Trigger Evaluator

Trigger Evaluator Skill

Evaluates how reliably a skill's description field causes agent CLIs to activate the skill for relevant queries. Runs each test query multiple times to account for model nondeterminism and records per-run trigger state.

Workflow

Step 1: Load Test Queries

Read <skill-dir>/evals/evals.json. This file contains an array of test queries with the schema:

[
  {"id": "q1", "query": "write an ADR for auth", "should_trigger": true},
  {"id": "q2", "query": "add a comment to the login function", "should_trigger": false}
]

should_trigger: true means the skill description SHOULD cause the CLI to activate this skill. should_trigger: false means the skill should NOT activate.

Step 2: Run Trigger Tests

For each query, invoke the target CLI 3 times per CLI target (opencode and gemini):

go run ./cmd/invoke-cli \
  --cli <opencode|gemini> \
  --prompt "<query>" \
  --workspace <workspace> \
  --skill <skill-path>

Use a 120-second timeout per invocation. The invoke-cli tool outputs JSON with a skill_activated boolean field.

Step 3: Record Results

For each CLI target, produce a result file at evals/workspace/trigger-results/<cli>.json:

{
  "cli": "opencode",
  "results": [
    {
      "id": "q1",
      "query": "write an ADR for auth",
      "should_trigger": true,
      "runs": [true, false, true],
      "trigger_count": 2,
      "total_runs": 3
    },
    {
      "id": "q2",
      "query": "add a comment to the login function",
      "should_trigger": false,
      "runs": [false, false, false],
      "trigger_count": 0,
      "total_runs": 3
    }
  ]
}

Step 4: Handle Failures

If a CLI invocation times out (exceeds 120s), record that run as null and reduce total_runs accordingly. If all 3 runs for a query time out, flag the query as status: "unavailable" and exclude it from downstream aggregation with a note in the output.

If invoke-cli crashes or returns malformed JSON, retry once. If the retry also fails, mark all remaining queries for that CLI as status: "cli_error" and write a partial results file.

Gotchas

• opencode may not invoke skills for single-step tasks: opencode's router can skip skill activation for queries it considers trivial or single-action. A query like "list files" (should_trigger: false for most skills) may silently bypass the skill system — this is expected behavior, not a test failure.
• gemini stream-json mode buffers differently: The gemini CLI emits skill activation events in stream-json mode, which can buffer lines. Ensure invoke-cli reads the full stream before parsing, or activation events may be missed.
• Model nondeterminism requires multiple runs: A single run can produce a false positive or false negative due to model variability. Three runs is the minimum; for high-stakes evaluations, consider 5 runs and use majority vote.
• Prompt length affects activation: Very long prompts (over 2000 tokens) can dilute the skill description's influence on routing. Keep test queries concise and focused.
• Description caching: Some CLI implementations cache skill descriptions at startup. If you modify the description between test runs, restart the CLI or clear its cache.
• Skill directory must be discoverable: The --skill path must be registered or resolvable by the CLI's skill discovery mechanism. If the skill is not found, skill_activated will always be false regardless of query relevance.

Verification

Verify this skill produces correct output:

1. Create a fixture skill with a description that mentions "kubernetes deployment".
2. Create an evals.json with two queries: one about "deploy to k8s" (should_trigger: true) and one about "sort an array" (should_trigger: false).
3. Run the trigger evaluator against both CLI targets.
4. Confirm evals/workspace/trigger-results/opencode.json and gemini.json exist.
5. Confirm that trigger_count is a number between 0 and 3 (or fewer if timeouts occurred) for each query.
6. Confirm that timed-out runs produce null entries in the runs array, not false.