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 causal-claim-extractiongit 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/causal-claim-extraction)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/causal-claim-extraction"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/causal-claim-extraction/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/causal-claim-extraction"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/causal-claim-extraction.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 | $0.00038 | $0.00310 |
| Opus 5.5 | $0.00015 | $0.00124 |
| Sonnet 5.5 | $0.00008 | $0.00062 |
| Haiku 4.5 | $0.00004 | $0.00031 |
Grade A, and why
causal-claim-extraction 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- causal-claim-extraction — 100% identical, 0 lines differ
What it actually says
Causal Claim Extraction
Extracts all explicit and implicit causal claims from an artifact.
Execution
Subagent — spawned via subagent-spawning/spawn-agent.
Why Subagent
Causal claim extraction requires careful linguistic analysis of the entire artifact. Isolated context prevents premature evaluation of claims.
Input
- artifact: The artifact to analyze
- artifact_type: Type of artifact (gap, hypothesis, claim, etc.)
Output
- causal_claims: List of {cause, effect, strength, evidence, location}
- causal_graph: Directed graph of cause-effect relationships
- claim_count: Total number of causal claims found
Budget
One unit = one extraction pass per artifact.
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 51 lines · 38 tokens per session scan A c27c372c4e88
causal-claim-extraction is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (505 stars, last pushed 5d ago), licensed Apache-2.0. It adds 38 tokens to every session and 310 once invoked, about $0.0002 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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