Finishing a Development Branch
obra/superpowers
Walks the last step of a branch: confirm tests pass, detect the git environment, ask how to integrate, carry out your choice and clean up the worktree.
Derive and run an interactive, evidence-recorded manual QA session for a code change, ticket, branch, or pull request.
$ npx skills add closedloop-ai/claude-plugins --skill guided-manual-qa -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install closedloop-ai/claude-plugins guided-manual-qa --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/closedloop-ai/claude-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/code/skills/guided-manual-qa .claude/skills/guided-manual-qa && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "guided-manual-qa" agent skill from https://github.com/closedloop-ai/claude-plugins/tree/main/plugins/code/skills/guided-manual-qa into .claude/skills/guided-manual-qa/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "guided-manual-qa", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/closedloop-ai/claude-plugins/tree/main/plugins/code/skills/guided-manual-qaType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add closedloop-ai/claude-plugins --skill guided-manual-qa -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install closedloop-ai/claude-plugins guided-manual-qa --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/closedloop-ai/claude-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/code/skills/guided-manual-qa .agents/skills/guided-manual-qa && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "guided-manual-qa" agent skill from https://github.com/closedloop-ai/claude-plugins/tree/main/plugins/code/skills/guided-manual-qa into .agents/skills/guided-manual-qa/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "guided-manual-qa", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add closedloop-ai/claude-plugins --skill guided-manual-qa -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install closedloop-ai/claude-plugins guided-manual-qa --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/closedloop-ai/claude-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/code/skills/guided-manual-qa .cursor/skills/guided-manual-qa && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "guided-manual-qa" agent skill from https://github.com/closedloop-ai/claude-plugins/tree/main/plugins/code/skills/guided-manual-qa into .cursor/skills/guided-manual-qa/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "guided-manual-qa", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/closedloop-ai/claude-plugins.git --path plugins/code/skills/guided-manual-qa--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add closedloop-ai/claude-plugins --skill guided-manual-qa -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install closedloop-ai/claude-plugins guided-manual-qa --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/closedloop-ai/claude-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/code/skills/guided-manual-qa .gemini/skills/guided-manual-qa && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "guided-manual-qa" agent skill from https://github.com/closedloop-ai/claude-plugins/tree/main/plugins/code/skills/guided-manual-qa into .gemini/skills/guided-manual-qa/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "guided-manual-qa", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install closedloop-ai/claude-plugins guided-manual-qaInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add closedloop-ai/claude-plugins --skill guided-manual-qa -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/closedloop-ai/claude-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/code/skills/guided-manual-qa .github/skills/guided-manual-qa && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "guided-manual-qa" agent skill from https://github.com/closedloop-ai/claude-plugins/tree/main/plugins/code/skills/guided-manual-qa into .github/skills/guided-manual-qa/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "guided-manual-qa", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add closedloop-ai/claude-plugins --skill guided-manual-qa -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install closedloop-ai/claude-plugins guided-manual-qa --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/closedloop-ai/claude-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/code/skills/guided-manual-qa .opencode/skills/guided-manual-qa && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "guided-manual-qa" agent skill from https://github.com/closedloop-ai/claude-plugins/tree/main/plugins/code/skills/guided-manual-qa into .opencode/skills/guided-manual-qa/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "guided-manual-qa", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
guided-manual-qaDerive and run an interactive, evidence-recorded manual QA session for a code change, ticket, branch, or pull request.
Guided Manual QA is an agent skill from closedloop-ai/claude-plugins. Derive and run an interactive, evidence-recorded manual QA session for a code change, ticket, branch, or pull request. Use when a human should validate live behavior checkpoint by checkpoint after repository-aware setup; do not use as a substitute for automated tests or a read-only code review.
Its SKILL.md is about 6.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/browser-state-fixtures.md` and `references/plan-methodology.md`).
It sits in Development, covering Pull requests. The repository describes itself as: Open-source Claude Code plugins for multi-agent software delivery. Plan-first SDLC workflow, code review, LLM quality judges, and self-learning — grounded in your codebase… The licence is Apache-2.0.
7 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 476b54c. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (JavaScript), which the agent can run.
Shell commands in SKILL.md call:
pnpmFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use pnpm, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Guided Manual QA loads about 6.3k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 78 tokens; SKILL.md has 3,627 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from closedloop-ai/claude-plugins at commit 476b54c, republished under its Apache-2.0 licence (© closedloop-ai). 3,627 words, ~6,294 tokens.
