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Contributor Pipeline Gardening

Community
JSONbored
contributor-pipeline-gardening

Maintenance of the contributor issue pipeline for JSONbored/awesome-claude (HeyClaude) — closing issues that are already done but not marked so, and keeping a small, high-value contributor-available backlog stocked with real website/content/feature/bugfix work. Runs every ~24h via a dedicated scheduled task. Invoke for "run the issue gardening", "audit open issues for stale/complete ones", "generate new contributor issues", or any recurring/scheduled run of this process. `reference.md` (next to this file) has the exhaustive label/milestone/ template detail — read it before doing real work, not just this file. This is the awesome-claude-specific instance; JSONbored/loopover and JSONbored/metagraphed each have their own separate copy with different conventions and a much larger backlog target — do not cross-apply either repo's specifics to this one without being asked.

Overview

PublisherJSONbored
Repositoryawesome-claude
Skill namecontributor-pipeline-gardening
Stars
303
Forks
163
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 JSONbored on GitHub. Read the source before you install it.

Installation

Install the Contributor Pipeline Gardening 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/JSONbored/awesome-claude/tree/main/.claude/skills/contributor-pipeline-gardening
  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/JSONbored/awesome-claude.git /tmp/awesome-claude
mkdir -p .claude/skills
cp -r /tmp/awesome-claude/.claude/skills/contributor-pipeline-gardening .claude/skills/contributor-pipeline-gardening
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Contributor Pipeline Gardening 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 Contributor Pipeline Gardening 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 Contributor Pipeline Gardening 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.

Contributor pipeline gardening — awesome-claude (HeyClaude)

HeyClaude is a curated registry/directory of Claude and AI-workflow assets, plus the website (apps/web), registry/schema library (packages/registry), and MCP package (packages/mcp) that serve it. This is a much smaller, quieter repo than loopover/metagraphed — as of 2026-07-17 it had zero open gittensor:*-labeled issues and a thin, fully-closed 2-milestone history. This skill exists to build and then maintain a small, deliberately curated backlog — 15-25 contributor-available issues, not 50-100 — because padding a codebase this size to a larger number would mean weak/duplicate issues, which is explicitly worse than a smaller real backlog here.

The one thing this skill exists to prevent

Confirmed by the maintainer, 2026-07-17: contributors here have recently gravitated to test(...): cover X PRs — real tests, but zero behavior change, added purely to nudge Codecov's codecov/patch: 70% bar (a soft, easily-hit target — unlike loopover's 99% gate, 70% patch coverage is trivially satisfied by testing already-written code). A second anti-pattern found during this skill's initial research: issue #550 (a "growth sprint" umbrella with a long child checklist) was being farmed almost entirely for its single easiest repeatable pattern — wiring one more page into the existing intent-event/analytics-click convention, dozens of near-identical PRs (#5205, #5202, #5201, ... #5168, all Made with Cursor), rather than any of the umbrella's harder, more valuable child issues.

Never generate a standalone test(...): cover X issue. A regression test attached to a real bug fix is fine and expected (see the template in reference.md); a test-only issue with no behavior change is not. Never generate more instances of an already-being-farmed shallow repeatable pattern (check open issues and recent merged-PR titles for a dense run of near-identical titles before filing anything that looks like "wire pattern X into one more file/page/route" — if 5+ near-identical PRs already exist for the same pattern, that vein is farmed out, not a gardening opportunity). The whole point of this skill is issues that "drive real value... website/content/features grow... fix bugs/issues in frontend/backend" (the maintainer's own words) — not more of what a low-effort automated farmer already does better and faster than a well-scoped issue ever could.

Scope: code only, not content submissions

content/ (the curated directory entries themselves) is a legitimate, real contribution surface per .gittensory.yml's wantedPaths — but content submissions are explicitly PR-first, with linkedIssuePolicy: optional and issueDiscoveryPolicy: discouraged, routed through a private submission gate that doesn't need or want a GitHub issue first. This skill does not generate content-entry issues. It scopes to the three code surfaces: apps/web/src/ (the TanStack Start website — routes, components, lib), packages/registry/src/ (schema/validation/submission-risk library), and packages/mcp/ (the MCP server package), plus scripts/ when a real gap is there.

Unlike loopover/metagraphed, a linked issue is advisory here, not a hard gate (gate.linkedIssue: advisory in .gittensory.yml) — there is no auto-close mechanic forcing a contributor to pick an issue. That means an issue has to be genuinely worth picking up on its own merits (clear, valuable, narrow, low-ambiguity) rather than relying on gate pressure to make it attractive. Write every issue like it has to compete for a contributor's attention, because it does.

Pass 1 — stale-issue sweep (do this first, every run)

Same method as loopover/metagraphed's copy of this skill: for every open issue, query timelineItems(itemTypes: [CROSS_REFERENCED_EVENT]) for merged PRs that referenced it, then read the actual PR body for any hit where willCloseTarget was false. Close what's genuinely done (with a comment naming the shipping PR and a direct code check confirming it exists); leave partial work open. Given this repo starts with a near-empty gittensor backlog, most runs will find little or nothing to sweep here until the backlog this skill builds has had time to accumulate real activity — that's expected, not a sign the check is broken.

