by stevesolun
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# Add to your Claude Code skills
git clone https://github.com/stevesolun/ctxGuides for using ai agents skills like ctx.
Last scanned: 5/26/2026
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"scannedAt": "2026-05-26T07:46:09.357Z",
"semgrepRan": false,
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}Find the cheapest AI coding setup that actually works on your repo.
CTX Fit analyzes your repository, tests promising AI coding configurations
against real tasks in it, and produces the winning configuration as a
reviewable change — in your working tree with --apply, or as a pull request
with --pr. It picks the cheapest setup that reliably works — reliability
is a requirement, not a tie-break — and if nothing beats what you already have,
it says so.
The winner is chosen by a fixed rule, not a score: discard every candidate below the reliability floor, then minimize attributable cost, then break ties toward the simpler configuration. An LLM may explain a result; it never decides one.
Release scope (1.0.21). CTX Fit compares capability configurations within one coding-agent harness; it does not compare Codex, Claude Code, or other harnesses against one another. It recognizes and can run repository-native verification commands for Python, JavaScript/TypeScript, Go, Rust, and Make, and treats the selected test command as the verification authority. For an installable Python project, CTX Fit builds a campaign environment and installs it without network access; its build backend and dependencies must already be available without downloading them. In the other ecosystems, verification is supported only when the runtime is usable from the host
PATHunder an isolated home and the verification dependencies are already available in the repository. Final verification uses that isolated home and runs without network access, so a user's package caches are not a supported dependency source. This is evidence for normal development; it does not prove that deliberately hostile code cannot deceive its own test runner. Release qualification did not include a paid live-provider trial, so inspectctx doctorand the dry run before authorizing spend.
pip install --upgrade claude-ctx
cd /path/to/my-project
ctx fit
Bare ctx fit is free, local and read-only: it runs no model, spends nothing,
and issues no git commands at all. Example output, abridged from a real run
against this repository:
Repository: /path/to/ctx
Languages: python, javascript
Current AI coding setup
Instructions: AGENTS.md, CLAUDE.md
Tool config: .claude/settings.local.json
Installed skills: 26
How this repository verifies itself
test python -m pytest -q
from pyproject.toml [tool.pytest] (high confidence)
typecheck python -m mypy src
from pyproject.toml [tool.mypy] (high confidence)
lint python -m ruff check .
from pyproject.toml [tool.ruff] (high confidence)
build python -m build
from pyproject.toml [build-system] (medium confidence)
AI agent readiness
91/100
Verification 30/30
Instructions 20/20
Environment 6/15
CI enforcement 15/15
Tool safety 10/10
Context tractability 10/10
Highest-impact improvements
1. Commit a dependency lockfile. (+9)
no dependency lockfile is committed
This repository has the static evidence needed to plan an evaluation: it
declares deterministic tests. Whether those tests can execute is checked only
inside the campaign.
Requires Python 3.11 or newer. Add --json for machine-readable output, or
--dry-run to see what a full evaluation would involve. --dry-run does read
your history — it runs read-only git queries (log, show --name-only,
ls-tree, rev-parse) to derive representative tasks — and writes nothing:
not to the repository, not to the index, not to any ref.
Beyond the free profile, ctx fit --test --budget N evaluates candidate
configurations against those tasks. Spending needs both flags: --test
without --budget only plans. Run ctx doctor to see whether a real
evaluation can run here. A real evaluation needs
pip install "claude-ctx[harness]", Node.js with npx for the
workspace-filesystem MCP, a matching provider credential, and Bubblewrap on
Linux; the base install can profile, plan, and simulate. Without a matching
provider credential, --test runs in simulation, which proves the pipeline but
not your repository. With a credential but a missing live prerequisite, CTX
refuses the run before trial setup. A simulated result is refused as evidence
for --apply and --pr.
