Convert raw plugin analysis results into actionable improvement recommendations for agent-scaffolders and agent-scaffolders. 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.
Scanned 9/2/2026
Install to Claude Code
npx -y skills add richfrem/agent-plugins-skills --skill synthesize-learnings --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: synthesize-learnings
plugin: agent-scaffolders
description: >
Convert raw plugin analysis results into actionable improvement recommendations for agent-scaffolders
and agent-scaffolders. 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
---
## Dependencies
This skill requires **Python 3.8+** and standard library only. No external packages needed.
**To install this skill's dependencies:**
```bash
pip-compile ./requirements.in
pip install -r ./requirements.txt
```
See `./requirements.txt` for the dependency lockfile (currently empty — standard library only).
---
# 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-scaffolders`
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-scaffolders` (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., `oracle-legacy-system-analysis`)
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:
```markdown
### [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.
### Step 5b: Log Recommendations to Tracker
Append each recommendation to `references/open-recommendations.md` using this format:
```markdown
| [YYYY-MM-DD] | [Title] | [Target] | [Priority] | open |
```
See `references/open-recommendations.md` for the tracker schema. When a recommendation is
implemented, update its status from `open` to `implemented` and add the PR or commit reference.
Format new catalog entries as:
```markdown
### [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:
1. **Executive Summary** — 3-5 bullet points of the highest-impact learnings
2. **Recommendations by Target** — Grouped by scaffolders / specs / analyzer
3. **Updated Pattern Count** — How many new patterns were added to the catalog
4. **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.
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