codesight
Universal AI context generator. Saves thousands of tokens per conversation in Claude Code, Cursor, Copilot, Codex, and more.
Runssource: GitHubhomepageTypeScriptMITcommit f9a43d70f0a6
TypeScript, MIT licensed. The project labels itself: ai, claude, cli, code analysis, codebase, codex, context engineering and copilot.
codesight runs. An Argusic agent installed it in 3.2 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
codesight v1.19.0 builds with tsc, all 149 tests pass, CLI scans and generates CODESIGHT.md and wiki output using real detectors (no mocks required).
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.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- import.meta.dirname is undefined on Node.js 18 (requires Node.js 21+) in tests/terraform-plugin.test.ts1.5 minutes
- WASI.getImportObject() not available on Node.js 18 WASI API (requires Node.js 21+)1.5 minutes
- Install time
- 3 minutes
- Cold machine to finish
- 4 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- pnpm test exited 0, 149 passed (42 suites, 0 failures), & node dist/index.js, version returned 'codesight v1.19.0' & scanning local repo produced 8 routes/0 models/68 libs in ~187ms
- Model tokens used
- 70,321
- Exact commit tested
- f9a43d70f0a6
- 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.
# duration_ms 2344.466276
exec
/bin/sh -lc 'cd /work/repo && echo "--- Time check ---" && date' in /work/repo
succeeded in 0ms:
--- Time check ---
Thu Oct 8 01:41:36 UTC 2026
All complete. Here's the summary:
**ARGUSIC_RESULT:** {"install_succeeded": true, "launch_succeeded": true, "install_minutes": 3.2, "…
tokens used
70,321
All complete. Here's the summary:
**ARGUSIC_RESULT:** {"install_succeeded": true, "launch_succeeded": true, "install_minutes": 3.2, "…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.
- Installed in 3.2 minutes, faster than the median of the 107 comparable projects Argusic has measured.
- Recovered from all 2 errors without a human stepping in, which says the failures are documented well enough to solve.
- 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
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 108 ai projects Argusic has installed and timed, codesight was the 40th fastest to reach a running state, and 74 of 108 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 3.2 minutes after the clone.
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)
aiclaudeclicode-analysiscodebasecodexcontext-engineeringcopilotcursordeveloper-toolsllmmcprepo-maptoken-savings
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/codesight)Questions
- Does codesight run?
- Yes. codesight runs. Argusic installed and launched it on a clean machine in 3 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test codesight?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit f9a43d70f0a6. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test codesight?
- The run that produced this verdict cost $0.09: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does codesight take to install?
- 3.2 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 codesight need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing codesight?
- 2 things broke in the recorded run, and 2 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does codesight compare with the alternatives?
- Of the 108 ai projects Argusic has installed and timed, codesight was the 40th fastest to reach a running state, and 74 of 108 reached one at all.
- Where is the evidence for codesight?
- 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.