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Extract all causal claims (X causes Y, X leads to Y, X enables Y) from an artifact, producing a structured list of cause-effect pairs.
Passed 14 of 23 test cases with the skill, and 6 of 23 without it.
and 5 more in the full report
8 fixed6 passed either way9 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
We have a 50-page architecture review document 'Enterprise Cloud Migration Plan'. How many extraction passes per artifact are budgeted for this analysis?
| Checked | Without skill | With skill |
|---|---|---|
| allocates one extraction pass per artifact | Missed | Passed |
The instructions your model reads when the skill loads.
Extracts all explicit and implicit causal claims from an artifact.
Subagent — spawned via subagent-spawning/spawn-agent.
Causal claim extraction requires careful linguistic analysis of the entire artifact. Isolated context prevents premature evaluation of claims.
One unit = one extraction pass per artifact.
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. |
test cases passed, vs 6 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.