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Purpose & Capability
The skill name/description (resume a previous 'lobster' benchmark run) aligns with the provided wrapper scripts (run_resume.py) and the large bundled evaluation harness. The bundle is large (full taster/harness/judge scaffolding) which is expected for a benchmark suite, though heavier than a minimal 'resume' helper.
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Instruction Scope
SKILL.md instructs the agent to run the repository wrapper (python run_resume.py), tail logs under ~/.openclaw/workspace/outputs/..., keep stdout/stderr visible, and stay attached while long runs execute. It also references and suggests reading SOUL.md and several optional env vars. The runtime instructions include prompt-injection-like constructs (pre-scan found 'ignore-previous-instructions' and unicode-control-chars) which could be attempting to influence agent behavior. The instructions also explicitly disallow inspecting the repo or switching to main.py — this is unusual and worth manual review.
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Install Mechanism
No external install/download step is included; code is packaged in the bundle and no remote URLs or extraction steps are declared. That lowers install-time risk compared to fetching arbitrary code at install time.
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Credentials
Declared requirements are just a Python binary (python3/python/py), which fits the CLI wrapper usage. However SKILL.md and README reference several environment variables (e.g., GIGO_LOBSTER_NAME, GIGO_UPLOAD_MODE, GIGO_REQUIRE_PNG_CERT) and a local gateway; none of these are declared in requires.env. Also the bundle contains code (gateway_client.py, judge_client.py, score_uploader.py) that performs outbound HTTP requests — consistent with a taster that uploads results, but you should be aware the skill may contact a gateway or uploader depending on mode.
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Persistence & Privilege
The skill is not marked always:true and does not request to modify other skills' configurations. It runs as an invoked local CLI tool and monitors a long-running process; that extended runtime is normal for this use-case but increases exposure while running.
Scan Findings in Context
[ignore-previous-instructions] unexpected: This pattern appeared in SKILL.md pre-scan. The skill's runtime instructions should be clear but should not include constructs that try to override or ignore prior agent policies; this looks like a prompt-injection signature and deserves caution.
[unicode-control-chars] unexpected: Unicode control characters were flagged in the SKILL.md pre-scan. Such characters can be used to obfuscate or manipulate how content is interpreted by the agent or UI; they are not expected in benign user-facing instructions.
What to consider before installing
What to check before installing/running:
- Manual inspection: open run_resume.py, scripts/score_uploader.py, scripts/gateway_client.py, and run_resume.py's CLI logic. Search the bundle for 'requests.post' or other outbound network calls and for any hard-coded remote hosts.
- Modes & uploads: the skill can upload results depending on the run mode. If you don't want any network activity, run with local-only flags (e.g., --skip-upload, or use gigo-lobster-local) and/or run gigo-lobster-doctor first.
- Secrets & scope: do not run this in an environment with sensitive credentials mounted/available if you haven't confirmed where the code will send data. The SKILL.md references environment variables (GIGO_*) that are optional; the bundle does not declare them as required but code may read them.
- Prompt-injection signs: SKILL.md contained prompt-injection-like patterns and unusual instructions (e.g., 'do not inspect the repo' and control characters). Treat those as a red flag: prefer to run the wrapper locally in an isolated VM/container if you proceed.
- Safer test: run the doctor mode and a local run (no upload) first, and inspect the outputs (gigo-run.log, lobster-report.html). If you plan to resume a prior run, inspect the checkpoint files to understand what state will be re-used.
If you want, I can (a) scan run_resume.py and the uploader/gateway files for outbound endpoints and ENV reads, or (b) produce concrete grep commands to help you find network calls and env reads in the bundle.
