| name | recommend-evolution |
| description | Detect capability gaps and record standardized evolution recommendations. |
| version | 1.1.0 |
| model | sonnet |
| invoked_by | both |
| user_invocable | true |
| tools | ["Read","Write","Edit","Skill"] |
| error_handling | graceful |
| streaming | supported |
| verified | true |
| lastVerifiedAt | "2026-02-22T00:00:00.000Z" |
| source | builtin |
| trust_score | 100 |
| provenance_sha | 8865ccfce5be1e54 |
Recommend Evolution
Overview
Recommend ecosystem evolution when repeated evidence indicates missing capability, and record the recommendation in a standard machine-readable format.
When to Use
- Reflection identifies recurring delivery failures with the same root cause
- Router/analysis signals no suitable agent or skill for recurring requests
- Repeated integration gaps imply missing artifact type or policy
- User explicitly requests a new capability path
Iron Laws
- NEVER spawn evolution-orchestrator directly from this skill — this skill records recommendations only; execution decisions belong to the orchestrator and approval pipeline.
- ALWAYS validate trigger type against defined thresholds before recording a recommendation — vague observations are not triggers; require concrete failure counts or routing misses.
- NEVER create a new evolution request when artifact-integrator or skill-updater would address the gap — reserve evolution for net-new capabilities, not integration or update gaps.
- ALWAYS append the recommendation to the JSONL queue AND include the required report block in the current output — dual recording ensures the recommendation is discoverable at both runtime and review time.
- NEVER proceed with a recommendation without evidence — single failures are noise; trigger thresholds exist for a reason.
Evolution recommendation skill for reflection/planning agents.
- Trigger classification (`repeated_error`, `no_agent`, `integration_gap`, `user_request`, `rubric_regression`, `stale_skill`, `other`)
- Recommendation-vs-integration decision branching
- Dual recording mode: JSONL runtime queue + reflection report block
Trigger Taxonomy Note
recommend-evolution uses a cause-oriented trigger taxonomy (repeated_error, no_agent, integration_gap, user_request, rubric_regression, stale_skill, other).
This intentionally differs from skill-updater, which uses a caller-oriented trigger taxonomy (reflection, evolve, manual, stale_skill) to describe who/what initiated the update path.
Step 0: Validate Trigger Type
Use these thresholds:
repeated_error: same class of failure in 5+ tasks
**Example Invocations**: