": "Evidence strength description", "rationale": "..." }
},
"mean_score": 3.6,
"overall_verdict": "strong",
"research_question_draft": "Research question draft (1 sentence)",
"improvement_suggestions": ["Suggestion 1", "Suggestion 2"]
}
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 · 58 lines · 30 tokens per session scan A dad58fd9a6f7
ahrq-picme-assessment is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (462 stars, last pushed 3d ago), licensed Apache-2.0. It adds 30 tokens to every session and 675 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 2026-09-03.
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