distilly
Distilly, Distill how they think into reusable Skills for any Agent or Bot. Formerly Colleague Skill（原同事 Skill）.
Runs with mockssource: GitHubPythonMITcommit b830d3dcbde0
Python, MIT licensed. The project labels itself: agent skills, agentic ai, ai agent, ai agents, ai assistants, ai persona, claude code and claude skills.
distilly runs, with stand-ins for the services it depends on. An Argusic agent installed it in 0.7 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
Python dependencies installed in venv, all 73 pytest tests pass, the Node.js distilly CLI prints version 1.0.0 and installs the Skill payload correctly.
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 92 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
- under a minute
- Cold machine to finish
- 4 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- python3 -m pytest tests/ -v exited 0, 73 passed. node bin/distilly.mjs --version returned 1.0.0. node bin/distilly.mjs install --path /tmp/test-distilly/distilly created valid install.
- Model tokens used
- 27,387
- Exact commit tested
- b830d3dcbde0
- 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
- Behaviour against real services
- this run was verified against stand-in services only
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
output Optional output path; defaults to <input>_transcript.txt options: -h, --help show this help message and exit exec /bin/sh -lc 'ls /tmp/test-distilly/ && echo "exists, will be cleaned up later"' in /work/repo succeeded in 0ms: distilly exists, will be cleaned up later All good. Here's the final report: tokens used 27,387 All good. Here's the final report:
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
- Installed in 0.7 minutes, faster than the median of the 10 comparable projects Argusic has measured.
- Nothing broke on the way: zero errors between clone and running.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 11 agent-skills projects Argusic has installed and timed, distilly was the 4th fastest to reach a running state, and 9 of 11 reached one at all.
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
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.02 |
Topics (from GitHub)
agent-skillsagentic-aiai-agentai-agentsai-assistantsai-personaclaude-codeclaude-skillscodexcodex-skillsdeepseek-harnessdigital-humandsh-pluginhermes-agentknowledge-distillationllm-agentmeta-skillopenclawopencode
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/distilly)Questions
- Does distilly run?
- distilly runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 1 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test distilly?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit b830d3dcbde0. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test distilly?
- The run that produced this verdict cost $0.02: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does distilly take to install?
- 0.7 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 distilly need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing distilly?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does distilly compare with the alternatives?
- Of the 11 agent-skills projects Argusic has installed and timed, distilly was the 4th fastest to reach a running state, and 9 of 11 reached one at all.
- Where is the evidence for distilly?
- 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.