Agent Skills: Run Skill Eval

Run Skill Eval

Run the skill eval suite for the skill path given in the request. If no path is given, resolve the target from the current working directory: use it when it contains eval/eval.yaml, otherwise ask which skill to evaluate rather than guessing.

Constraints

• This skill requires Bash to run grader scripts. It must execute in the main session — do NOT delegate to a background subagent. Background subagents auto-deny Bash prompts, causing silent failures.
• Run every task in the eval.yaml — do not skip any.
• For each task, actually execute the full skill procedure (all checks). Do not shortcut.
• Write output files in the exact format the skill specifies so graders can parse them.
• If a grader fails to run, report score 0.0 for that task and note the error.
• For llm_rubric graders, be strict — only score 1.0 if the output clearly meets the rubric criteria.
• This runner executes one trial per task. The scaffolded eval.yaml sets trials: 5 for skillgrade in CI, so a flaky task may pass here and still fail CI. Treat a local pass as a smoke signal, not a CI guarantee.

Steps

1. Cleanup: Delete any eval/output.md and eval/output-*.md files from a previous run so graders see fresh results.

2. Read the eval/eval.yaml inside the skill directory. Parse defaults.threshold (the pass mark skillgrade enforces in CI; treat a missing value as 1.0). Then parse all tasks — each has a name, instruction, workspace (fixture path), and graders.

3. For each task: a. Read the skill's SKILL.md to understand what it does. b. Follow the skill's steps against the fixture at the path specified in the task's workspace. c. Write the full skill output to eval/output-{task-name}.md. The task's instruction says to write output.md; override that filename to output-{task-name}.md so tasks don't clobber each other in the shared eval/ directory. d. Run all graders for the task:

   • deterministic graders: Run the run command with Bash from the eval/ directory. Replace output.md in the command with output-{task-name}.md.
   • llm_rubric graders: Read the rubric text and the output file (eval/output-{task-name}.md). Evaluate the output against the rubric yourself — score 1.0 if the output meets the rubric, 0.0 if not. Return a JSON object: {"score": 1.0, "details": "reason"}. e. Decide pass/fail using skillgrade's model, not a weighted-score cutoff. A trial passes only if every grader scores 1.0; the weighted score sum(score * weight) / sum(weight) is reported for visibility but is not the gate. This runner executes one trial, so the task's pass rate is 1.0 (all graders scored 1.0) or 0.0. The task passes only when that pass rate is greater than or equal to the threshold resolved in step 2 — i.e. only when every grader scored 1.0. (skillgrade computes the same way across trials; matching it here keeps local and CI verdicts aligned.)

4. After all tasks complete, print a summary table:

| Task | Score | Details |
|------|-------|---------|
| valid-project | 1.0 | deterministic(0.7): 1.0, llm_rubric(0.3): 1.0 |
| missing-agents | 1.0 | deterministic(1.0): 1.0 |
| ... | ... | ... |

5. Report the overall pass rate: {passed}/{total} tasks passed, and state the threshold each task was scored against.

Example commands

Evaluate the skill in the current directory, or point it at a specific skill directory:

$ /run-skill-eval
$ /run-skill-eval plugins/boss-experimental/boss-experimental/skills/claude-config-validation