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Strategy for mining concepts from sources — systematic extraction, page creation, alias resolution.
Passed 16 of 20 test cases with the skill, and 10 of 20 without it.
and 11 more in the full report
8 fixed10 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
In a Small budget concept extraction run (targets: 8 sources, 12 concepts, 3 aliases), we have currently processed 6 sources, extracted 9 concepts, and resolved 2 aliases. Can we exit the execution loop according to the hard gate criteria?
| Checked | Without skill | With skill |
|---|---|---|
| Evaluates whether 80% of each target metric has been achieved. | Missed | Passed |
| Concludes that the iteration cannot exit yet because 6 of 8 sources processed is below the 80% threshold. | Missed | Passed |
The instructions your model reads when the skill loads.
Mine source pages for concepts. Create concept pages, resolve aliases, ensure every concept is properly named and connected.
Extract concepts systematically from sources. Prefer precision over recall — it's better to have 20 well-defined concepts than 50 vague ones. Resolve naming conflicts immediately.
| Metric | S | M | L |
|---|---|---|---|
| Sources processed | 8 | 20 | 40 |
| Concepts extracted | 12 | 30 | 60 |
| Aliases resolved | 3 | 10 | 20 |
| Metric | Target | Current | Status |
|---------------------|--------|---------|--------|
| Sources processed | X | 0 | ⬜ |
| Concepts extracted | X | 0 | ⬜ |
| Aliases resolved | X | 0 | ⬜ |
Optional, no fixed order; the final leaf is always a sop.
test cases passed, vs 10 / 20 without
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