Manage and execute all Epistemic Council pipeline operations including status checks, validations, audits, and adversarial re-challenges.

What to check before installing/running this skill: - Runtime dependencies: The code expects a local LLM (Ollama) reachable at http://localhost:11434 and uses the 'requests' library. Make sure you have the model service and Python dependencies installed in a controlled environment before running. The SKILL.md does not declare these requirements. - Filesystem path: SKILL.md runs Python from /root/.openclaw/workspace-epistemic-council-bot/epistemic_council. Confirm you are comfortable granting read/write access to that path (it will create/modify epistemic.db, memory/, heartbeat-state.json, openclaw-runs/). If you run as a non-root user, update the path or run in a sandbox/container. - Inspect skill_downloader.py and any omitted files: downloader scripts can fetch code at runtime. If present and not clearly benign, review its contents to ensure it doesn't pull code from arbitrary external servers. - Network endpoints: The visible code posts to a localhost model endpoint by default. If you override model_url or code uses other endpoints in omitted files, review them to ensure no unexpected external exfiltration (the provided excerpts do not show external third-party endpoints). - Code correctness / runtime errors: There are small inconsistencies in the code excerpts (e.g., some agent classes expect parameters but are sometimes instantiated without them), which could cause runtime exceptions. Test the skill in a disposable environment first. - Least-privilege test: Run the skill in a sandbox or container with limited filesystem and network access to observe behavior before granting it access to your main environment. If you want, I can: (1) list the omitted files for review, (2) show the contents of skill_downloader.py, or (3) point out the exact lines where constructor/argument mismatches appear so you can patch them before running.

Type: OpenClaw Skill Name: epistemic-council Version: 1.0.0 The skill is classified as suspicious due to its broad `shell: true` permission and the use of `subprocess.run` to execute other Python scripts, as well as the inherent prompt injection surface against the local LLM. While the implementation attempts to mitigate direct shell injection by using list-form arguments for `subprocess.run` (defaulting to `shell=False`), and network access is strictly `localhost_only` (preventing direct exfiltration), the power granted by `shell: true` remains a significant vulnerability risk. An attacker might exploit complex interactions or subtle flaws in argument parsing to achieve unauthorized execution or manipulate the LLM's reasoning, leading to compromised data integrity within the `epistemic.db` or `agent_learning.json` files.