Finding Skill Opportunities
Overview
A skill opportunity is a recurring, multi-step procedure that involves judgment and keeps getting repeated by hand. Git history and a codebase's existing automation are the evidence: the procedures a team performs over and over leave fingerprints — repeated commit types, files that always change together, release/migration keywords, half-written Makefile targets and runbook docs.
This skill scans those fingerprints deterministically (via the bundled scripts) so you don't eyeball thousands of commits, then applies judgment to turn the raw signals into a ranked list of skill candidates with evidence.
Core principle: Let the scripts find the repetition; you decide which repetitions are worth a skill.
When to Use
- "What skills should we write for this repo?"
- Auditing a codebase for automatable or skill-worthy workflows
- Onboarding to an unfamiliar repo and wanting to know its recurring rituals
- Before writing skills, to ground them in real evidence instead of guesses
Not for: writing the skill itself (use
superpowers:writing-skills
once you've picked a candidate) or one-off tasks with no repetition.
Workflow
Run the two scripts, then synthesize. Both are read-only and deterministic.
-
Mine git history for repetition signals:
bash
bash "${CLAUDE_SKILL_DIR}/scripts/git-signals.sh" --repo <path> --top 20
# optionally scope recent work: --since "1 year ago"
-
Inventory existing workflow encodings (these are the lowest-hanging fruit — the steps already exist):
bash
bash "${CLAUDE_SKILL_DIR}/scripts/scan-workflows.sh" --repo <path>
-
Cross-reference and synthesize. A signal is strong when it shows up in
both outputs — e.g. release keywords in commit subjects AND a
package.json script AND a
/manifest co-change cluster all describe one release procedure.
-
Rank candidates against the criteria below and write the report (see Output).
-
Hand each chosen candidate to superpowers:writing-skills
to author the actual SKILL.md. This skill finds opportunities; it does not write the skills.
Signal Interpretation
What each script section tends to mean:
| Signal (from ) | Likely skill opportunity |
|---|
| High-frequency commit prefix (e.g. many , ) | A routine procedure tied to that type |
| Recurring keywords (release, deploy, migrate, bump, regenerate, rotate, seed, backfill) | The named chore is done repeatedly by hand |
| Hot files (changed in many commits) | A repeated editing ritual centered on that file |
| Co-changed pairs (files that move together) | A "change A, remember to also change B" procedure — high-value, easy to forget steps |
| Busiest directories | Where the team's repeated work concentrates |
| Signal (from ) | Likely skill opportunity |
|---|
| Makefile targets / package.json scripts / task-runner recipes | Commands a skill can wrap with context and judgment |
| CI workflow jobs | Procedures currently only encoded for machines — a human/agent equivalent may be missing |
| and entries | Existing automation that a skill can orchestrate |
| "How to" / runbook doc sections | Procedural knowledge already written in prose, ready to become a skill |
| Existing skills | Do NOT propose these — gaps, not duplicates, are the goal |
What Makes a Good Skill Candidate
Rank each candidate by these. A strong candidate hits most of them:
- Recurring — happens repeatedly (the signals prove this), not once.
- Multi-step — enough steps that order and completeness matter.
- Error-prone / forgettable — co-change pairs and "don't forget to also…" rituals are gold.
- Involves judgment — if it's purely mechanical and a script already does it end-to-end, it may not need a skill (point to the script instead).
- Reusable / generalizable — applies across the project, not a single fix.
- Not already a skill — check the existing-skills section first.
Drop candidates that are: one-offs, already fully automated with no judgment, or purely project-trivia better left in a CLAUDE.md.
Output
Produce a ranked report, strongest first. For each candidate:
### <candidate skill name (verb-first, e.g. "cutting-a-release")>
Evidence: <which signals, with counts — e.g. "52 'release' subjects;
CHANGELOG.md+package.json co-change x62; package.json `release` script">
Procedure: <the repeated steps, as far as the evidence reveals them>
Why a skill: <which criteria it hits>
Next step: hand to superpowers:writing-skills
End with a short list of signals you considered and rejected, so the audit is auditable (e.g. "lockfile churn — mechanical, no skill needed").
Common Mistakes
- Proposing skills for fully-mechanical tasks. If a script already does it with no decisions, recommend the script, not a skill.
- Ignoring co-change pairs. They're the highest-signal section — they reveal the steps humans forget.
- Duplicating existing skills. Always read the existing-skills section of first.
- Trusting one signal. Confidence comes from corroboration across both scripts.
- Writing the skill here. This skill stops at a ranked candidate list; authoring is
superpowers:writing-skills
Scripts
- — deterministic git-history analysis (prefixes, keywords, hot files, co-change pairs, busy dirs). , , . Run for details.
scripts/scan-workflows.sh
Both default to the current directory and emit sorted, timestamp-free output, so reruns on unchanged history are byte-identical.
If a script can't run here (missing
/a tool, or a different OS): don't abandon the task — the scripts only automate ordinary git/text commands, so reproduce the same steps directly with whatever tools this environment has.