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Iteratively fix all failing tests until the test suite is green
Passed 11 of 16 test cases with the skill, and 6 of 16 without it.
and 13 more in the full report
10 fixed5 passed either way4 still failing3 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
What type of step execution is defined for generating the fix report summary in step 2?
| Checked | Without skill | With skill |
|---|---|---|
| Defines step 2 present as an inline step | Missed | Passed |
The instructions your model reads when the skill loads.
Before starting execution, initialize task_context.json:
{
"task_id": "<task_id from input>",
"current_step": 0,
"current_step_id": null,
"status": "running",
"steps": {
"fix_loop": "pending",
"present": "pending"
},
"updated_at": "<ISO timestamp>"
}
Update this file after each step completes. On error, set step status to "failed" and overall status to "failed".
Type: loop Description: Run tests, analyze failures, fix source code, repeat until green
Goal: pytest exits with 0 failures (all tests pass) Max Iterations: 10
This step executes as a loop. The body steps repeat until the goal is met or max iterations reached.
Type: script
test cases passed, vs 6 / 16 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.