agent-toolkit
A curated collection of skills for AI coding agents. Skills are packaged instructions and scripts that extend agent capabilities across development, documentation, planning, and professional workflows.
Runssource: GitHubPythonMITcommit 3027f20f3181
Python, MIT licensed. The project labels itself: agent skills, ai, automation, claude, claude code, coding agent and development.
agent-toolkit runs. An Argusic agent installed it in 4 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
The build script assembles all 56 plugins into dist/ (232 files, 1.7MB), the marketplace.json passes CI validation, dist is in sync with source, version bumping works, and every SKILL.md, Python script, and bash script in the repository is syntactically valid.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
- Install time
- 4 minutes
- Cold machine to finish
- 4 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- python3 scripts/build_plugins.py built 56 plugins/232 files; jq validated marketplace.json schema; git diff confirmed dist up to date; all 43 SKILL.md files have valid frontmatter with name/directory consistency; all 18 .py and 5 .sh files pass syntax checks; all 60 README links resolve; manual skill copy to ~/.claude/skills/ succeeds
- Model tokens used
- 74,768
- Exact commit tested
- 3027f20f3181
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
**What was verified:** - **Build pipeline**: `python3 scripts/build_plugins.py` — built 56 plugins, 232 files, 1.7MB total - **Marketplace validation**: `jq` confirms all required fields (`name`, `owner`, `plugins`, `metad… - **Dist integrity**: `git diff --quiet dist/ .claude-plugin/marketplace.json` — dist is up to date… - **Version bumping**: `python3 scripts/bump_version.py --show` reports 1.0.10; `bump_version.py pa… - **SKILL.md frontmatter**: all 43 skills have valid `name` and `description` fields; directory nam… - **Python scripts**: all 18 `.py` files across repo and skills pass `python3 -m py_compile` - **Bash scripts**: all 5 `.sh` files pass `bash -n` - **README links**: all 60 links in the skills table resolve - **Manual install**: `cp -r skills/codex ~/.claude/skills/` succeeds per README instructions - **CI workflow compatibility**: all checks in `.github/workflows/main.yaml` pass locally **One limitation** (not a repo defect): the `npx skills add` command requires Node.js >=22.20.0 but…
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Nothing broke on the way: zero errors between clone and running.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Took 4 minutes to install, slower than the median of the 30 comparable projects Argusic has measured.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 31 agent-skills projects Argusic has installed and timed, agent-toolkit was the 20th fastest to reach a running state, and 22 of 31 reached one at all.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 4 minutes after the clone.
- Following its own README: the documented path worked with nothing to fix.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
Topics (from GitHub)
agent-skillsaiautomationclaudeclaude-codecoding-agentdevelopment
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/agent-toolkit)Questions
- Does agent-toolkit run?
- Yes. agent-toolkit runs. Argusic installed and launched it on a clean machine in 4 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test agent-toolkit?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 3027f20f3181. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test agent-toolkit?
- The run that produced this verdict cost $0.04: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does agent-toolkit take to install?
- 4 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does agent-toolkit need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing agent-toolkit?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does agent-toolkit compare with the alternatives?
- Of the 31 agent-skills projects Argusic has installed and timed, agent-toolkit was the 20th fastest to reach a running state, and 22 of 31 reached one at all.
- Where is the evidence for agent-toolkit?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.