learn-from-feedback — for Claude Code learn-from-feedback, duklog, community, for Claude Code, ide skills, gh pr view $ARGUMENTS --comments, reflect, CLAUDE.md, MEMORY.md, Feedback

v1.0.0
GitHub

À propos de ce Skill

Scenario recommande : Ideal for AI agents that need learn from feedback. Resume localise : learn-from-feedback helps AI agents handle repository-specific developer workflows with documented implementation details.

Fonctionnalités

Learn from Feedback
Identify the source :
User feedback : read PR comments (gh pr view $ARGUMENTS --comments) or parse the correction from
Self-observation : findings from the reflect skill, or patterns noticed during implementation
Classify the learning :

# Core Topics

for Claude Code gh pr view $ARGUMENTS --comments reflect CLAUDE.md MEMORY.md Feedback
rubberduck203 rubberduck203
[3]
[0]
Updated: 3/13/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 8/11

This page remains useful for teams, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
8/11
Quality Score
30
Canonical Locale
en
Detected Body Locale
en

Scenario recommande : Ideal for AI agents that need learn from feedback. Resume localise : learn-from-feedback helps AI agents handle repository-specific developer workflows with documented implementation details.

Pourquoi utiliser cette compétence

Recommandation : learn-from-feedback helps agents learn from feedback. learn-from-feedback helps AI agents handle repository-specific developer workflows with documented implementation details.

Meilleur pour

Scenario recommande : Ideal for AI agents that need learn from feedback.

↓ Cas d'utilisation exploitables for learn-from-feedback

Cas d'usage : Applying Learn from Feedback
Cas d'usage : Applying Identify the source :
Cas d'usage : Applying User feedback : read PR comments (gh pr view $ARGUMENTS --comments) or parse the correction from

! Sécurité et Limitations

  • Limitation : Be conservative: only save verified, stable patterns — not one-off corrections
  • Limitation : Don't duplicate: check if the learning already exists before adding
  • Limitation : Requires repository-specific context from the skill documentation

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Install Path

Start With Installation And Validation

If this skill is worth continuing with, the next step is to confirm the install command, CLI write path, and environment validation.
Trusted Recheck

Cross-Check Against Trusted Picks

If you are still comparing multiple skills or vendors, go back to the trusted collection before amplifying repository noise.
Workflow

Move To Workflow Collections For Team Rollout

When the goal shifts from a single skill to team workflows, approvals, and repeatable execution, move into workflow collections.
Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is learn-from-feedback?

Scenario recommande : Ideal for AI agents that need learn from feedback. Resume localise : learn-from-feedback helps AI agents handle repository-specific developer workflows with documented implementation details.

How do I install learn-from-feedback?

Run the command: npx killer-skills add rubberduck203/duklog/learn-from-feedback. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for learn-from-feedback?

Key use cases include: Cas d'usage : Applying Learn from Feedback, Cas d'usage : Applying Identify the source :, Cas d'usage : Applying User feedback : read PR comments (gh pr view $ARGUMENTS --comments) or parse the correction from.

Which IDEs are compatible with learn-from-feedback?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for learn-from-feedback?

Limitation : Be conservative: only save verified, stable patterns — not one-off corrections. Limitation : Don't duplicate: check if the learning already exists before adding. Limitation : Requires repository-specific context from the skill documentation.

↓ How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add rubberduck203/duklog/learn-from-feedback. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use learn-from-feedback immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

learn-from-feedback

Install learn-from-feedback, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Learn from Feedback

When receiving feedback (PR comments, user corrections, code review findings) or observing patterns yourself during implementation, update the project's knowledge base so the same issues don't recur.

Process

  1. Identify the source:
    • User feedback: read PR comments (gh pr view $ARGUMENTS --comments) or parse the correction from context
    • Self-observation: findings from the reflect skill, or patterns noticed during implementation
  2. Classify the learning:
    • Coding standard → update .claude/skills/coding-standards/SKILL.md
    • Domain knowledge → update .claude/rules/domain.md
    • Testing practice → update .claude/rules/testing.md
    • Workflow/process → update CLAUDE.md (Git Workflow section) or relevant skill
    • Project-specific pattern → update auto memory (MEMORY.md)
    • Missed refactoring → add to the "When Touching a File" checklist in .claude/rules/testing.md (test helpers) or .claude/skills/coding-standards/SKILL.md (code patterns) so the pattern fires proactively next time
  3. Apply the fix: If the feedback points to a code issue, fix it
  4. Update the knowledge base: Write the learning to the appropriate file so it persists
  5. Summarize: Report what was learned and where it was saved

Rules

  • Be conservative: only save verified, stable patterns — not one-off corrections
  • Keep entries concise: one bullet point per learning
  • Don't duplicate: check if the learning already exists before adding
  • If unsure where a learning belongs, prefer auto memory (lowest commitment)

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