feedback-loop-fine-tuner
v1.0.0Provides tools for implementing feedback loops to fine-tune LLM agents using user feedback for continuous personalization and improvement, including training...
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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
The name/description (feedback-loop fine-tuner) matches the included SKILL.md and index.js: the code implements feedback collection, aggregation, dataset generation (jsonl/openai/llama/alpaca), preference-pair generation, prompt optimization, and metrics tracking. One note: the skill describes 'fine-tuning' and 'RLHF' workflows but the implementation focuses on data preparation and analysis (no built-in training calls or cloud upload). That is a legitimate design choice for a local library, but users expecting automated model training integrations should not assume those are present.
Instruction Scope
SKILL.md instructions are narrowly scoped to collecting feedback, generating datasets, optimizing prompts, tracking metrics, and running A/B tests. They do not instruct reading arbitrary system files, contacting external endpoints, or accessing environment variables beyond what the module exposes. The example usage assumes requiring the module from a local path, which is normal for a Node library.
Install Mechanism
No install spec is provided (instruction-only plus a local index.js), so nothing will be downloaded or installed by the platform. The package.json is minimal and the code is included in the bundle. This is low-risk from an install/execution vector perspective.
Credentials
The skill declares no required environment variables, credentials, or config paths and the code does not reference process.env or external secrets. That matches the stated purpose (local data processing) and is proportionate.
Persistence & Privilege
The skill does not request always:true or other privileged persistent presence. It keeps feedback in an in-memory store (feedbackStore) and provides export functions; it does not modify other skills or system-wide agent settings. Autonomous invocation is allowed by platform default but there's no additional persistence or privilege escalation requested by the skill.
Assessment
This skill appears to do what it says: local collection, analysis, and formatting of user feedback for dataset preparation. Before installing or using it, consider: (1) Privacy — the skill will aggregate user interactions and can export datasets (JSON/CSV/jsonl) that may include PII or sensitive conversation content; ensure you filter or redact data before training or sharing. (2) Scope — the module prepares data but does not perform model training or upload to external services, so plan how/where you'll run fine-tuning or RLHF steps. (3) Code review — although included code shows no network calls or secret access, review the full (non-truncated) index.js to confirm there are no hidden endpoints or telemetry. (4) Test in a sandboxed environment and enforce policies about what feedback may be captured (e.g., do not collect credentials). If you need automatic cloud training integrations, prefer a skill that explicitly requests and documents the required credentials and endpoints.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.