Systemic Issue Triage logo

Systemic Issue Triage

OrganizationPopular
Gentleman-Programming
systemic-issue-triage

Trigger: new issue, bug report, triage, backlog, issue flood, community report, root cause, dead-end, blocked user. Attack issues by root class, never one-by-one; fixes must shrink the system, not grow it.

Overview

PublisherGentleman-Programming
Repositorygentle-ai
Skill namesystemic-issue-triage
Stars
7K
Forks
760
Bundled files
Instructions only
LicenseApache-2.0
Links
  • 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 Systemic Issue Triage 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/internal/assets/skills/systemic-issue-triage .claude/skills/systemic-issue-triage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Systemic Issue Triage 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 Systemic Issue Triage 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 Systemic Issue Triage 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.

Systemic Issue Triage

Activation Contract

Use when triaging incoming issues, planning fixes for a backlog, or deciding how to respond to any bug report. The failure mode this skill exists to prevent: fixing symptoms one-by-one until the system becomes over-engineered machinery that grows exponentially and nobody can maintain.

For a full-backlog audit REPORT (dispositions across every open issue/PR), use backlog-triage; this skill governs how the resulting fixes are designed, batched, and closed.

Hard Rules

  • Classify by ROOT CLASS before touching code. Every issue lands in exactly one bucket: (A) superseded by an in-flight design change — name the change and the test that proves it; (B) duplicate of a known class — name the canonical tracker; (C) real new bug — assign to a root-cause CLUSTER, never a standalone fix; (D) feature request; (E) unclear — ask the reporter, don't guess.
  • Two or more issues sharing a root = ONE fix at the root, closing all of them with named test evidence. N issues never justify N patches.
  • Issues close against NAMED TESTS (supersession evidence), never against promises or "should be fixed now".
  • The over-engineering test before ANY fix: does it add a state, a verb, a config flag, a gate, or a parallel representation of existing truth? If yes, redesign — the correct fix usually DELETES or RELAXES something (13 states became 5; a dead-end became a one-line exit message).
  • A block whose fix is "name the executable exit in the message" is a MESSAGE fix — do not build machinery around it. Every refusal must name a runnable continuation; verify runnability by executing the printed command, not by assuming.
  • Blocking budget: humans may be blocked only at genuine consent and terminal decisions. Any fix that introduces a new human block or a state without a self-service exit is rejected at review, not shipped.
  • If a fix crosses a systemic-cluster boundary another issue already tracks, STOP and write one unified design for the cluster (owner, error precedence, migration table per call site) before any PR. Duplicating an approved sibling issue's scope is a defect.
  • Self-reported "fixed" is a claim: reproduce the original report's exact scenario on a fresh binary before closing.
  • Retiring a command or verb: grep its user-facing STRING repo-wide, not just its symbol call graph. In a system where every refusal names an executable exit, deleting a verb orphans every refusal, help text, and sanctioned-exit list that names it — the user is then told to run a command that no longer exists. Recurse: each caller's callers, and any test file whose helpers other retained tests reuse. Expect a design-time inventory to undercount real scope by 2-5x; report the delta rather than trusting the citation.
  • A deletion slice's deadcode ratchet may legitimately go net-POSITIVE when the consumer dies before its provider (consumer-first ordering). Report the true number and name the slice that absorbs it; forcing artificial negativity hides the transition.
  • An issue's stated MECHANISM is a hypothesis; only its symptom is evidence. Reproduce before implementing. A correct conclusion routinely names the wrong line, one surface away — a test written against the stated mechanism that PASSES on unmodified main means the report is right and the diagnosis is not. Trust your reproduction over the issue text and say so.
  • Broken in production but green in tests = suspect the test was TAUGHT to agree. Check whether the commit that introduced the defect also taught the corpus, a test helper, or a fixture to satisfy it; that removes the broken shape from the test surface at the moment it becomes broken. Also check for a test row asserting the defect as intended behavior. Remove it rather than leaving a test that pins a bug.
  • Before deriving any plan from a contract document, identify which copy CONSUMERS receive. A docs mirror and a shipped asset drift, and the guard enforcing meaning may point at the mirror while the guard pointing at the shipped copy only counts rows. Verify the row TEXT, not the row's existence.
  • A WRONG exit outranks a missing one. A dead end tells the user to stop; advice that does not work sends them in circles blaming themselves. Check each named exit still runs before treating the message as fixed.
  • Closing an issue while half of it is still true strands the reporters of the other half. When one thread carries two failure modes, close nothing and comment naming both, what shipped for which, and what the rest are still waiting on.
  • Distinguish "the provider lacks this fact" from "lacks it AT THIS LINE". The second is plumbing and costs nothing to thread through; only the first justifies new wire vocabulary, which is a one-way door consumers must implement forever.

Decision Gates

SituationAction
3+ issues, same subsystemOne cluster exploration → one chained fix batch, slices by root not by issue
Issue matches an in-flight redesignBucket A; close at delivery with the named test; offer the current workaround (e.g. kill switch) meanwhile
Fix wants a new state/flag/verbRedesign: what can be deleted or relaxed instead?
Dead-end report (no exit)Highest severity class; the fix is an exit, not a guard
Reporter's value not reproducible from codeMake the message self-diagnosing, then ask the reporter for their exact input
Test written against the issue's mechanism passes on unmodified mainThe diagnosis is wrong, the report is not. Strengthen the test until it drives the real surface
One thread, two failure modesComment naming both and what each waits on; close neither
A gate ships without the change that satisfies itHighest severity: the population it blocks cannot even reproduce their other issues

Output Contract

Per triage: bucket counts, per-issue table (issue | bucket | root/cluster | evidence ref), urgent flags. Per fix batch: which issues it closes, the named tests proving each closure, and net line delta (deletion-heavy is the goal).

Frequently asked questions

What does the Systemic Issue Triage AI skill do?

Trigger: new issue, bug report, triage, backlog, issue flood, community report, root cause, dead-end, blocked user. Attack issues by root class, never one-by-one; fixes must shrink the system, not grow it.

Why use Systemic Issue Triage on TypingMind?

Because you install it once and use it with any model. Systemic Issue Triage 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 Systemic Issue Triage in TypingMind?

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

Which AI models can use Systemic Issue Triage?

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 Systemic Issue Triage?

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

Is the Systemic Issue Triage 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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