Issue Root Resolution logo

Issue Root Resolution

OrganizationPopular
Gentleman-Programming
issue-root-resolution

Trigger: root audit, atacar la raíz, issue roots, backlog roots, mechanism map, deletion-driven fix, resolver issues de raíz, close outdated issues. Audit and resolve issue clusters by verified root cause.

Overview

PublisherGentleman-Programming
Repositorygentle-ai
Skill nameissue-root-resolution
Stars
7K
Forks
760
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Gentleman-Programming on GitHub. Read the source before you install it.

Installation

Install the Issue Root Resolution AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Gentleman-Programming/gentle-ai.git /tmp/gentle-ai
mkdir -p .claude/skills
cp -r /tmp/gentle-ai/skills/issue-root-resolution .claude/skills/issue-root-resolution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Issue Root Resolution in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Issue Root Resolution on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Issue Root Resolution is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Activation Contract

Load when auditing a defect backlog for shared root causes, proposing a fix for an issue cluster, or closing issues as resolved/outdated. Complements rdd-defect-workflow (single-defect flow) — this skill governs the cluster-level method.

Hard Rules

  • Read full bodies and comments, never titles. Classify against the actual code or PR diff, not descriptions of it.
  • Verify every "fixed" or "broken" claim against current origin/main the day you act. Compare report dates to fix merge dates: a repro filed before the fix landed is evidence about old builds, not current code.
  • Cluster defects by causal root, not surface. Each root gets a measured row in the meta-issue (#2471 style): issues attached, fix shape, state.
  • Before proposing any fix, produce a read-only mechanism map: one file:line anchor per claim, mechanism explained as implemented today. Report claim-vs-code mismatches explicitly; never force evidence to fit the hypothesis. Let the map shrink the proposal.
  • Rank solutions by what they DELETE: (1) removes a mechanism so the class becomes impossible, (2) static guard-ratchet making reintroduction a test failure, (3) localized predicate fix behind a failing repro test. New surface (verbs, flags, mechanisms) is last resort — defer until re-verified evidence demands it.
  • Extract maintainer choices as named D-items with recommended defaults. A maintainer condition recorded in an issue thread outranks any plan table, including yours.
  • Close only with evidence, one rule per closure: (A) fixed on main, cite commit AND proving test; (B) superseded by recorded maintainer decision; (C) surface no longer exists; (D) duplicate of a fixed issue. Comment before closing: what resolved it, verified today, reopen invitation. When in doubt, do not close — list as borderline.
  • Clean break, never compatibility. When a format or identity changes, bump its versioned tag as a REPLACEMENT: old records become outdated and fail closed with an actionable refusal naming the rerun. Never write dual recognition, legacy fallbacks, or compat shims; that residue is what the deletion criterion exists to prevent. Stored bytes are never rewritten and stay readable for forensics, but carry no gate or lifecycle validity.
  • Implementation follows the waves recipe: repro as failing test first, byte-stable goldens as defect signal, independently revertible slices, one writer per slice.
  • A universal guard's verification matrix is go test ./... at the repository root, never a curated package list. A guard that forbids a shape breaks every fixture that relied on it, including in packages the change never touched.
  • One worktree per writer, always, including the orchestrator. Before editing inline, check whether a delegated worker holds that path; if so, create a separate worktree rather than reusing it.
  • Audit every worker report yourself: re-run its key verification, decoy-test any new guard, spot-check its diffs. A self-report is a claim, not evidence.

Decision Gates

ConditionAction
Issue's failure mechanism absent from current mainRe-verify on latest build; close by rule A/C or mark stale — never fix ghost code.
Fix would add a mechanism, flag, or verbDefer with written reason; re-rank for a deletion-shaped alternative first.
Map contradicts the issue or your hypothesisThe map wins. Revise the plan and correct the public record (meta-issue) before coding.
Blocker's fix lives in an unmerged PRLeave open; it closes on merge or by its own thread condition.
Closure evidence incompleteBorderline list, not closure.

Execution Steps

  1. Measure: read the cluster fully; partition by root; record counts in the meta-issue.
  2. Classify each issue against the real diff/code; separate closes-via-X, improved-not-closed, unrelated, blocker.
  3. Map mechanisms read-only with anchors; flag mismatches.
  4. Rank fixes deletion-first; name D-items; get maintainer answers.
  5. Implement in slices (issue-first: every PR links a status:approved issue), audit each worker report.
  6. Hygiene pass: evidence-gated closures, stale-repro re-verification requests, meta-issue update with what changed and why.

Output Contract

Per pass, report: roots table (issues, fix shape, state), closures with rule+evidence, borderline list with reasons, D-items and their answers, mechanism-map mismatches found, and the meta-issue comment link.

References

  • ../rdd-advisory-transport/references/shared-advisory-transport-proposal.md — exemplar proposal shape produced by this method.
  • ../rdd-advisory-transport/references/issue-impact-matrix.md — exemplar per-issue disposition matrix.

Frequently asked questions

What does the Issue Root Resolution AI skill do?

Trigger: root audit, atacar la raíz, issue roots, backlog roots, mechanism map, deletion-driven fix, resolver issues de raíz, close outdated issues. Audit and resolve issue clusters by verified root cause.

Why use Issue Root Resolution on TypingMind?

Because you install it once and use it with any model. Issue Root Resolution is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Issue Root Resolution in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Gentleman-Programming/gentle-ai/tree/main/skills/issue-root-resolution. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Issue Root Resolution?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Issue Root Resolution?

As many as you like. As long as a model supports skills, you can use Issue Root Resolution with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Issue Root Resolution AI skill free?

Yes. It is published on GitHub by Gentleman-Programming under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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