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Github Cache Hygiene

CommunityPopular
steipete
github-cache-hygiene

GitHub quota/cache hygiene: Gitcrawl archives, Octopool-backed gh, freshness, limits.

Overview

Publishersteipete
Repositoryagent-scripts
Skill namegithub-cache-hygiene
Stars
6.6K
Forks
547
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Github Cache Hygiene 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/steipete/agent-scripts.git /tmp/agent-scripts
mkdir -p .claude/skills
cp -r /tmp/agent-scripts/skills/github-cache-hygiene .claude/skills/github-cache-hygiene
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Github Cache Hygiene 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 Github Cache Hygiene 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 Github Cache Hygiene 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.

GitHub Cache Hygiene

Goal: discover in the local Gitcrawl archive first, then use the existing Octopool-backed gh shim for current GitHub metadata and authorized writes.

Default Path

Start with local archive reads:

bash
gitcrawl search prs "<terms>" -R owner/repo --state open --json number,title,url
bash
gitcrawl threads owner/repo --numbers 123 --include-closed --json

--include-closed keeps closed or merged candidates in scope. Archive state can lag GitHub; it is not proof of current state.

Then use bare PATH gh when current metadata is needed. On Peter's machines it is expected to be the Octopool-backed shim, so supported JSON reads share the fleet cache without changing authentication or command routing:

bash
gh search issues "<terms>" -R owner/repo --state open --json number,title,state,url,updatedAt,labels,author
gh search prs "<terms>" -R owner/repo --state open --json number,title,state,url,updatedAt,isDraft,author
gh issue list -R owner/repo --state open --author user --assignee user --label bug --json number,title,url
gh pr list -R owner/repo --state open --author user --label dependencies --json number,title,url
gh issue view 123 -R owner/repo --json number,title,state,body,comments,labels,url
gh pr view 123 -R owner/repo --json number,title,state,url,headRefName,headRefOid
gh pr checks 123 -R owner/repo --json name,state,bucket,link
gh run list -R owner/repo --branch branch-name --json databaseId,workflowName,status,conclusion,url
gh pr diff 123 -R owner/repo --patch

Use exact refs and narrow fields. Avoid broad loops like one gh issue view per result when a single gh search or gh issue list --json ... can answer the first-pass question.

For CI, avoid tight gh run list / gh run view polling loops. After a push or workflow dispatch, identify one exact run, then poll that run at 30s, 60s, then 120s intervals. Fetch logs once, only after failure or explicit request. Reuse prior output instead of re-reading completed runs.

Freshness

Local answers are good for discovery, duplicate search, old thread review, author/label triage, and "is there likely already an issue/PR?" checks.

Use a live call when:

  • writing, commenting, closing, merging, rerunning, or editing
  • checking final current state before a maintainer action
  • verifying CI status after a push
  • the local result is missing or obviously stale
  • the user asks for latest/live state

Hydrate exact PR details only when the local archive needs files, commits, checks, or run summaries for repeated review:

bash
gitcrawl sync owner/repo --numbers 123 --with pr-details

This refresh spends GitHub API calls and updates Gitcrawl's archive, not Octopool's separate gh cache. Bare gh reads do not auto-hydrate the Gitcrawl archive.

gitcrawl gh is retired and exits 2 with a migration note. Replace those recipes with archive reads followed by bare gh; the note is not an authentication failure. Do not run octopool login, change tokens/auth/PATH/config, or bypass the existing shim to repair a retired command.

After a write, do one targeted readback, not a broad rescan.

Octopool

Inspect cache behavior when rate limits are suspected:

bash
octopool whoami
octopool health
octopool stats --since 1h
octopool stats --since 24h --json

Check the saved-vs-backend totals, eligible hit rate, top route kinds, fallbacks, and client attribution. A missing client or unexpected server means that machine is outside the shared fleet cache.

Use OCTOPOOL_NO_FALLBACK=1 only for a bounded read probe that must prove relay coverage. Do not set it globally; mutations and unsupported reads still need real gh.

For relay-only proof:

bash
OCTOPOOL_NO_FALLBACK=1 gh api repos/owner/repo --jq .full_name

Agent Etiquette

Batch questions by repo and state. Reuse data already printed in the session. Back off CI polling; inspect logs only once for a failed run. Use bare PATH gh for ordinary reads and authorized writes; let Octopool own fallback to the real CLI. Do not bypass the shim with an absolute real-gh path or a binary override to replace retired Gitcrawl recipes.

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 Github Cache Hygiene AI skill do?

GitHub quota/cache hygiene: Gitcrawl archives, Octopool-backed gh, freshness, limits.

Why use Github Cache Hygiene on TypingMind?

Because you install it once and use it with any model. Github Cache Hygiene 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 Github Cache Hygiene in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/steipete/agent-scripts/tree/main/skills/github-cache-hygiene. 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 Github Cache Hygiene?

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 Github Cache Hygiene?

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

Is the Github Cache Hygiene AI skill free?

Yes. It is published on GitHub by steipete 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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