.claude/skills/guided-manual-qa/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.Run manual QA as a collaboration with the human, and spend the human's time only on what needs human eyes. Present a checkpoint to the human only when both are true: passing E2E on the current head does not already verify it, and the agent cannot reliably verify it itself (see "Prove the checkpoint oracle before asking the human"). Record everything else as E2E_COVERED or AGENT_VERIFIED with its evidence; neither is ever a human PASS. Prepare a safe test environment, then present the remaining human checkpoints one at a time.
E2E_COVERED when E2E covers it; plan only the uncovered part or an explicitly human-only requirement as a checkpoint. A nearby or narrower test does not count as coverage.State the proposed scope, environment, fixtures, and known gaps before launching. If requirements and the live change disagree, record the discrepancy and ask for direction when it materially changes what success means.
ORACLE CORRECTION, withdraw any dependent result, and reopen the affected checkpoints on the approved origin.--ready-selector for this gate; see references/browser-state-fixtures.md.localhost with the repository's intended local service. When persistence is involved, inventory both container runtimes and native listeners, identify the actual engine that owns each port, and compare that with the repository's documented path and the user's stated expectation. Never silently use a pre-existing native database when Docker or Compose is the expected lane, and never claim a database is in Docker without verifying the container and port mapping.started is not readiness by itself; prove the actual listener and the route-owned ready selector before presenting a checkpoint. If a wrapper hangs before spawning the child or listener, run one bounded foreground diagnostic of the same documented command to distinguish wrapper failure from app/runtime failure, then stop that diagnostic before trying a fallback.PENDING checkpoint as the resume point in the record, and continue from it. Do not re-present a checkpoint whose result still applies to the current head.javascript: URLs, raw CDP, or the user's ordinary browser profile.scripts/dist/launch-interactive-browser.mjs (Node 18+, no install step) to open the intentional interactive window with preloaded state. Keep its process alive through the checkpoint and stop only the launcher processes created for the session. The launcher path is relative to this skill's directory, not to the repository under test. Run the launcher with the bootstrapped repository root as the working directory, so it resolves Playwright from that repository, and invoke it by the absolute path you resolve from the directory where you read this SKILL.md.Use bounded recovery. Never repeat an unchanged failing launch command. Make at most one targeted repair per documented launch path, capture the exact command and failure, then move to a documented fallback or mark the affected checkpoint BLOCKED. Do not improvise an unverified substitute and present it as equivalent.
Before the first checkpoint, create a physical, durable Markdown record outside the tracked source tree when possible. Prefer an existing repository-declared QA artifact location; otherwise use a user-level directory outside every repository checkout (for example ~/.local/state/manual-qa/<repo>/<change-target>/). Do not stage or commit it. Base it on references/qa-record-template.md.
Write the exact-head E2E coverage map and the complete remaining checkpoint inventory into that file before walkthrough work begins, including expected results, dependencies, priorities, and initially known gaps. For an existing plan, label a transferred scenario E2E_COVERED only after recording the matching assertion and passing current-head result; exclude it from human PASS counts and keep its earlier details for lineage. Do not silently erase it. The file is the source of truth for session continuity; chat context, summaries, and model memory are not.
Record enough detail for another person to reproduce the session: repository and worktree, base and head, environment, services, flags and permissions, fixtures and cleanup, automated prechecks, each scenario's expected and actual result, confirmer, evidence, recovery attempts, findings, and disposition. Never store secrets or sensitive production data.
Update the physical record immediately after every material setup change, checkpoint response, blocker, recovery attempt, finding, scope change, and cleanup action. Do this before presenting the next checkpoint or ending a turn so a different agent or developer can resume solely from the file. Preserve incomplete scenarios as PENDING or BLOCKED; never remove them because context is tight or a dependency failed.
Do not turn a plausible expectation into a human checkpoint. Before presenting each checkpoint, establish and write its oracle in the physical record:
NOT APPLICABLE and route the requirement to an eligible owner or record the coverage gap.FEATURE_MAP.md, especially entries marked as unreviewed drafts) is corroborating evidence, not a requirement. When a checkpoint shows the map is wrong, record it in the QA record as map drift for the map's owner, not as a product FAIL.NOT APPLICABLE and move the checkpoint to the surface that owns it. Absence by design is neither a pass nor a product failure for the misplaced assertion.pnpm control command set), drive the checkpoint yourself on the same stack before presenting it. Enter through the entry point the requirement names (for example its feature-map id and route or hash when the repository keeps a feature map), not a convenient one. Capture the action and the resulting state. After a write, add a read-only second view of the stored value (for example pnpm control api GET <path>). Run a writing dry run only on disposable data, and reset that data before the human's run. If your run does not show the expected result, settle it as a setup problem, an oracle problem, or a candidate finding before involving the human. If this entry point was already captured on this head, link that capture instead of repeating it. If the protocol cannot reach the entry point, record why. Record the run as agent-observed.If the oracle is still uncertain, run a bounded read-only inspection or split the checkpoint into a diagnostic observation first. Do not ask the human to adjudicate an expectation the agent has not established. An observation that differs from an unsupported inference is not a product FAIL and does not authorize a fix.
