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 appropriateness-boundinggit 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/appropriateness-bounding)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/appropriateness-bounding"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/appropriateness-bounding/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/appropriateness-bounding"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/appropriateness-bounding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Prompt Injection · line 80 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00024 | $0.00739 |
| Opus 5.5 | $0.00010 | $0.00296 |
| Sonnet 5.5 | $0.00005 | $0.00148 |
| Haiku 4.5 | $0.00002 | $0.00074 |
Grade A, and why
appropriateness-bounding 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 1mo 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Appropriateness Bounding
Purpose: Determine what is appropriate, acceptable, or indicated for a given context. Uses the RAND/UCLA Appropriateness Method (rating + discussion + re-rating) or Consensus Conference (citizen jury) format to establish boundaries of acceptability.
When to use:
- Medical guideline development (appropriate indications)
- Regulatory standard setting
- Establishing acceptable thresholds for action
- Any question of the form "is X appropriate when Y?"
Budget
| Parameter | Constraint |
|---|---|
| Rounds | 2 (rate → discuss → re-rate) |
| Perspectives | ≥4 (ideally 7–15 for RAND/UCLA) |
| Rating scale | 1–9 (inappropriate to appropriate) |
| Agreement threshold | Median ≥7 without disagreement |
State Ledger
| Key | Type | Description |
|---|---|---|
| indications | array | List of scenarios to rate |
| perspectives | array | Panel member perspectives |
| round_1_ratings | array | Initial ratings per indication |
| discussion_notes | string | Key points from discussion |
| round_2_ratings | array | Post-discussion ratings |
| classifications | object | Appropriate/uncertain/inappropriate per item |
Available Tactics
- iterative-convergence-round — Two-round rate-discuss-rerate cycle
- threshold-calibration — Determine where appropriateness boundaries fall
Available SOPs
- judgment-collection
- feedback-distribution
- consensus-measurement
- round-decision
- threshold-sweep
- consensus-classification
- consensus-synthesis
Execution Guidance
- Define indications/scenarios clearly (clinical scenarios, use cases)
- Collect Round 1 ratings (1–9 scale) with brief rationale
- Distribute feedback showing distribution of ratings
- Facilitate structured discussion of disagreements
- Collect Round 2 ratings
- Classify each indication: appropriate (median 7–9), uncertain (4–6), inappropriate (1–3)
- Flag items with disagreement (where panel lacks agreement despite median)
Output Format
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.
- 1mo ago First seen · 100 lines · 24 tokens per session scan A 9d47d1bce3fb
appropriateness-bounding is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (505 stars, last pushed 4d ago), licensed Apache-2.0. It adds 24 tokens to every session and 739 once invoked, about $0.0001 per session on Opus 5.5. 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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