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Gather evidence for causal claims
Passed 19 of 22 test cases with the skill, and 7 of 22 without it.
and 17 more in the full report
13 fixed6 passed either way2 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
Before deciding whether to continue or exit an evidence collection loop iteration, what action must be performed in the output stream?
| Checked | Without skill | With skill |
|---|---|---|
| The response mandates printing the state ledger before each iteration decision. | Missed | Passed |
The instructions your model reads when the skill loads.
Systematically gather and attach evidence pages to the causal claims in the model, creating supported_by edges for confirming evidence and contradicts edges for disconfirming evidence. A causal model without evidence provenance is speculation; this strategy converts it into a structured, auditable knowledge artifact.
CC should treat evidence collection as adversarial by default: for every supported_by edge added, actively search for contradicting evidence before moving on. The contradicts edges are as important as the supported_by edges — a model that only records confirming evidence is biased. Evidence pages must record the source, study design (where applicable), and a brief assessment of quality. Quantity matters less than coverage across independent sources.
| Metric | S | M | L |
|---|---|---|---|
| Evidence pages created | 8 | 20 | 45 |
| supported_by edges | 10 | 25 | 50 |
| contradicts edges flagged | 2 | 5 | 10 |
| Metric | Target | Current | Status |
|--------------------------|--------|---------|--------|
| Evidence pages created | S:8 / M:20 / L:45 | 0 | ⬜ |
| supported_by edges | S:10 / M:25 / L:50 | 0 | ⬜ |
| contradicts edges flagged | S:2 / M:5 / L:10 | 0 | ⬜ |
test cases passed, vs 7 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.