Once the oracle is established, route the checkpoint. Record AGENT_VERIFIED with its agent-observed evidence, and do not present the checkpoint, when your own observation on this head (the dry run, a DOM or accessibility read, an API second view, or a log) conclusively shows the expected result and the result needs no human judgment. Route it to the human only when it needs human eyes, and record why:
When your observation of an established expectation is inconclusive, the checkpoint is not AGENT_VERIFIED; route it to the human. If the human cannot observe the discriminating state either, record BLOCKED with reason "inconclusive". When it contradicts the expectation, settle it as a setup problem, an oracle problem, or a candidate finding, as item 6 describes. Never silently pass either.
When a human observation conflicts with the prompt, re-check the cited acceptance or approved requirement and its applicability before opening a finding. If the expectation was wrong or only an unsupported inference, record an ORACLE CORRECTION, preserve the useful observation, withdraw any candidate finding, and revise dependent checkpoints. Do not count an oracle correction as a product FAIL.
For each checkpoint routed to the human:
PASS, FAIL, or BLOCKED, plus the observed result. After presenting the checkpoint, stop tool calls and do not advance on an assumption; resume only when the human responds or asks for setup help.FAIL, re-run the environment proof: listener ownership, settled origin, data target, and the repository's doctor or health command when it has one (for example pnpm control doctor). A result explained by environment drift is a setup BLOCKED or an ORACLE CORRECTION, not a product FAIL. Re-check disputed expectations before classifying a mismatch, then write the status, exact actual behavior, confirmer, timestamp, evidence location, and any oracle correction to the QA record.FAIL, do not change the fixture, flag, seed, viewport, or wording and present it again as the same checkpoint. A changed setup is a new checkpoint with its own oracle; the FAIL row stays.Use these meanings consistently:
PASS: the named human observed the expected behavior in the recorded environment.FAIL: the named human observed behavior that contradicts the expectation.BLOCKED: the checkpoint could not be exercised or judged; record why and what remains unverified. An inconclusive observation is BLOCKED with reason "inconclusive", never PASS.AGENT_VERIFIED: the agent conclusively observed the expected result under the routing rule in "Prove the checkpoint oracle before asking the human"; the human was not asked.E2E_COVERED: a passing E2E assertion on the current head proves the checkpoint's exact host, state, action, and result.Agent inspection, screenshots, logs, API probes, and automated assertions are not human confirmation. They can support a human checkpoint or close one as AGENT_VERIFIED or E2E_COVERED, never as PASS. Label them agent-observed or automated; never fill the confirmer field with the human's name unless that human actually confirmed the result. A result reported outside this conversation (a PR comment, a message) counts only when the platform's author identity matches the named human confirmer. Anyone else's report is supporting evidence.
When a finding is confirmed, assemble a reproducible evidence package in the local record: environment and head, prerequisites, minimal steps, expected and actual behavior, frequency, relevant logs or screenshots, affected surfaces, and cleanup state. Continue with independent checkpoints when safe. Before any source change or external-system mutation, offer an explicit next-action choice and wait for separate authorization.
Clean up only the disposable processes and data created for this run, using repository-supported teardown where available. Do not remove unrelated state.
Store the screenshots, snapshots, and verification-protocol artifacts the record cites in the record's own directory outside the worktree, not only under the worktree; for example, pnpm control writes to the worktree's .control/runs/, which goes away with the worktree. After cleanup, confirm every evidence pointer in the record still resolves, and record any that does not.
End with a concise summary containing:
PASS and FAIL, AGENT_VERIFIED, E2E_COVERED, and BLOCKED, never folded together;Reconcile that summary from the physical QA record rather than reconstructing it from conversation history. Each summary line cites the record section or evidence path that supports it. Label a claim nobody observed inferred (from code) or unverified; agent-observed and automated still say who observed it.
Do not file issues, mutate work items, change source, push code, trigger CI or reviews, or perform other external writes without separate authorization for that specific action.
© closedloop-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 5 other files (scripts, references) in plugins/code/skills/guided-manual-qa of closedloop-ai/claude-plugins.
