Security audit
Security checks for vulnerabilities and agentic risk
This skill is a structured learning-output helper with a disclosed local handoff file, but no evidence of hidden code execution, credential access, exfiltration, or destructive behavior.
Before installing, be aware that generated learning outputs are intended to be saved at ~/.openclaw/swarm_tmp/expert_output.json and may be read by later audit skills. Avoid using it for private or sensitive topics unless you are comfortable with that local persistence.
This markdown file includes example invocations using generic verbs like "learn:" and "study:" without explaining whether these are the only trigger phrases, what contexts activate the skill, or what inputs are out of scope. Such broad wording overlaps with common user requests and could cause unintended or ambiguous skill invocation.
The instruction to persist output at a stable path in the user's home directory introduces session-to-session state sharing and makes the data referenceable by subsequent skills. In this context, the chaining requirement increases risk because later skills may trust or consume this file without the user's awareness, enabling covert data retention or cross-skill influence.
#### Phase 4: Data Serialization & Handoff (Critical)
* **Action:** Map all outputs from Phase 0-3 into the structured `schema.json` format.
* **Integrity Check:** The resulting JSON **MUST** pass structural validation.
* **Persistence:** Write the final JSON to `~/.openclaw/swarm_tmp/expert_output.json`.
### 3. Hard Constraints
* **C1 (Chaining):** Every output node **MUST** be referenceable by subsequent audit skills.
The skill explicitly instructs the agent to write data into a fixed path under the user's home directory without any user consent, transparency, or scoping to a safe temporary workspace. This creates unauthorized filesystem side effects and could enable silent persistence of potentially sensitive or misleading data for later reuse by other skills.
The README presents the skill in both Chinese and English, but it does not state how the skill chooses a response language or whether the user can opt in to a preferred locale. This can create a language/locale policy concern because the skill's language behavior is not explicitly user-directed.
The skill forces a rigid output format and forbids normal prose without obtaining user opt-in, which can override expected interaction patterns and reduce transparency. While not directly a code-execution risk, it can suppress warnings, context, or safety caveats that would otherwise be presented to the user.
No suspicious patterns detected.