Anchor Sheet
v1.0.0Extract per-subsection “anchor facts” (NO PROSE) from evidence packs so the writer is forced to include concrete numbers/benchmarks/limitations instead of ge...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description say: extract numeric/benchmark/limitation anchors from evidence packs; the code and SKILL.md only require reading outline/evidence_drafts.jsonl and citations/ref.bib and writing outline/anchor_sheet.jsonl. Required binaries (python3/python) match the shipped Python scripts. No unrelated credentials or external services are requested.
Instruction Scope
Runtime instructions are narrowly scoped: read the local evidence JSONL and BibTeX, select anchors by pattern (digits, benchmark/dataset/metric keywords, limitation keywords), filter to citation keys present in the provided .bib, and write a JSONL output. The script reads/writes only workspace-local paths and enforces outline state checks; it does not access network resources or other system paths.
Install Mechanism
No install spec; this is instruction + bundled Python code. There is no remote download/install step and nothing is written outside the workspace paths the script accepts. Running requires Python available on PATH, which is proportionate.
Credentials
The skill requires no environment variables, no credentials, and no config paths. All file reads/writes are workspace-local and match the stated inputs/outputs. There are no requests for tokens/secrets or unrelated system config.
Persistence & Privilege
Flags: always:false and model invocation is allowed by default. The skill writes output and a local freeze marker (outline/anchor_sheet.refined.ok) within the workspace to prevent regeneration; it does not modify other skills or global agent config. No elevated privileges or persistent cross-skill changes are requested.
Assessment
This skill is internally consistent: it is a Python-based extractor that reads local evidence JSONL and a BibTeX file and writes an anchor_sheet JSONL. Before installing/running, review the bundled scripts if you want to be extra cautious (they run locally and modify files under the workspace). Ensure you only point the tool at workspaces you trust (it reads and writes files there and will create a local 'refined.ok' marker to freeze results). There is no network access, no external downloads, and no secrets requested, so the primary operational risk is accidental overwriting of files in the workspace — back up any important data beforehand.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.
Runtime requirements
Any binpython3, python