Robust Agent Design
v1.0.0Apply robust Agent design patterns for building fault-tolerant, state-driven automation systems. Use when designing or refactoring systems that require high...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
Capability signals
These labels describe what authority the skill may exercise. They are separate from suspicious or malicious moderation verdicts.
OpenClaw
Benign
high confidencePurpose & Capability
The name/description (robust agent design, fault tolerance, compensation patterns) matches the SKILL.md content and the included Python template/example files. The code and prose focus on state-driven agents, retries, compensation transactions, and persistence — all coherent with the stated purpose.
Instruction Scope
SKILL.md is a design/instruction document and does not instruct the agent to read unrelated host files, call external endpoints, or collect hidden data. The included code examples implement state persistence and compensation logic but do not send data to external services (EmailService/NotificationService are simulated in-memory).
Install Mechanism
There is no install specification; this is instruction-only with example code files. No packages are downloaded or executed automatically as part of installation.
Credentials
The skill requests no environment variables or credentials (good). One design choice to note: the Python template defaults to file-based state persistence and writes a state file under /tmp/agent_<uuid>.state. Persisted state contains metadata and an input checksum (not raw input), but you should consider whether file persistence is acceptable for your environment and whether state files could expose sensitive metadata.
Persistence & Privilege
The skill does not request elevated privileges and always:false. It does write local state files by default (under /tmp) and maintains in-memory logs in examples. This is expected for agent state persistence, but you may want to configure state_persistence to a secure location or memory-only mode before using in production.
Assessment
This skill appears to be legitimate instructional material with working Python templates. Before using in production: (1) review and test the provided Python code yourself; (2) configure state persistence (avoid leaving sensitive state on disk — use encrypted storage or memory if appropriate); (3) remove or control any simulated randomness and test-only failure rates (e.g., random failures in examples); (4) audit any compensation actions you add to ensure they don't perform unsafe side effects (external calls, destructive operations); and (5) run examples in an isolated environment until you are satisfied with behavior.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.