Context Builder
v0.8.3-1Generate LLM-optimized codebase context from any directory using context-builder CLI
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by@igorls
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's name/description (generate LLM-optimized context from a directory) matches the instructions: it calls a context-builder CLI, describes filters, signatures, and token budgeting. The install guidance (cargo install with tree-sitter feature or GitHub releases) is consistent with a CLI that needs language parsers. There are no unrelated env vars, binaries, or config paths requested.
Instruction Scope
The SKILL.md explicitly instructs reading file contents from an input directory (central to the skill). It also documents safeguards (auto-ignore heavy dirs, respects .gitignore, binary detection) and warns that outputs may contain embedded secrets and should be reviewed. This is appropriate for the purpose, but agent workflows that use -y to skip prompts can cause automatic, unreviewed ingestion if the agent is fed an overly broad path — exercise caution and ensure explicit, scoped paths.
Install Mechanism
There is no registry install spec in the skill bundle (instruction-only), but SKILL.md directs installation via cargo (crates.io) or downloading prebuilt binaries from GitHub Releases with SHA256 checksums. Both sources are standard; building requires the Rust toolchain and network access. Recommend verifying checksums and preferring distro packages or pinned release artifacts if available.
Credentials
The skill requests no environment variables or credentials. While the tool will read repository files (which may contain secrets), the declared requirements are minimal and proportionate to the stated functionality.
Persistence & Privilege
The skill does not request 'always: true' and has no install-time persistence specified. It does not ask to modify other skills or system-wide settings. Normal autonomous invocation is allowed by default (disable-model-invocation is false), which is expected for an agentic CLI helper.
Assessment
This skill appears coherent for producing LLM-ready snapshots of projects. Before installing or running it: 1) only point it at explicit project roots (avoid ~, /etc, ~/.ssh, ~/.aws, or other credential stores); 2) prefer running a token-count dry-run or using filters (--token-count, -f) and review outputs before sharing (the tool can surface embedded keys/secrets); 3) verify prebuilt binary checksums from the GitHub Releases page or build from source in a sandboxed environment; 4) be cautious with automated agent runs that use -y (they skip confirmations) — require explicit absolute paths in agent prompts to avoid accidental wide scans.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.