Team Diagnose logo

Team Diagnose

Organization
tikalk
team-diagnose

Use when the team context did not appear at session start, a skill failed to trigger, the install behaves unexpectedly, or someone asks why a session ran without team rules — walks the failure chain with commands and evidence before any conclusion.

Overview

Publishertikalk
Repositoryadlc-team-skills
Skill nameteam-diagnose
Stars
144
Forks
2
Bundled files
Instructions only
LicenseMIT
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 tikalk on GitHub. Read the source before you install it.

Installation

Install the Team Diagnose 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.
    https://github.com/tikalk/adlc-team-skills/tree/main/skills/team/team-diagnose
  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/tikalk/adlc-team-skills.git /tmp/adlc-team-skills
mkdir -p .claude/skills
cp -r /tmp/adlc-team-skills/skills/team/team-diagnose .claude/skills/team-diagnose
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Team Diagnose 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 Team Diagnose 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 Team Diagnose 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.

team-diagnose

Overview

Evidence-first diagnosis of broken adlc-team-skills wiring. The chain has exactly five links, each checkable with one command — a diagnosis without command output is a guess, and guesses get pasted into bug reports.

Boundary: this repo owns .events.json and the boot scripts' output. The injection side (dispatchers, generated per-agent plugins) is owned by adlc-cli — the docs/event-hook-contract.md file in the adlc-team-skills repo draws the line. Diagnose up to it, then route.

When to Use

  • Team context missing at session start (no Team Context & Decisions section).
  • A rule that should have matched didn't load.
  • Install behaves unexpectedly after an update.

When NOT to use: for application bugs unrelated to the team-context chain, or for skill behavior debates (that's a goldset/eval question).

Core Process

Run the checks in order — stop at the first failure, fix, re-run:

#Symptom areaCheck (run it)Green means
1Not configuredcat .adlc/init-options.jsonA team_ai_directives path that exists
2Missing toolcommand -v jqjq on PATH (skills registry needs it)
3Broken handlerbash .agents/skills/team-boot/scripts/bash/boot.sh from the project rootThe directives index on stdout, wrapped in EXTREMELY_IMPORTANT
4Stale installpytest tests/unit/test_generated_artifacts_sync.py -q (in the skills repo) or compare skills/**/SKILL.md vs .agents/skills/ namesMirror matches source
5Injection sideGenerated plugin present and loaded for the agent?If 1–4 are green and the session still lacks context → adlc-cli's territory — file there with the outputs of checks 1–4 attached

For "a rule didn't load" specifically: confirm the session began with the index (check 3's content), then check the task actually matched the rule's descriptor — on-demand pull is match-driven, not mind-reading.

Whole-loop check: scripts/acceptance-test.sh scratch-installs and asserts the chain end-to-end (add --live for a real agent session).

Report format

For a bug report, include: the failing check number, the command, its verbatim output, agent name/version, and how the repo was installed (npx skills add vs adlc-cli). No scrubbed narrative — raw output.

Red Flags

  • Concluding before running a command — every check is one command.
  • "Reinstall everything" before reading check 3's output.
  • Blaming a skill's content when check 1–3 failed (config, not skills).
  • Filing injection-side bugs in adlc-team-skills (check 5 routes them).

All of these mean: run the checks, then report.

Verification

A diagnosis is done when:

  • Checks 1–4 each ran with output captured (or 5 routed with evidence)
  • The failing link is named with its command output
  • The report routes to the owning repo

Frequently asked questions

What does the Team Diagnose AI skill do?

Use when the team context did not appear at session start, a skill failed to trigger, the install behaves unexpectedly, or someone asks why a session ran without team rules — walks the failure chain with commands and evidence before any conclusion.

Why use Team Diagnose on TypingMind?

Because you install it once and use it with any model. Team Diagnose 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 Team Diagnose in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tikalk/adlc-team-skills/tree/main/skills/team/team-diagnose. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Team Diagnose?

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 Team Diagnose?

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

Is the Team Diagnose AI skill free?

Yes. It is published on GitHub by tikalk under the MIT 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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