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Gstack Openclaw Investigate

CommunityPopular
garrytan
gstack-openclaw-investigate

Use when asked to debug, fix a bug, investigate an error, or do root cause analysis, and when users report errors, stack traces, unexpected behavior, or say something stopped working.

Overview

Publishergarrytan
Repositorygstack
Skill namegstack-openclaw-investigate
Stars
133.5K
Forks
19.9K
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 garrytan on GitHub. Read the source before you install it.

Installation

Install the Gstack Openclaw Investigate 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/garrytan/gstack.git /tmp/gstack
mkdir -p .claude/skills
cp -r /tmp/gstack/openclaw/skills/gstack-openclaw-investigate .claude/skills/gstack-openclaw-investigate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gstack Openclaw Investigate 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 Gstack Openclaw Investigate 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 Gstack Openclaw Investigate 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.

Systematic Debugging

Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST.

Fixing symptoms creates whack-a-mole debugging. Every fix that doesn't address root cause makes the next bug harder to find. Find the root cause, then fix it.


Phase 1: Root Cause Investigation

Gather context before forming any hypothesis.

  1. Collect symptoms: Read the error messages, stack traces, and reproduction steps. If the user hasn't provided enough context, ask ONE question at a time. Don't ask five questions at once.

  2. Read the code: Trace the code path from the symptom back to potential causes. Search for all references, read the logic around the failure point.

  3. Check recent changes:

    bash
    git log --oneline -20 -- <affected-files>

    Was this working before? What changed? A regression means the root cause is in the diff.

  4. Reproduce: Can you trigger the bug deterministically? If not, gather more evidence before proceeding.

  5. Check memory for prior debugging sessions on the same area. Recurring bugs in the same files are an architectural smell.

Output: "Root cause hypothesis: ..." ... a specific, testable claim about what is wrong and why.


Phase 2: Pattern Analysis

Check if this bug matches a known pattern:

Race condition ... Intermittent, timing-dependent. Look at concurrent access to shared state.

Nil/null propagation ... NoMethodError, TypeError. Missing guards on optional values.

State corruption ... Inconsistent data, partial updates. Check transactions, callbacks, hooks.

Integration failure ... Timeout, unexpected response. External API calls, service boundaries.

Configuration drift ... Works locally, fails in staging/prod. Env vars, feature flags, DB state.

Stale cache ... Shows old data, fixes on cache clear. Redis, CDN, browser cache.

Also check:

  • Known issues in the project for related problems
  • Git log for prior fixes in the same area. Recurring bugs in the same files are an architectural smell, not a coincidence.

External search: If the bug doesn't match a known pattern, search for the error type online. Sanitize first: strip hostnames, IPs, file paths, SQL, customer data. Search the error category, not the raw message.


Phase 3: Hypothesis Testing

Before writing ANY fix, verify your hypothesis.

  1. Confirm the hypothesis: Add a temporary log statement, assertion, or debug output at the suspected root cause. Run the reproduction. Does the evidence match?

  2. If the hypothesis is wrong: Search for the error (sanitize sensitive data first). Return to Phase 1. Gather more evidence. Do not guess.

  3. 3-strike rule: If 3 hypotheses fail, STOP. Tell the user:

    "3 hypotheses tested, none match. This may be an architectural issue rather than a simple bug."

    Options:

    • Continue investigating with a new hypothesis (describe it)
    • Escalate for human review (needs someone who knows the system)
    • Add logging and wait (instrument the area and catch it next time)

Red flags ... if you see any of these, slow down:

  • "Quick fix for now" ... there is no "for now." Fix it right or escalate.
  • Proposing a fix before tracing data flow ... you're guessing.
  • Each fix reveals a new problem elsewhere ... wrong layer, not wrong code.

Phase 4: Implementation

Once root cause is confirmed:

  1. Fix the root cause, not the symptom. The smallest change that eliminates the actual problem.

  2. Minimal diff: Fewest files touched, fewest lines changed. Resist the urge to refactor adjacent code.

  3. Write a regression test that:

    • Fails without the fix (proves the test is meaningful)
    • Passes with the fix (proves the fix works)
  4. Run the full test suite. No regressions allowed.

  5. If the fix touches >5 files: Flag the blast radius to the user before proceeding. That's large for a bug fix.


Phase 5: Verification & Report

Fresh verification: Reproduce the original bug scenario and confirm it's fixed. This is not optional.

Run the test suite.

Output a structured debug report:

DEBUG REPORT

  • Symptom: what the user observed
  • Root cause: what was actually wrong
  • Fix: what was changed, with file references
  • Evidence: test output, reproduction showing fix works
  • Regression test: location of the new test
  • Related: prior bugs in same area, architectural notes
  • Status: DONE | DONE_WITH_CONCERNS | BLOCKED

Save the report to memory/ with today's date so future sessions can reference it.


Important Rules

  • 3+ failed fix attempts: STOP and question the architecture. Wrong architecture, not failed hypothesis.
  • Never apply a fix you cannot verify. If you can't reproduce and confirm, don't ship it.
  • Never say "this should fix it." Verify and prove it. Run the tests.
  • If fix touches >5 files: Flag to user before proceeding.
  • Completion status:
    • DONE ... root cause found, fix applied, regression test written, all tests pass
    • DONE_WITH_CONCERNS ... fixed but cannot fully verify (e.g., intermittent bug, requires staging)
    • BLOCKED ... root cause unclear after investigation, escalated

Frequently asked questions

What does the Gstack Openclaw Investigate AI skill do?

Use when asked to debug, fix a bug, investigate an error, or do root cause analysis, and when users report errors, stack traces, unexpected behavior, or say something stopped working.

Why use Gstack Openclaw Investigate on TypingMind?

Because you install it once and use it with any model. Gstack Openclaw Investigate 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 Gstack Openclaw Investigate in TypingMind?

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

Which AI models can use Gstack Openclaw Investigate?

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 Gstack Openclaw Investigate?

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

Is the Gstack Openclaw Investigate AI skill free?

Yes. It is published on GitHub by garrytan 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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