Ubuntu 24.04 restricts unprivileged user namespaces, and merely installing
bwrap does not prove it can start the network-disabled namespace CTX uses for
repository commands. Install and load Ubuntu's packaged, scoped
bwrap-userns-restrict profile for /usr/bin/bwrap:
sudo apt update
sudo apt install bubblewrap apparmor-profiles apparmor-utils
if [ ! -e /etc/apparmor.d/bwrap-userns-restrict ]; then
sudo install -m 0644 \
/usr/share/apparmor/extra-profiles/bwrap-userns-restrict \
/etc/apparmor.d/bwrap-userns-restrict
fi
sudo apparmor_parser -r /etc/apparmor.d/bwrap-userns-restrict
ctx doctor
Keep Ubuntu's global unprivileged-user-namespace restriction enabled; CTX uses
the targeted Bubblewrap profile instead of weakening that system-wide security
boundary. The profile is administrator-visible host policy for every
/usr/bin/bwrap caller, not a CTX-private setting; the commands above preserve
an existing local profile rather than overwriting it. ctx doctor proves this
path with a bounded /bin/true probe in the same no-network namespace. It
executes no repository code and calls no model.
See Ubuntu's AppArmor user-namespace guidance
and the packaged Bubblewrap profile.
--apply and --pr write different thingsctx fit --apply writes the winning configuration into your working tree, on
whatever branch you are standing on. It prints every proposed change first and
stops there unless you pass --yes. The write itself runs no git command:
nothing is staged, committed, or pushed. Getting to it does run git — --apply
is refused without evidence from ctx fit --test --budget N, and deriving the
tasks for that evaluation uses the same read-only queries --dry-run uses.
Each proposed change names the file and whether CTX Fit is creating or
modifying it. Today every plan contains exactly one CTX-owned artifact,
.ctx/fit-configuration.json. The sidecar records the pinned model plus the
exact instruction and capability bytes that were evaluated, with their hashes;
ordinary ctx run invocations validate and activate that configuration.
| It printed | State after the write | Review with | Undo with |
|---|---|---|---|
modify: .ctx/fit-configuration.json |
existing sidecar replaced after a compare-and-swap check | git diff -- .ctx/fit-configuration.json when tracked; otherwise inspect the file directly |
restore the tracked file from version control, or restore your saved copy if it was untracked |
create: .ctx/fit-configuration.json |
new and untracked until you add it | git status --short --untracked-files=all and inspect the file directly |
delete .ctx/fit-configuration.json |
CTX Fit does not rewrite AGENTS.md, CLAUDE.md, or other user-authored
instruction files. Their evaluated bytes are embedded in the sidecar instead.
If an existing untracked sidecar matters to you, save a copy before confirming
the write; version-control restore commands cannot recover an untracked file.
ctx fit --pr writes to a remote. It creates a branch, commits the winning
configuration, pushes it to origin, and opens a pull request through the
GitHub CLI. Before running anything it prints the pull-request body, the files
it will write, and the exact command sequence:
git checkout -b ctx-fit/<timestamp>
git add -- <paths>
git commit -m "<pull request title>"
git push --set-upstream origin ctx-fit/<timestamp>
gh pr create --title "<pull request title>" --body-file -
Without --yes it stops there and changes nothing. With --yes it writes those
files into the working tree and then runs those five commands, in that order and
no others. Before any of them runs, the gate described below runs read-only
probes — git rev-parse, git status, git remote get-url, and gh auth status — which is what lets every refusal leave the repository exactly as it
found it. CTX Fit never merges.
--pr refuses before touching anything if you are not inside a git repository,
if the working tree has changes CTX Fit did not write (including untracked
files — they would be carried onto the new branch), if gh is not installed or
not logged in, if the branch already exists, or if there is no remote to push
to. Each refusal says which one it was, exits non-zero, and leaves the tree
untouched. If a command fails partway, CTX Fit reports which one and how many
ran, and how to get back to the branch you were on; the files it had already
written stay in your working tree.
Release: v1.0.21
is the CTX Fit release. The distribution remains
claude-ctx; the installed command is
ctx.
Requires CPython 3.11 or newer. Linux and macOS are the tested host platforms; other POSIX systems are best-effort. Native Windows and Powe
ctx is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by stevesolun. Repo-aware recommendations for skills, agents, MCP servers, and model harnesses. Use your own inventory or the shipped 79,958-node graph with 68,494 skills, 467 agents, 10,790 MCPs, and 207 harnesses. It has 583 GitHub stars.
ctx returned warnings in SkillsLLM's automated security scan. It has no critical vulnerabilities, but review the flagged issues in the Security Report section before adding it to your workflow.
Clone the repository with "git clone https://github.com/stevesolun/ctx" and add it to your Claude Code skills directory (see the Installation section above).
ctx is primarily written in Python. It is open-source under stevesolun on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh ctx against similar tools.
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