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Ghcrawl Cluster Operator

Community
vincentkoc
ghcrawl-cluster-operator

Use when inspecting a ghcrawl SQLite store, pulling GitHub issue/PR data, refreshing summaries, embeddings, and clusters, or extracting one cluster and its evidence through the ghcrawl CLI.

Overview

Publishervincentkoc
Repositorydotskills
Skill nameghcrawl-cluster-operator
Stars
112
Forks
9
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by vincentkoc on GitHub. Read the source before you install it.

Installation

Install the Ghcrawl Cluster Operator 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/vincentkoc/dotskills/tree/main/skills/ghcrawl-cluster-operator
  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/vincentkoc/dotskills.git /tmp/dotskills
mkdir -p .claude/skills
cp -r /tmp/dotskills/skills/ghcrawl-cluster-operator .claude/skills/ghcrawl-cluster-operator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ghcrawl Cluster Operator 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 Ghcrawl Cluster Operator 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 Ghcrawl Cluster Operator 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.

ghcrawl Cluster Operator

Purpose

Operate ghcrawl as a local-first GitHub issue and pull request crawler: inspect the SQLite store, pull GitHub data, refresh summaries and embeddings, build clusters, and extract cluster evidence through deterministic CLI commands.

The default stance is conservative and cost-aware. Inspect first, then run mutating or API-spend commands only when the operator asked for fresh data or enrichment.

When to use

  • Inspecting ghcrawl repository state, local runs, thread counts, clusters, or durable cluster decisions.
  • Pulling GitHub issue/PR data into a local ghcrawl store.
  • Running OpenAI-backed summaries, structured key summaries, embeddings, and clustering.
  • Explaining a cluster with members, events, canonical selections, exclusions, and local evidence.
  • Operating maintainer edits such as excluding a member from a durable cluster or setting a canonical item.

Workflow

  1. Start with read-only checks: doctor, configure, runs, clusters, cluster-explain, and threads.
  2. Confirm the target repo as owner/repo and prefer --json for agent-readable output.
  3. Use sync or refresh only when fresh GitHub data is needed.
  4. Use --include-code only when file overlap matters; it hydrates PR file metadata and can increase DB size.
  5. Run structured key summaries before embedding when LLM summaries should influence vectors.
  6. Run embed, then cluster, after summary or configuration changes.
  7. Pull one cluster with cluster-explain before making durable maintainer edits.
  8. After durable edits, rerun cluster and explain the affected cluster to verify the decision stuck.

Inputs

  • repo (required): GitHub repository in owner/repo format.
  • db_path (optional): explicit SQLite database path when not using the configured default.
  • cluster_id (optional): durable or run cluster identifier to explain or edit.
  • thread_numbers (optional): comma-separated GitHub issue/PR numbers to inspect.
  • include_code (optional): whether PR file metadata should be hydrated and used as clustering evidence.
  • summary_model (optional): LLM model for structured summaries, usually gpt-5.4.
  • embedding_basis (optional): vector source such as title_original or llm_key_summary.
  • limit (optional): item cap for sync, summaries, or listing commands.

Outputs

  • Local health and configuration status.
  • Run history and current cluster counts.
  • Cluster lists with size, names, titles, states, and member evidence.
  • Cluster explain output with members, events, exclusions, canonical picks, summaries, and top touched files when available.
  • Thread snapshots for selected issue/PR numbers.
  • Verification notes after refresh, embedding, clustering, or durable maintainer actions.

Ground Rules

  • Prefer read-only inspection commands first: doctor, runs, clusters, cluster-explain, threads.
  • Treat refresh, sync, summarize, key-summaries, and embed as remote/API-spend commands.
  • cluster is local-only but can be CPU-heavy on huge repos.
  • Always pass --json for agent-readable output unless opening the TUI.
  • Use --include-code only when file overlap matters.

Setup Check

bash
ghcrawl doctor --json
ghcrawl configure --json
ghcrawl runs owner/repo --limit 10 --json

If the local store is empty or stale, pull current open GitHub data:

bash
ghcrawl sync owner/repo --limit 200 --json
ghcrawl sync owner/repo --include-code --limit 200 --json

For a normal end-to-end update:

bash
ghcrawl refresh owner/repo --json

Use code hydration when file evidence should affect clustering:

bash
ghcrawl refresh owner/repo --include-code --json

LLM And Embedding Pipeline

Default clustering can run without LLM summaries. LLM summaries and embeddings enrich the cluster graph.

Useful configurations:

bash
ghcrawl configure --summary-model gpt-5.4 --embedding-basis title_original --json
ghcrawl configure --summary-model gpt-5.4 --embedding-basis llm_key_summary --json

For structured key summaries:

bash
ghcrawl key-summaries owner/repo --limit 200 --json
ghcrawl key-summaries owner/repo --number 12345 --json

Then refresh vectors and clusters:

bash
ghcrawl embed owner/repo --json
ghcrawl cluster owner/repo --json

Pull A Cluster And Its Info

List clusters:

bash
ghcrawl clusters owner/repo --min-size 2 --limit 20 --sort size --json
ghcrawl clusters owner/repo --search "cron timeout" --limit 10 --json

Explain one durable cluster:

bash
ghcrawl cluster-explain owner/repo --id 123 --member-limit 50 --event-limit 50 --json

Inspect current durable clusters with members:

bash
ghcrawl durable-clusters owner/repo --member-limit 25 --json
ghcrawl durable-clusters owner/repo --include-inactive --member-limit 25 --json

Pull specific issues/PRs from the local store:

bash
ghcrawl threads owner/repo --numbers 123,456,789 --json

Open the TUI:

bash
ghcrawl tui owner/repo

Local Maintainer Actions

Use these only when the operator asks for durable cluster edits:

bash
ghcrawl exclude-cluster-member owner/repo --id 123 --number 456 --reason "not same root cause" --json
ghcrawl include-cluster-member owner/repo --id 123 --number 456 --reason "same root cause" --json
ghcrawl set-cluster-canonical owner/repo --id 123 --number 456 --reason "clearest report" --json
ghcrawl merge-clusters owner/repo --source 123 --target 456 --reason "same issue family" --json

After edits, re-run:

bash
ghcrawl cluster owner/repo --json
ghcrawl cluster-explain owner/repo --id 123 --member-limit 50 --event-limit 50 --json

Flow

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ghcrawl Cluster Operator AI skill do?

Use when inspecting a ghcrawl SQLite store, pulling GitHub issue/PR data, refreshing summaries, embeddings, and clusters, or extracting one cluster and its evidence through the ghcrawl CLI.

Why use Ghcrawl Cluster Operator on TypingMind?

Because you install it once and use it with any model. Ghcrawl Cluster Operator 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 Ghcrawl Cluster Operator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vincentkoc/dotskills/tree/main/skills/ghcrawl-cluster-operator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ghcrawl Cluster Operator?

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 Ghcrawl Cluster Operator?

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

Is the Ghcrawl Cluster Operator AI skill free?

Yes. It is published on GitHub by vincentkoc 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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