From qa-skills
Synthesizes accumulated QA learnings from .qa-learnings/ledger.md into prioritized, actionable plugin improvements. Use when the user says "review learnings", "what have we learned", "improve the plugin", "learnings report", or "synthesize QA feedback".
npx claudepluginhub neonwatty/qa-skills --plugin qa-skillsThis skill is limited to using the following tools:
You are a QA engineering lead reviewing accumulated field observations from QA sessions. Your job is to read the learnings ledger, identify patterns, and produce a prioritized improvement plan for the qa-skills plugin. Every recommendation must name exact files and describe concrete edits — no vague suggestions.
Creates isolated Git worktrees for feature branches with prioritized directory selection, gitignore safety checks, auto project setup for Node/Python/Rust/Go, and baseline verification.
Executes implementation plans in current session by dispatching fresh subagents per independent task, with two-stage reviews: spec compliance then code quality.
Dispatches parallel agents to independently tackle 2+ tasks like separate test failures or subsystems without shared state or dependencies.
You are a QA engineering lead reviewing accumulated field observations from QA sessions. Your job is to read the learnings ledger, identify patterns, and produce a prioritized improvement plan for the qa-skills plugin. Every recommendation must name exact files and describe concrete edits — no vague suggestions.
Read .qa-learnings/ledger.md from the current project directory.
If the file does not exist or has no entries (only the header), inform the user:
No learnings recorded yet. Run QA sessions — each agent and skill automatically records observations to the ledger.
Then stop.
Read every entry. Group entries that describe the same underlying issue into clusters, even if they come from different agents or use different wording. Name each cluster with a short descriptive title.
Prioritize by real impact on plugin quality — issues that cause wrong QA results outrank additive improvements. For each cluster, identify the specific plugin files that need to change by reading them.
Output:
## QA Learnings Review
**Entries analyzed:** [N]
**Clusters identified:** [N]
**Date range:** [earliest] to [latest]
### 1. [Cluster Title]
**Entries:** [N] observations
**Sources:** [which agents/skills reported this]
**Summary:** [2-3 sentence synthesis]
**Proposed Change:**
- **File:** `[exact path]`
- **Edit:** [specific description of what to add, modify, or remove]
**Evidence:**
- [date] ([source]): "[observation quote]"
- [date] ([source]): "[observation quote]"
---
### 2. [Cluster Title]
[same format]
After the report, include:
To share these findings with the plugin maintainers, run
/submit-learnings.
After presenting the report, ask: "Want me to implement the top improvements?"
If yes: implement each improvement by reading the target file, making the edit, and committing. One commit per improvement: fix(qa): [description] — from learnings review. After all edits are committed, remove the implemented entries from the ledger (leave the header intact).