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Scan prompt history for recurring patterns and unmet needs, then propose new skills or command templates
Passed 15 of 20 test cases with the skill, and 11 of 20 without it.
and 5 more in the full report
7 fixed10 passed either way3 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
What metric must be included to quantify time efficiency in the output report for each candidate?
| Checked | Without skill | With skill |
|---|---|---|
| Reports estimated time savings in minutes per week | Missed | Passed |
The instructions your model reads when the skill loads.
Reads your opencode prompt history, finds repeated multi-step workflows, and recommends skill-worthy candidates. Saves you from having the same conversation twice.
Use this skill when the user wants to mine opencode prompt history for repeated workflows, recurring unmet needs, or candidates for new reusable skills.
Run /skill skill-suggester to scan the full history. Optionally pass --since <date> (e.g. --since 2026-05-01) to limit the window.
~/.local/state/opencode/prompt-history*.jsonl## Skill Candidates (last N entries)
### 1. "<candidate name>" (PRIORITY)
- **Pattern**: <what you keep asking for>
- **Frequency**: X times in history
- **Avg complexity**: Y tool calls per instance
- **Estimated savings**: ~Z minutes/week
- **Evidence**:
- "[excerpt from prompt history]"
- "[another excerpt]"
- **Recommendation**: <create as skill | add as command template | not worth it>
test cases passed, vs 11 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.