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Apply when you catch yourself writing the same instruction a second time, or notice a recurring correction. Encode the rule as a lint, metadata flag, runtime check, or script instead of more text.
Passed 18 of 23 test cases with the skill, and 17 of 23 without it.
2 fixed17 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
In the python service inventory-api, developers keep putting relative imports before standard library imports. You have twice updated CONTRIBUTING.md asking developers to order imports correctly, but PR reviews still catch misordered imports. What concrete action should be taken now?
| Checked | Without skill | With skill |
|---|---|---|
| Recommend implementing an automated import linter rule or tool. | Passed | Passed |
| Recommend removing the manual text instruction from CONTRIBUTING.md. | Missed | Passed |
The instructions your model reads when the skill loads.
Encode recurring fixes in mechanisms (tools, code, metadata, automation) instead of textual instructions. Every error, human correction, and unexpected outcome is a learning signal. Capture it, route it, and close the loop.
Why: Textual instructions are easy to miss. They require the reader to notice, remember, and comply. Structural mechanisms (lint rules, metadata flags, runtime checks, automation scripts) enforce the rule without cooperation.
Pattern: When you catch yourself writing the same instruction a second time:
Corollary: Don't paper over symptoms. If the fix is structural, ONLY use the structural fix. The instruction IS the symptom.
Feedback loop:
Anti-patterns:
test cases passed, vs 17 / 23 without
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