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-logic-constraint-mappinggit 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-logic-constraint-mapping)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-logic-constraint-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-logic-constraint-mapping/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-logic-constraint-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-logic-constraint-mapping.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 | $0.00115 | $0.01372 |
| Opus 5 | $0.00057 | $0.00686 |
| Sonnet 5 | $0.00023 | $0.00274 |
| Haiku 4.5 | $0.00012 | $0.00137 |
Grade A, and why
s4h-logic-constraint-mapping 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Logic Constraint Mapping
Every decision happens inside a constraint space. Some limits are real and fixed. Others feel fixed but aren't. And some constraints conflict with each other in ways nobody has named yet.
The map makes that space visible — so you're solving the actual problem, not a version of it you've accidentally invented by treating assumptions as facts.
Types of Constraints
Hard constraints — cannot be violated without abandoning the goal entirely. Physical laws, legal requirements, contractual obligations, irreversible dependencies.
Soft constraints — strong preferences or defaults that can be negotiated under sufficient pressure. Budget, timeline, team size, technology choices, organisational preferences.
Hidden constraints — not explicitly stated, but load-bearing. Discovered when violated. Often cultural, political, or architectural. The most dangerous kind.
Conflicting constraints — two constraints that cannot both be fully satisfied. Require a conscious trade-off decision rather than a solution.
Your Process
Step 1: Extract stated constraints What limits have been explicitly named? Separate:
- Stated as hard: "must", "cannot", "required", "non-negotiable"
- Stated as soft: "should", "prefer", "ideally", "target"
- Implied but unstated: present in the problem framing without being declared
Framing check: Confirm the specific constraint landscape before continuing. State what you've identified — the decision or plan being constrained and its primary goal — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific decision or plan and what it's trying to achieve]. 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: Test each constraint's hardness For every constraint labelled hard, ask: what would actually happen if we violated it?
- Legal/regulatory: real hard — violation has defined consequences
- Budget/timeline: often softer than declared — the consequence is negotiation, not failure
- Technical: depends on reversibility — changing a database schema is hard; changing a variable name is not
- Organisational: often the softest of all, disguised as the hardest
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 · 129 lines · 115 tokens per session scan A 8e918387abdf
s4h-logic-constraint-mapping is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 1,372 once invoked, about $0.0006 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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