s4h-logic-causality-mapping

s4h-logic-causality-mapping is a skill for Claude Code from human-avatar/skills-for-humanity. It costs 118 tokens per session (1,505 once invoked), scanned A, original, MIT.

A causal-reasoning tool that maps what causes what, which parts depend on each other, and what may happen when something changes. It separates cause-and-effect relationships from events that merely occur together or one after another.

In plain words
What is it for?
Use it to trace root causes, map dependencies, assess proposed changes, and predict what could break or follow from a plan.
Why use it?
It helps prevent wrong diagnoses and fixes based on coincidence, timing, or incomplete dependency chains.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: names the AskUserQuestion tool.

Part of the skills-for-humanity plugin — 197 skills, 1 hook shipped together

Good fit Use it to trace root causes, map dependencies, assess proposed changes, and predict what could break or follow from a plan.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/human-avatar/skills-for-humanity/s4h-logic-causality-mapping
Install

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.

Any agent
npx skills add human-avatar/skills-for-humanity --skill s4h-logic-causality-mapping
Clone the repo
git clone --depth 1 https://github.com/human-avatar/skills-for-humanity

Made for: Claude Code.

Or install skills-for-humanity, the plugin that ships this one along with the rest of its 197 skills, 1 hook.

Wrote 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.

agentmods badge for s4h-logic-causality-mapping

README.md
[![agentmods](https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-logic-causality-mapping/github.svg)](https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-logic-causality-mapping)
Your own site
<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.

agentmods 80×15 button for s4h-logic-causality-mapping

Your own site · 80×15
<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>
Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,505 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash 1f4b5e3369e7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

skills/s4h-logic-causality-mapping/SKILL.md · 145 lines

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:

  1. State the observed effect precisely. Not "the system is slow" — "p95 latency increased from 120ms to 840ms after the Tuesday deploy."
  2. Ask: what are the immediate causes that could produce this effect? List all plausible candidates.
  3. For each candidate: what evidence would confirm or rule it out?
  4. Eliminate candidates. For the survivors: what caused them?
  5. Continue until you reach a cause that has no upstream cause within scope — or a point where further tracing requires different expertise or data.
  6. 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.

Read the full file on GitHub · 145 lines

Changes

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.

  1. 8d ago First seen · 145 lines · 118 tokens per session scan A 1f4b5e3369e7

Subscribe to this mod's changes

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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