Fix Llm Artifacts
v1.1.4Applies fixes from a prior review-llm-artifacts run, with safe/risky classification
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byKevin Anderson@anderskev
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
The skill claims to apply fixes from a prior review and its steps (reading .beagle/llm-artifacts-review.json, running git operations, applying code edits, running linters/tests) align with that purpose. However the SKILL.md assumes the availability of developer tools (git, jq, ruff, mypy, npx/npm/yarn, pytest, go, etc.) while the registry metadata declares no required binaries; this mismatch should be noted before running.
Instruction Scope
Instructions operate directly on the repository (git stash, apply fixes, run linters/tests, potentially remove .beagle/llm-artifacts-review.json). That is within scope for a fixer. The doc instructs spawning parallel agents using a 'Task' tool to apply fixes — this grants the skill the ability to perform many parallel edit operations and should be used with caution. Risky fixes are interactively prompted (y/n/s), which reduces silent destructive changes.
Install Mechanism
Instruction-only skill with no install spec or code files; nothing is written to disk by an installer. This is the lowest-risk install model.
Credentials
The skill requests no credentials or environment variables. It reads repository state and a local review JSON file (.beagle/llm-artifacts-review.json), which is appropriate for its purpose. No unrelated secrets or external endpoints are referenced.
Persistence & Privilege
The skill is not forced always-on and has disable-model-invocation set to true (reducing autonomous model use). It modifies only repository files and its own review artifact file; it does not request system-wide configuration or other skills' settings.
Assessment
This skill appears to do what it says, but it will modify your repository and expects common developer tools to be present. Before running: (1) run the --dry-run first to preview changes, (2) make sure you have a clean backup or commit (the skill will stash dirty work and may delete the .beagle review file on success), (3) ensure required tools are installed (git, jq, ruff/mypy for Python, npm/npx or yarn for JS/TS, pytest, go toolchain as relevant), and (4) be prepared to respond to prompts for risky fixes. If you manage a shared or production repo, test on a branch or clone/CI run before applying automatic fixes.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.