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Llm Data Automation

v2.1.0

Automate construction data processing using LLM (ChatGPT, Claude, LLaMA). Generate Python/Pandas scripts, extract data from documents, and create automated p...

⭐ 0· 854·4 current·5 all-time
by@datadrivenconstruction
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
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Benign
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high confidence
✓
Purpose & Capability
Name/description match the instructions and manifest: examples show generating Python/Pandas code, extracting PDFs, processing CSV/Excel/BIM exports. Declared requirement (python3) and filesystem permission are appropriate for a file-processing, code-generation skill.
✓
Instruction Scope
SKILL.md instructs the agent to gather user-provided data/files, generate or run Python code (pandas, pdfplumber), and optionally use local LLMs (Ollama/LM Studio) or online LLMs. All referenced actions are within the stated purpose and limited to user-supplied data; there are no instructions to read unrelated system files or to exfiltrate data to hidden endpoints.
✓
Install Mechanism
There is no install spec (instruction-only). The skill recommends third-party tools (Ollama, LM Studio) but does not bundle downloads or run installers itself. This is low-risk as long as the user installs those tools from official sources.
✓
Credentials
The skill requests no environment variables or credentials. The only declared permission is filesystem access in claw.json, which is appropriate for reading/writing user-supplied data files. No unrelated secrets or config paths are requested.
✓
Persistence & Privilege
always is false and the skill is user-invocable with normal autonomous invocation allowed. It does not request persistent special privileges or attempt to modify other skills or system-wide settings.
Assessment
This skill appears coherent with its stated purpose, but take these precautions before installing or using it: - Note the OS restriction: metadata lists win32; ensure it matches your environment. - The skill expects python3 and filesystem access; make sure you run generated scripts in a safe environment (virtualenv/container) and review LLM-generated code before executing it — LLMs can produce buggy or unsafe commands. - The SKILL.md suggests using online LLM services (ChatGPT/Claude) or installing local tools (Ollama, LM Studio). Avoid sending sensitive or proprietary project data to online models unless you trust the provider and understand data retention policies. - If you install third-party LLM runtimes, download them only from official sites and verify checksums where available. - The skill does not ask for API keys or other credentials, which reduces exfiltration risk; nevertheless, monitor any prompts that ask you to paste credentials into the tool or chat. If you want a stricter safety posture, run the skill's workflows in an isolated VM/container and review all generated scripts before running them.

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.
Termshttps://spdx.org/licenses/MIT-0.html

Runtime requirements

🐼 Clawdis
OSWindows
Binspython3

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