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Agent skill repository index

Skill aAAaqwq/AGI-Super-Team/skills/agent-skill-repository-index

Find and compare Daniel's reviewed GitHub skill sources. Use for high-star skill discovery, link checks, or safe installation, update, and removal of one selected skill.From its SKILL.md

Install
npx -y skills add aAAaqwq/AGI-Super-Team --skill agent-skill-repository-index

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • runs commandsInstructs the agent to run 3 commands, including `python3 scripts/verify_repository_index.py --repo-root /absolute/path/to/repos` and 2 more.

SKILL.md

3.5 KB, 721 tokens by cl100k_base, as published. Nobody here has run it

Daniel's Recommended High-Star GitHub Skills

Route agents to reviewed upstream repositories without globally activating every repository. Treat popularity as a discovery signal, never as proof of quality or safety.

Load Only What You Need

  • Read references/repositories.md to find and compare sources.
  • Read references/star-snapshot.md only when the user asks about popularity or ranking.
  • Read references/installing.md before installing, updating, linking, migrating, or removing a skill.
  • Run python3 scripts/verify_repository_index.py --repo-root /absolute/path/to/repos to verify index-to-clone identity. Add --remote when current GitHub reachability matters.

Fast Workflow

  1. Resolve intent: discovery, comparison, installation, update, removal, or link verification.
  2. Search already installed skills first. Reuse an equivalent Codex-native skill instead of adding a duplicate.
  3. Shortlist at most three sources by capability, runtime compatibility, maintenance, license, and risk class. Use stars only as a tie-breaker.
  4. If a repository is an awesome-* catalog or has no root SKILL.md, use it for discovery only. Trace the selected item to its original upstream.
  5. Inspect one candidate directory completely: SKILL.md, adjacent resources, license, Git remote, commit, worktree state, scripts, hooks, dependencies, credentials, network actions, and persistent state.
  6. Classify it as DAILY, LIBRARY, QUARANTINE, or DUPLICATE. Do not install QUARANTINE capabilities without explicit authorization for their runtime effects.
  7. Follow references/installing.md. Install only the reviewed skill subdirectory, not the entire aggregator.
  8. Validate metadata and resources, then test one positive trigger and one negative trigger.
  9. Return an installation receipt containing upstream URL, local source, subpath, commit, license, class, target, install method, adaptations, checks, update method, and rollback method.

Efficient Search

Search metadata before opening files:

rg -n --glob 'SKILL.md' '^(name|description):.*KEYWORD' /absolute/path/to/repos

Then inspect only the chosen skill directory. Scan risky behavior without printing secret values:

rg -l --hidden '(curl.+\|\s*(sh|bash)|sudo\b|rm\s+-rf|cookies?|auth|token|secret|hook|auto.?update|deploy|tunnel|daemon|memory)' /absolute/path/to/candidate

Decision Rules

  • Prefer the original upstream, a Codex-native plugin, or a single passive instruction skill.
  • Prefer a symlink for a clean reviewed local source that should update with its repository; prefer a normalized copy when Codex-specific adaptation is required.
  • Keep catalogs, full runtimes, hooks, MCP servers, auto-updaters, deployers, tunnels, browser profiles, and persistent-memory systems inactive during discovery.
  • Preserve dirty repositories and existing targets. Never overwrite or delete them to complete an installation.
  • Fetch live star counts only when the user asks for a current ranking. Label the bundled figures as Daniel's July 2026 snapshot.

For “redesign this landing page,” invoke the selected design skill. For “which high-star design skill repository does Daniel recommend, and install the safest option,” invoke this skill first.

What ships with it: 5 files

17.4 KB alongside SKILL.md, 1 of them executable

agents/

scripts/

Gives 0 of the 12 instructions most context ai engineering skills give in 721 tokens

Counted across 1,328 of the 2,349 authors here whose files we hold, read 2026-09-06

  • Dispatch a fresh subagent for each taskin 76 of 1328, across 59 files
  • Perform spec compliance review before code quality reviewin 44 of 1328, across 34 files
  • Dispatch a final code reviewer after all tasksin 38 of 1328, across 26 files
  • Answer subagent questions before allowing implementationin 36 of 1328, across 26 files
  • Use the least powerful model capable of the taskin 33 of 1328, across 26 files
  • Create a TodoWrite list for all tasksin 32 of 1328, across 22 files
  • Perform a task review after each implementationin 31 of 1328, across 24 files
  • Extract all tasks and context from the planin 29 of 1328, across 20 files
  • Provide full task text to subagentsin 28 of 1328, across 20 files
  • Use git worktrees for isolated workspacesin 25 of 1328, across 20 files
  • Specify the model explicitly when dispatching a subagentin 23 of 1328, across 18 files
  • Execute all tasks from the plan without stoppingin 21 of 1328, across 16 files

Said here and by no other author read

  • Read repository references to find and compare sources
  • Shortlist at most three sources by capability and risk
  • Inspect candidate directories for security and metadata
  • Classify skills as daily, library, quarantine, or duplicate
  • Install only the reviewed skill subdirectory
  • Validate metadata and test positive and negative triggers

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.