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

1. NEVER spawn evolution-orchestrator directly from this skill — this skill records recommendations only; execution decisions belong to the orchestrator and approval pipeline.
2. ALWAYS validate trigger type against defined thresholds before recording a recommendation — vague observations are not triggers; require concrete failure counts or routing misses.
3. 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.
4. 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.
5. NEVER proceed with a recommendation without evidence — single failures are noise; trigger thresholds exist for a reason.

<identity> Evolution recommendation skill for reflection/planning agents. </identity> <capabilities> - 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 </capabilities>

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.

<instructions> <execution_process>

Step 0: Validate Trigger Type

Use these thresholds:

• repeated_error: same class of failure in 5+ tasks
• rubric_regression: repeated score drop below threshold for same class of task
• no_agent: recurring need with no valid routing match
• integration_gap: existing artifact integration missing (prefer artifact-integrator)
• user_request: explicit request for capability not available
• stale_skill: audit pipeline reports verified artifact older than 6 months or invalid lastVerifiedAt

Step 1: Decide Recommendation Path

• If gap is integration of existing artifact, prefer: Skill({ skill: 'artifact-integrator' })
• If gap is stale/underperforming existing skill, prefer: Skill({ skill: 'skill-updater' })
• If gap requires net-new capability/artifact, continue with evolution recommendation
• If no artifact change needed, update memory only and exit

Step 2: Create Standard Recommendation Payload

Build one JSON object with required fields:

{
  "timestamp": "2026-02-14T00:00:00.000Z",
  "source": "reflection-agent",
  "trigger": "repeated_error",
  "evidence": "Same routing failure observed in 6 tasks over 2 days.",
  "suggestedArtifactType": "skill",
  "summary": "Create a new routing-context skill for reflection-time grounding.",
  "status": "proposed"
}

Schema reference: .claude/schemas/evolution-request.schema.json

Step 3: Record Recommendation

1. Append JSON line to: .claude/context/runtime/evolution-requests.jsonl
2. Add required report block:

## Evolution Recommendation

- Trigger: <trigger>
- Evidence: <evidence>
- Suggested Artifact Type: <type|null>
- Summary: <1-2 sentences>
- Queue Record: `.claude/context/runtime/evolution-requests.jsonl`

Step 3: Output

Return recommendation summary and what was recorded.

</execution_process> </instructions>

<examples> <usage_example> **Example Invocations**:

// Repeated failure pattern -> recommend skill creation
Skill({
  skill: 'recommend-evolution',
  args: '--trigger repeated_error --suggestedArtifactType skill',
});

// Routing miss -> recommend new agent/workflow discussion
Skill({ skill: 'recommend-evolution', args: '--trigger no_agent --suggestedArtifactType agent' });

</usage_example> </examples>

Anti-Patterns

Anti-Pattern	Why It Fails	Correct Approach
Spawning evolution-orchestrator directly from this skill	Violates single-responsibility; bypasses approval and resource gates	Record recommendation to JSONL queue only; let the orchestrator decide on execution
Recording an evolution request for an integration gap that already has artifacts	Creates unnecessary new artifacts when an integration fix would suffice	Check artifact-integrator path first; escalate only if gap requires net-new capability
Submitting a recommendation without trigger evidence	Uninformed evolution wastes resources and pollutes the queue with noise	Require concrete evidence: failure counts, routing miss logs, or explicit user request
Routing stale-skill triggers through this skill instead of skill-updater	Wrong escalation path; creates evolution requests for work that belongs in an update cycle	Route stale_skill triggers directly to skill-updater; only escalate if the skill cannot be updated
Triggering evolution after a single failure instance	Single failures are noise; premature evolution wastes build capacity	Apply defined thresholds: 5+ repeated errors, consistent routing misses across sessions

Memory Protocol (MANDATORY)

Before starting:

Read .claude/context/memory/learnings.md using Read or Node fs.readFileSync (cross-platform).

After completing:

• Recommendation pattern -> .claude/context/memory/learnings.md
• Ambiguous trigger logic -> .claude/context/memory/issues.md
• Evolution policy decision -> .claude/context/memory/decisions.md

ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.