Story generation pipeline skill

Story generation pipeline skill. Supports multi-episode continuous generation, graph management, AI quality check + human confirmation dual control mechanism...

This skill appears to be a self-contained, local story-generation pipeline that stores state and graphs under data/. Before installing or running it, consider the following: - The documentation and code disagree in two places: (1) SKILL.md mentions graph API calls but the code uses local JSON files; (2) SKILL.md says AI retry is capped at 3, but pipeline.py allows unlimited retries. Decide which behavior you expect and inspect/modify the code to enforce desired limits or change storage behavior. - The skill will create and modify files under data/ (pipeline_state.json and data/graphs/*.json). If you run it in an environment with sensitive files, ensure the working directory is isolated and not containing secrets. - No network endpoints or credentials are required by the included code, which reduces remote exfiltration risk. However, the skill bundles runnable Python code — review the files for any modifications you’d want (e.g., add retry caps, explicit user prompts, or logging controls) before enabling autonomous invocation. - Source is unknown. If you plan to use this in production or with sensitive inputs, request provenance from the author or run it in a sandbox first. If you want strict behavior (e.g., limit AI retries, or use a remote graph service), update the code to make that explicit and documented.

Type: OpenClaw Skill Name: story-pipeline Version: 1.0.4 The story-pipeline skill bundle is a well-structured framework for multi-episode story generation, featuring automated quality checks and human-in-the-loop confirmation. The Python scripts (pipeline.py, graph_manager.py, ai_reviewer.py) handle local state persistence and prompt construction for creative writing tasks without any evidence of data exfiltration, unauthorized network access, or malicious execution. While the documentation mentions remote APIs, the implementation is strictly local and focused on its stated purpose.