Verify against synced upstream, not a stale local checkout. Before treating any local grep/read as evidence that an issue's described work does or doesn't exist, confirm the code you're reading matches the default branch's current tip — fetch and fast-forward the checkout (or use a disposable worktree off origin/main if the primary checkout is dirty or has unpushed work on another branch) before doing any verification. A checkout that's merely clean isn't the same as current — a stale-but-clean checkout silently produced false "already done"/"not done" conclusions in the sibling loopover/metagraphed repos' gardening runs on 2026-07-17/18, causing duplicate issues to be filed for already-shipped work. Confirm sync every run; never assume a previous run's freshness carried over.

Also check issue #550 (the growth-sprint umbrella) and any other checklist-style tracker each run: sync stale checkboxes against real child-issue state, same as the sibling repos' Pass 1. Do not add new child issues to #550's shallow analytics-wiring pattern (see above) even if asked to "keep it topped up" — that pattern is already over-farmed.

Pass 2 — backlog top-up

  1. Compute the current contributor-available count: gh issue list --repo JSONbored/awesome-claude --state open --limit 1000 --json number,labels,assignees, filtered to unassigned, no maintainer-only, carrying a gittensor:* label. Target: 15-25, maintained continuously. If already in range, a quiet run that files 0-2 issues (or none) is a correct, expected outcome — this is a small repo, don't force volume.
  2. Real gaps to scope from, in priority order:
    • A genuine functional gap in apps/web/src/routes/*.tsx (~70 routes) — missing feature parity between similar pages, a real UX/accessibility gap, a bug found by reading the code against its own stated behavior.
    • A real correctness/hardening gap in packages/registry/src/ (schema/validation/risk-scoring logic) — found by reading the code, not by grepping for TODOs (a repo-wide TODO/FIXME sweep came back essentially empty as of 2026-07-17; this repo is clean, so gap-finding here means reading real logic against its own documented intent, the same technique loopover's gardening uses for its "hardening round" audits).
    • packages/mcp/'s tool/resource surface for a real, narrow parity or correctness gap.
    • Closed-milestone follow-ups: Code Quality — Round 5 (#3) and Growth & Maintainability — Round 7 (#4) are both fully closed, historical sprints — read a few of their closed issues for the kind of real, valuable work this repo's maintainer has asked for before, as a calibration reference for what "real value" looks like here, not as a source of reopenable work.
  3. Every new issue gets Contributor Backlog (Gardening) (milestone #5) — the two historical Round milestones are closed and not the target. This milestone was created 2026-07-17 specifically as the evergreen home for this skill's output (see reference.md's milestone-discipline note for why a new one was warranted here).
  4. Apply gittensor:bug / gittensor:feature / gittensor:priority (same 0.05x/0.25x/1.5x convention as loopover — gittensor:priority reserved, sparingly applied) plus help wanted. Do not apply size:*, category:*, contributor:*, pr:*, or submission-* labels — those belong to this repo's separate content-submission/PR-automation machinery, not gardening-filed issues.
  5. Use the repo's own existing product-feature.yml issue-form template (via gh issue create --template or matching its field structure manually) — see reference.md for the exact field list. This is a real, maintainer-authored template already built for exactly this use case; do not invent a different body structure.
  6. Check every new batch for a real dependency, then link with native addSubIssue/ addBlockedBy (confirmed working on this repo via GraphQL) — same discipline as the sibling repos: most independent bug/feature issues need no link; only connect a genuine prerequisite relationship.
  7. Quality over volume, always — this repo's small size makes padding especially visible and especially damaging to the "issues are worth picking up voluntarily" value proposition Pass 2's intro describes.

Daily digest

Same shape as the sibling repos': issues closed + why, checklists fixed, new issues filed with milestone/label, before/after contributor-available count (against the 15-25 target, not 50-100), anything left alone on purpose — including explicitly naming any shallow-pattern-farming or test-coverage-padding temptation that was deliberately declined.

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 Contributor Pipeline Gardening AI skill do?

Maintenance of the contributor issue pipeline for JSONbored/awesome-claude (HeyClaude) — closing issues that are already done but not marked so, and keeping a small, high-value contributor-available backlog stocked with real website/content/feature/bugfix work. Runs every ~24h via a dedicated scheduled task. Invoke for "run the issue gardening", "audit open issues for stale/complete ones", "generate new contributor issues", or any recurring/scheduled run of this process. `reference.md` (next to this file) has the exhaustive label/milestone/ template detail — read it before doing real work,...

Why use Contributor Pipeline Gardening on TypingMind?

Because you install it once and use it with any model. Contributor Pipeline Gardening 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 Contributor Pipeline Gardening in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JSONbored/awesome-claude/tree/main/.claude/skills/contributor-pipeline-gardening. 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 Contributor Pipeline Gardening?

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 Contributor Pipeline Gardening?

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

Is the Contributor Pipeline Gardening AI skill free?

Yes. It is published on GitHub by JSONbored 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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