| name | synthesize-learnings |
| description | Convert raw plugin analysis results into actionable improvement recommendations for agent-scaffolders and agent-skill-open-specifications. Trigger with "synthesize learnings", "generate improvement recommendations", "what should we improve in our scaffolders", "update our meta-skills based on these findings", or after completing a plugin analysis.
|
| allowed-tools | Bash, Read, Write |
Synthesize Learnings
Take raw analysis output from analyze-plugin and transform it into concrete, actionable improvements for our meta-skills ecosystem. This is the "close the loop" skill that turns observations into evolution.
Improvement Targets
Learnings are mapped to three improvement targets:
Target 1: agent-scaffolders
Improvements to the plugin/skill/hook/sub-agent scaffolding tools.
What to look for:
- New component types or patterns that
scaffold.py should support
- Better default templates based on exemplary plugins
- New scaffolder skills needed (e.g., creating connectors, reference files)
- Improved acceptance criteria templates based on real-world examples
Target 2: agent-skill-open-specifications
Improvements to ecosystem standards and authoritative source documentation.
What to look for:
- New best practices discovered from high-quality plugins
- Anti-patterns that should be documented as warnings
- Spec gaps where plugins do things the standards don't address
- New pattern categories to add to ecosystem knowledge
Target 3: agent-plugin-analyzer (Self-Improvement)
Improvements to this analyzer plugin itself.
What to look for:
- New patterns discovered that should be added to
pattern-catalog.md
- Analysis blind spots — things that should have been caught
- Framework gaps — phases that need refinement
- New anti-patterns to add to the detection checklist
Target 4: Domain Plugins (e.g., legacy system)
Improvements to the primary domain plugins in this repository — especially the legacy Oracle Forms/DB analysis plugins.
What to look for:
- Severity/classification frameworks that could improve how legacy code issues are categorized (e.g., GREEN/YELLOW/RED deviation severity from legal contract-review)
- Playbook-based review methodology adaptable to legacy code review playbooks (standard migration positions, acceptable risk levels)
- Confidence scoring applicable to legacy code analysis certainty levels
- Connector abstractions (
~~category patterns) for tool-agnostic Oracle analysis workflows
- Progressive disclosure structures for organizing deep Oracle Forms/DB reference knowledge
- Decision tables for legacy migration pathways (like chart selection guides but for migration strategies)
- Checklist patterns for legacy system audit completeness
- Tiered execution strategies for handling different legacy code complexity levels
- Bootstrap/iteration modes for incremental legacy system analysis
- Output templates (HTML artifacts, structured reports) for presenting legacy analysis results
Synthesis Process
Step 1: Gather Analysis Results
Collect all analysis reports from the current session or from referenced analysis artifacts.
Step 2: Categorize Observations
Sort every observation into one of these categories:
| Category | Description | Maps To |
|---|
| Structural Innovation | Novel directory layouts, component organization | Scaffolders |
| Content Pattern | Reusable content structures (tables, frameworks, checklists) | Specs + Catalog + Domain |
| Execution Pattern | Workflow designs, phase structures, decision trees | Scaffolders + Specs + Domain |
| Integration Pattern | MCP tool usage, connector abstractions, cross-tool design | Specs + Domain |
| Quality Pattern | Testing, validation, compliance approaches | Scaffolders + Specs |
| Meta Pattern | Self-referential or recursive designs (skills that build skills) | Analyzer + Scaffolders |
| Anti-Pattern | Things to avoid, documented pitfalls | Specs |
| Domain Applicability | Patterns transferable to legacy code analysis workflows | Domain |
| Novel Discovery | Something entirely new not in existing catalogs | All targets |
Step 3: Generate Recommendations
For EACH observation, produce a structured recommendation:
### [Recommendation Title]
**Source**: [Plugin/skill where observed]
**Category**: [from table above]
**Target**: [which meta-skill to improve]
**Priority**: [high / medium / low]
**Observation**: [What was found]
**Current State**: [How our meta-skills handle this today, or "not addressed"]
**Proposed Improvement**: [Specific change to make]
**Example**: [Before/after or concrete illustration]
Step 4: Prioritize
Rank recommendations by impact:
| Priority | Criteria |
|---|
| High | Universal pattern found across many plugins; would improve ALL generated plugins; addresses a gap in current standards |
| Medium | Common pattern found in several plugins; would improve most generated plugins; refines existing standards |
| Low | Niche pattern from specific domain; would improve specialized plugins; nice-to-have enhancement |
Step 5: Update the Pattern Catalog
Append any newly discovered patterns to references/pattern-catalog.md in the analyze-plugin skill. This is the self-improvement loop — every analysis makes future analyses better.
Format new catalog entries as:
### [Pattern Name]
- **Category**: [Structural / Content / Execution / Integration / Quality / Meta]
- **First Seen In**: [plugin name]
- **Description**: [2-3 sentences]
- **When to Use**: [trigger conditions]
- **Example**: [brief illustration]
Step 6: Generate Summary Report
Produce a final synthesis report with:
- Executive Summary — 3-5 bullet points of the highest-impact learnings
- Recommendations by Target — Grouped by scaffolders / specs / analyzer
- Updated Pattern Count — How many new patterns were added to the catalog
- Virtuous Cycle Status — What percentage of the analysis framework was exercised and how it can be tightened
Output
The synthesis report should be a standalone markdown document suitable for:
- Filing as a reference artifact
- Using as a briefing for planning sessions
- Driving specific PRs against the scaffolders and specs
Iteration Directory Isolation: Do NOT overwrite existing synthesis reports. Always output to a newly isolated directory (e.g. synthesis-reports/run-1/) so historical recommendations are preserved.
Asynchronous Benchmark Metric Capture: Log the total_tokens and duration_ms consumed during the synthesis back to timing.json to track the ROI cost of this meta-analysis.
Always close with a Next Steps section listing the 3 most impactful changes to make first.