Open the folder on GitHubat commit 476b54c
Guided Manual QA next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Guided Manual QA this skillclosedloop-ai/claude-plugins | 122 | — | ~6.3k | Automated safety check: Pass | Apache-2.0 | |
| Finishing a Development Branchobra/superpowers | 296k | 5 repos | ~1.9k | Automated safety check: Pass | MIT | |
| PR Babysitteropeninterpreter/openinterpreter | 69k | 3 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Check PRonyx-dot-app/onyx | 32k | 2 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Understand Diff AnalysisEgonex-AI/Understand-Anything | 86k | 1 repos | ~1.4k | Automated safety check: Pass | MIT | |
| PR Design DocOpenHands/OpenHands | 90k | — | ~2.4k | Automated safety check: Pass | MIT |
obra/superpowers
Walks the last step of a branch: confirm tests pass, detect the git environment, ask how to integrate, carry out your choice and clean up the worktree.
openinterpreter/openinterpreter
Watches an open GitHub pull request until it merges, handling review comments, diagnosing CI failures and retrying flaky checks along the way.
onyx-dot-app/onyx
Checks a GitHub, GitLab, or Perforce (p4) pull request (or merge request, or shelved changelist) for unresolved review comments, failing status checks, and incomplete PR descriptions.
Egonex-AI/Understand-Anything
Reads your git changes or a pull request against a prebuilt knowledge graph of the project to explain what changed, which components are affected and what is risky.
OpenHands/OpenHands
For a non-trivial pull request, write a self-contained HTML design doc under the temporary .pr/ directory and link a visibility-appropriate preview in the PR description, so maintainers grasp the…
woocommerce/woocommerce
Reviews WooCommerce code changes against the project's standards, flagging backend PHP architecture, naming, documentation, data integrity and testing violations.
closedloop-ai/claude-plugins
Run Codex to review a plan file and return structured feedback with a verdict.
closedloop-ai/claude-plugins
Check if critic reviews are still valid before re-running Phase 2.5 critics.
closedloop-ai/claude-plugins
Check if cross-repo coordinator results can be reused, avoiding redundant Sonnet agent launches.
closedloop-ai/claude-plugins
Check for a cached plan-evaluation.json result before launching the plan-evaluator agent.
closedloop-ai/claude-plugins
This skill should be used when needing to locate files within the Claude Code plugins cache directory (~/.claude/plugins/cache).
closedloop-ai/claude-plugins
Start a detached GitHub pull-request monitor that wakes the exact launching Codex Desktop or CLI root through the managed Codex App Server when review, CI, conflict, merge-queue, closure, readiness…
Categories
Derive and run an interactive, evidence-recorded manual QA session for a code change, ticket, branch, or pull request. Guided Manual QA is an agent skill from closedloop-ai/claude-plugins. Derive and run an interactive, evidence-recorded manual QA session for a code change, ticket, branch, or pull request.
Guided Manual QA fits situations like: A human should validate live behavior checkpoint by checkpoint after repository-aware setup; do not use as a substitute for automated tests; A read-only code review.
Run `npx skills add closedloop-ai/claude-plugins --skill guided-manual-qa -a claude-code`. Or copy the skill folder (plugins/code/skills/guided-manual-qa in closedloop-ai/claude-plugins) into .claude/skills/guided-manual-qa in your project. Claude Code loads it when a task matches its description.
Run `npx skills add closedloop-ai/claude-plugins --skill guided-manual-qa -a codex`. Or copy the skill folder (plugins/code/skills/guided-manual-qa in closedloop-ai/claude-plugins) into .agents/skills/guided-manual-qa in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add closedloop-ai/claude-plugins --skill guided-manual-qa -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/guided-manual-qa, .gemini/skills/guided-manual-qa, .github/skills/guided-manual-qa and .opencode/skills/guided-manual-qa in your project.
Going by SKILL.md and its folder, Guided Manual QA needs JavaScript for the scripts in its folder and the command-line tools its instructions call (pnpm). Our summary lists: Node.js; Docker.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Guided Manual QA is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 6.3k tokens (SKILL.md is roughly 25k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.4k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Guided Manual QA: Finishing a Development Branch (obra/superpowers, 296k stars), PR Babysitter (openinterpreter/openinterpreter, 69k stars), Check PR (onyx-dot-app/onyx, 32k stars) and Understand Diff Analysis (Egonex-AI/Understand-Anything, 86k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
closedloop-ai (a GitHub organization) maintains it in closedloop-ai/claude-plugins, which has 122 GitHub stars. The repository holds 41 skills in this directory. The repository was last updated on October 7, 2026.
Source: closedloop-ai/claude-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.