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-causality-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-causality-mapping)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-logic-causality-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-logic-causality-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-causality-mapping"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-logic-causality-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 | $0.00118 | $0.01505 |
| Opus 5 | $0.00059 | $0.00753 |
| Sonnet 5 | $0.00024 | $0.00301 |
| Haiku 4.5 | $0.00012 | $0.00151 |
Grade A, and why
s4h-logic-causality-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 8d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Logic Causal Reasoning
Correlation isn't causation. Neither is sequence. "This happened, then that happened" is not the same as "this caused that" — but it's treated as equivalent constantly, and it produces wrong diagnoses, failed fixes, and surprised engineers.
This skill makes causal structure explicit: what actually depends on what, what change produces what effect, and what must be true for a plan to hold.
Four Modes
Use the mode that matches the question.
Framing check: Confirm the specific causal situation before selecting a mode. State what you've identified — the system or situation, the observed effect or proposed change, and the core causal question — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific situation and causal question]. 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
Mode 1: Root Cause Tracing
"Why did this happen?"
Work backwards from an observed effect to its cause — and to the cause of that cause.
Process:
- State the observed effect precisely. Not "the system is slow" — "p95 latency increased from 120ms to 840ms after the Tuesday deploy."
- Ask: what are the immediate causes that could produce this effect? List all plausible candidates.
- For each candidate: what evidence would confirm or rule it out?
- Eliminate candidates. For the survivors: what caused them?
- Continue until you reach a cause that has no upstream cause within scope — or a point where further tracing requires different expertise or data.
- Distinguish: root cause (the origin), proximate cause (the immediate trigger), contributing factors (conditions that allowed it).
Mode 2: Impact Mapping
"What breaks if I change X?"
Work forwards from a proposed change through its downstream effects.
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.
- 8d ago First seen · 145 lines · 118 tokens per session scan A 1f4b5e3369e7
s4h-logic-causality-mapping is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 118 tokens to every session and 1,505 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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