ClawHub

Chess Coach

v1.0.0

Interactive chess coaching based on Chess.com games and stats. Monitors a user's progress, analyzes recent games according to their skill level, and offers p...

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by@twharmon
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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medium confidence
✓
Purpose & Capability
Name/description match the actual behavior: the skill uses the Chess.com public API (api.chess.com) and a small helper script to fetch stats and games, and the documentation/references match the coaching objective.
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Instruction Scope
SKILL.md instructs the agent to fetch public Chess.com data, analyze games, and store state/observations in memory/chess_state.json and memory/chess_observations.jsonl. Writing those files and periodic monitoring are expected for this skill, but they are not declared in metadata; the instructions also rely on running scripts/chess_api.py which assumes a Python runtime and network egress to Chess.com.
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Install Mechanism
There is no install spec (instruction-only), which is low risk. However, the included scripts/chess_api.py depends on Python and the 'requests' package; the skill metadata does not declare required binaries or packages, so the runtime environment may need manual setup.
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Credentials
The skill requests no credentials or environment variables and only accesses Chess.com public endpoints. It stores only usernames and observation logs locally per SKILL.md; no sensitive credentials are requested.
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Persistence & Privilege
always:false (normal). The skill's SKILL.md allows periodic (background) monitoring and autonomous invocation (disable-model-invocation:false), which is expected for ongoing coaching but means the agent could perform repeated network calls and writes without explicit user action.
Assessment
This skill appears to do what it says: it fetches public Chess.com data and stores simple local state. Before enabling it, confirm you are comfortable with the agent making outbound requests to api.chess.com and writing files under memory/ (memory/chess_state.json, memory/chess_observations.jsonl). Ensure your runtime has Python and the 'requests' package installed or be prepared to install them. If you do not want background monitoring, keep autonomous invocation disabled or explicitly invoke the skill only when you want analysis.

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

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