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 human-avatar/skills-for-humanity --skill s4h-decision-criteria-weightinggit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-decision-criteria-weighting)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-decision-criteria-weighting"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-decision-criteria-weighting/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/human-avatar/skills-for-humanity/s4h-decision-criteria-weighting"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-decision-criteria-weighting.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 | $0.00063 | $0.01186 |
| Opus 5 | $0.00032 | $0.00593 |
| Sonnet 5 | $0.00013 | $0.00237 |
| Haiku 4.5 | $0.00006 | $0.00119 |
Grade A, and why
s4h-decision-criteria-weighting 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 12d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision Criteria Weighting
Intuitive decisions fail when too many criteria are in play and their relative importance isn't made explicit. This skill forces that explicitness. The goal is not to replace judgment — it is to make the judgment visible enough to inspect, challenge, and defend.
Your Process
Step 1: State the Decision and List Real Options Name the decision. List the actual options being considered — not aspirational ones. If an option isn't genuinely available, remove it before it contaminates the analysis.
Framing check: Confirm the decision and its intended outcome before continuing. State what you've identified — the specific decision being made and the options on the table — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the decision and the options being compared]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
Step 2: Identify 4-8 Criteria Name the criteria that define a good outcome for this specific decision. Criteria should be independent (not measuring the same thing twice), observable (you can score against them), and genuinely relevant (removing one would change the analysis).
Before narrowing: Show the complete generated set of candidate criteria to the user first. Use AskUserQuestion:
- Question: "I've identified [N] candidate criteria. Before I narrow to the most decision-relevant ones, are there any you'd flag as especially important, or any I've missed?"
- Header: "Prioritise"
- Options:
- Proceed with your selection — the set looks right
- Flag one — user will name a specific criterion to include
- Add a missing one — user will describe it
Step 3: Weight the Criteria Distribute exactly 100 points across the criteria. This forces trade-offs — you cannot weight everything highly. If everything matters equally, the distribution reveals a failure to think through what actually matters most.
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.
- 12d ago First seen · 120 lines · 63 tokens per session scan A baf02e63f7b4
s4h-decision-criteria-weighting is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 63 tokens to every session and 1,186 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 2026-08-30.
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