Skill: h4444433333/skill-graph-for-analogical-reasoning (version 1.0.4)

--- name: grap-skill description: Skill Graph for Analogical Reasoning. Build a graph over SKILL.md skills, choose one primary skill, and retrieve complementary support instead of naively attaching top similar skills. user-invocable: true metadata: {"openclaw":{"requires":{"bins":["python3"]}}} --- # Skill Graph for Analogical Reasoning Use this skill when the user wants to work with a **graph skill** over local `SKILL.md` folders. This skill is for: - graph-based skill indexing - analogical reasoning and transfer - one primary skill plus complementary support - avoiding naive "top similar skills" retrieval The full Python package and CLI for this project are available in the GitHub repository. ## What this skill does This skill wraps the `grap-skill` capability. It supports three primary commands: - `/grap-skill build` - `/grap-skill query` - `/grap-skill look` ## Core rule - `build` is the only command that may scan skill folders and update the graph. - `query` and `look` are read-only against an existing `graph.json`. - Do not silently rebuild during `query`. ## Code-backed usage This skill is allowed to call code. The `python3 {baseDir}/scripts/run_grap_skill.py ...` commands below are the stable execution entrypoints intended for the skill runtime and for model-driven tool use. They let the skill call a controlled wrapper inside the installed skill bundle instead of relying on ad hoc shell reconstruction. Preferred execution order: 1. Use the helper script in `{baseDir}/scripts/run_grap_skill.py`. 2. If the Python package is installed in the current interpreter, the wrapper may use `python -m auto_grap_skill`. 3. If neither bundled code nor the local Python package is available, direct the user to the GitHub repository for this project and install the Python package first. ## Primary commands ### `/grap-skill build` ```bash python3 {baseDir}/scripts/run_grap_skill.py build --source <skills_dir> --output <graph_dir> ``` Example: ```bash python3 {baseDir}/scripts/run_grap_skill.py build --source ./skills-main --output ./.grap-skill ``` ### `/grap-skill query` ```bash python3 {baseDir}/scripts/run_grap_skill.py query "<task text>" --graph <graph_json_path> ``` Example: ```bash python3 {baseDir}/scripts/run_grap_skill.py query "edit a docx file with comments" --graph ./.grap-skill/graph.json ``` ### `/grap-skill look` ```bash python3 {baseDir}/scripts/run_grap_skill.py look --graph <graph_json_path> --output <html_path> ``` Example: ```bash python3 {baseDir}/scripts/run_grap_skill.py look --graph ./.grap-skill/graph.json --output ./.grap-skill/graph-look.html ``` ## How to interpret results - `primary_skill` is the execution center. - Always treat `primary_skill` as the default lead. Use a `supporting_skill` only if it adds clear missing coverage and does not conflict with or dilute the primary skill. If a suggested supporting skill is not actually helpful for the current task, do not adopt it. - `supporting_skills` are the only skills that should normally supplement the primary skill in context. - `fallback_skills` are substitutes for failure paths. - `similar_skills` are for graph browsing, not default prompt context. ## Why this is different Most retrieval systems stop at similarity. This one tries to separate: - the skill that should lead execution - the skills that add complementary coverage - the skills that are merely nearby or redundant That is the whole analogical-reasoning goal of this project. ## More files in this skill - `README.md` — human-facing usage and install notes - `reference.md` — algorithm and result interpretation - `scripts/run_grap_skill.py` — helper wrapper that calls the actual Python package/CLI ## GitHub version The GitHub release of this project should also point users back to this skill bundle for skill-style installation: - ClawHub/OpenClaw install: `openclaw skills install skill-graph-for-analogical-reasoning` - GitHub/Python install: use the repository's Python package and local CLI when a full source checkout is preferred


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)