exploration-optimizer

User wants to evaluate and improve a specific exploration skill. User: Evaluate and improve the exploration-session-brief skill using the optimization loop. Agent: [invokes exploration-optimizer, runs baseline-first iteration on exploration-session-brief] User notices a skill feels weak and wants a systematic improvement cycle. User: The exploration cycle feels slow — help me identify which skill to optimize first. Agent: [invokes exploration-optimizer, runs discovery phase to identify highest-impact target] BRD generation routes to business-requirements-capture, not this skill. User: Generate a BRD from our session captures. Agent: [invokes business-requirements-capture, NOT exploration-optimizer]

Exploration Optimizer

See acceptance criteria

Discovery Phase

Ask for:

• The target exploration skill or agent to optimize.
• The eval set to use, or whether to generate one from the current architecture.
• The iteration budget.
• Whether auto-apply of winning variants is allowed.
• Which metrics matter most for this loop: routing quality, artifact usefulness, handoff stability, re-entry quality, or human intervention burden.
• Whether post-run survey data exists and should be included in the decision.

Recap

Confirm:

• target component
• eval source
• loop budget
• chosen scoring dimensions
• whether survey data is available
• whether auto-apply is enabled

Execution

This skill implements autoresearch-style optimization for the exploration-cycle system. It uses a baseline-first iteration loop to improve skill prompts and logic.

Usage:

python3 ./scripts/execute.py \
  --target ${plugins}/skills/user-story-capture/SKILL.md \
  --eval-script ./scripts/eval_runner.py \
  --goal "Improve Gherkin block accuracy" \
  --iterations 3

For a concrete target-specific playbook, use references/spec-kitty-skill-optimizer-program.md when optimizing the Spec-Kitty agent/workflow files themselves.

Iteration Loop

The execute.py script follows a disciplined loop:

1. Change one dominant variable per iteration.
2. Re-run evaluations.
3. Mark the attempt as keep or discard.
4. If the run crashes or times out, log the failure and continue from the last known good state.
5. Never let a subjective preference override a clear regression in the tracked metrics.
6. Use survey feedback as a quality signal, not an excuse to ignore the baseline-first method.

Suggested Metrics

• routing quality
• artifact usefulness
• handoff stability
• re-entry usefulness
• human intervention burden
• unnecessary agent invocation rate
• post-run survey composite score

Output

Always conclude execution with a Source Transparency Declaration explicitly listing what was queried to guarantee user trust: Sources Checked: [list] Sources Unavailable: [list]

Next Actions

• Use ./scripts/benchmarking/run_loop.py --results-dir evals/experiments for repeatable improvement loops.
• Suggest the user run audit-plugin to verify the generated artifacts.