task-feedback-analyser

Extracts recurring patterns from task feedback into generalizable workflow rules.

Updated Apr 17, 2026
One-click install
npx skills add https://github.com/mistakenot/skills --skill task-feedback-analyser-mistakenot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: task-feedback-analyser
Source: https://github.com/mistakenot/skills/tree/main/plugins/planning-workflow/skills/task-feedback-analyser
Command: npx skills add https://github.com/mistakenot/skills --skill task-feedback-analyser-mistakenot

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Completed task feedback and PR review threads accumulate valuable lessons, but they stay scattered across files and are never turned into actionable guidance. This Skill mines that history and converts repeated observations into durable workflow rules. ## Core Features & Use Cases - Feedback Scanning: Reads all docs/tasks/*/feedback.md files and merged PR review threads, tracking processed files to avoid re-scanning. - Theme Clustering with a Strict Threshold: Groups observations by theme and only promotes a theme to a rule when it appears in 3 or more independent task examples; weaker themes are reported but dropped. - Rule Authoring and Maintenance: Drafts rules in imperative form with use_when triggers and verbatim evidence, then merges them into docs/rules.md with sequential IDs and commits the result. - Use Case: After completing a dozen tasks, run the analysis to discover that missing tenant isolation appeared in four separate reviews, and codify it as a rule so future planning catches it. ## Quick Start Ask the agent to analyse feedback across completed tasks and extract new rules into docs/rules.md.

Frequently Asked Questions about task-feedback-analyser

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract rules from task feedback?▼

Run the feedback analysis to scan all docs/tasks/*/feedback.md files and merged PR review threads. Observations are clustered by theme, and themes with 3 or more independent examples are drafted as rules and merged into docs/rules.md.

How many examples are needed before feedback becomes a rule?▼

A theme must appear in at least 3 independent examples from different task folders to qualify as a rule. Themes below this threshold are reported but dropped, and examples are never fabricated to meet the bar.

Does the feedback analysis avoid re-scanning old files?▼

Yes. The process checks existing rules in docs/rules.md for Evidence task references to determine which feedback files were already processed, so subsequent runs only analyse new material.

What format do the generated workflow rules use?▼

Each rule follows a template with a sequential RULE-$NNN ID, an imperative statement, a use_when trigger condition, and verbatim evidence excerpts quoted directly from feedback or review threads, never paraphrased.

What are the limitations of automated feedback pattern analysis?▼

The analysis only surfaces patterns that recur across at least three tasks, so rare but serious one-off issues may be missed. It also depends on teams consistently writing feedback.md files for completed tasks.