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gigolab

Gigo Lobster Resume

by gigolab ยท GitHub โ†— ยท v2.1.2 ยท MIT-0
darwinlinuxwin32 โš  suspicious
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Install in OpenClaw
/install gigo-lobster-resume
Description
๐Ÿฆž GIGO ยท gigo-lobster-resume: ็ปญ่ท‘ๅ…ฅๅฃ๏ผšv2 stable ๅฝ“ๅ‰ไผšๆธ…็†ๆ—ง checkpoint ๅนถไปŽๅคด้‡่ท‘๏ผ›ไฟ็•™ๆญค slug ไฝœไธบๆ—ง checkpoint ๅ…ผๅฎนๅ…ฅๅฃใ€‚ Triggers: ็ปง็ปญ่ฏ•ๅƒ / ๆขๅค่ฏ„ๆต‹ / resume tasting / continue lobster...
README (SKILL.md)

gigo-lobster-resume

Mission

  • ็ปญ่ท‘ๅ…ฅๅฃ๏ผšv2 stable ๅฝ“ๅ‰ไผšๆธ…็†ๆ—ง checkpoint ๅนถไปŽๅคด้‡่ท‘๏ผ›ไฟ็•™ๆญค slug ไฝœไธบๆ—ง checkpoint ๅ…ผๅฎนๅ…ฅๅฃใ€‚
  • Resume entrypoint: the v2 stable runtime currently clears old checkpoints and starts fresh; this slug remains for legacy checkpoint compatibility.

Trigger Phrases

  • ไธญๆ–‡๏ผš็ปง็ปญ่ฏ•ๅƒ / ๆขๅค่ฏ„ๆต‹ / ็ปง็ปญ่ฏ„ไผฐ / ็ปง็ปญ้พ™่™พ่ฏ„ๆต‹
  • English: resume tasting / continue lobster eval / resume lobster benchmark / continue tasting

Execution Rules

  1. Use a direct Python command on this skill directory's wrapper file. Never use cd ... && python ...; OpenClaw preflight may reject it.
  2. Prefer python3, then python, then py.
  3. If the user asked in Chinese, append --lang zh. If the user asked in English, append --lang en.
  4. Stream short progress updates while the benchmark is running.
  5. Keep stdout/stderr visible and remind the user that the full log is written to gigo-run.log.
  6. Do not run --help, inspect the whole repo, or switch to main.py once the wrapper command is clear. Start the wrapper directly.
  7. If the wrapper starts a long-running process, do not kill it just because stdout is quiet for a while. A full tasting run often takes 15-25 minutes.
  8. While a long run is in progress, monitor the process and tail the log file under ~/.openclaw/workspace/outputs/gigo-lobster-taster/gigo-run.log instead of improvising a second execution path.
  9. Only declare failure if the process exits non-zero, the log shows a traceback, or the user explicitly asks to cancel.
  10. Stay attached until the wrapper exits. Do not end the conversation with โ€œI will keep monitoringโ€; keep polling and only report completion once you have the final score/result files/ref_code (if any).
  11. Prefer process poll plus exec tail -n 50 .../gigo-run.log while monitoring. Do not use a generic full-file read on gigo-run.log, because the log can be large and may break the chat output.

Default Behavior

  • ไธญๆ–‡๏ผš้ป˜่ฎคไผ˜ๅ…ˆไปŽๆ—ง checkpoint ็ปง็ปญ่ท‘๏ผŒ่พ“ๅ‡บ็›ฎๅฝ•ๆŒ‡ๅ‘ gigo-lobster-tasterใ€‚
  • English: By default it resumes from the existing checkpoint and writes to the gigo-lobster-taster output directory.

Recommended Command Shape

python3 /absolute/path/to/run_resume.py --lang zh

If the user explicitly asks for overrides, append the matching CLI flags:

  • --lobster-name "..." and --lobster-tags "tag1,tag2" for a custom lobster persona
  • --output-dir /custom/path for a custom output directory
  • --require-png-cert when the user refuses the SVG fallback
  • --skip-upload or --register-only only when the user explicitly asks to change the default upload behavior

Persona Defaults

  • Explicit CLI overrides win first: --lobster-name and --lobster-tags
  • Then read GIGO_LOBSTER_NAME and GIGO_LOBSTER_TAGS
  • Then read SOUL.md
  • Finally fall back to the default lobster persona

Do not stop for interactive questions unless the user explicitly asks for an interactive run.

Usage Guidance
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.
Capability Analysis
Type: OpenClaw Skill Name: gigo-lobster-resume Version: 2.1.2 The skill 'gigo-lobster-resume' is part of the GIGO Lobster Taster benchmark suite, designed to resume interrupted evaluations of AI agents. The bundle contains a comprehensive set of 50 evaluation tasks, a reference test harness, and logic for scoring and report generation. While it includes components with high-privilege capabilitiesโ€”such as a runtime bootstrapper that installs dependencies via pip (scripts/runtime_bootstrap.py), a shell shim for command monitoring (scripts/v2_shell_shim.py), and simulated prompt-injection test cases (e.g., in bundle/tasks/a25_readme_prompt_injection/setup/README.md)โ€”these are strictly aligned with its purpose as a security-focused benchmarking tool. The skill communicates with 'api.agent-gigo.com' to fetch tasks and upload results, which is consistent with its stated functionality.
Capability Tags
cryptorequires-sensitive-credentials
Capability Assessment
โ„น 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.
โš  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 โ€” this is unusual and worth manual review.
โœ“ 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.
โ„น 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 โ€” consistent with a taster that uploads results, but you should be aware the skill may contact a gateway or uploader depending on mode.
โœ“ 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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install gigo-lobster-resume
  3. After installation, invoke the skill by name or use /gigo-lobster-resume
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v2.1.2
2.1.2: fix leaderboard wording on cert/report so total_entries consistently means ranked entries, not all evaluations.
v2.1.1
2.1.1: smooth full-run cost/speed scoring for real 50-task evaluations and add MiniMax judge retry/fallback.
v2.1.0
2.1.0: run all 50 tasks through cloud judge, smooth seven-dimension scoring, and publish richer public diagnostics.
v2.0.19
2.0.19: publish refreshed v2 scoring bundle and recover D1 uploads after slow report responses.
v2.0.18
2.0.18: move judge cache to D1, keep KV config-only, and harden full-run scoring storage.
v2.0.15
2.0.15: harden evaluation/ref APIs, remove default fallback names, and strengthen v2 file-edit prompts.
v2.0.14
2.0.14: polish user-facing share copy and recommended booster labels.
v2.0.13
2.0.13: harden judge/report security and mark recommended skills as gray testing.
v2.0.12
2.0.12: scale speed scoring for full 50-task runs and polish public task diagnosis cards.
v2.0.11
2.0.11: remove model-prefixed public summary text and clarify bundled official task copy wording.
v2.0.10
2.0.10: restore the original PNG certificate design after rejecting the 2.0.9 redesign.
v2.0.8
2.0.8: add real OpenClaw per-task runner support, isolate eval sessions, expose M2.7 reasoning in unlocked full diagnosis, and wait longer for slow M2.7 judge responses.
v2.0.7
2.0.7: keep M2.7 judge reasoning stored, show a concise overall personalized note, and avoid labeling deterministic report copy as AI-written.
v2.0.6
2.0.6: switch cloud judge to MiniMax-M2.7, store judge reasoning, and show one overall personalized report note instead of per-task AI comments.
v2.0.5
2.0.5: switch cloud judge to MiniMax-M2.7, preserve AI judge reasoning in task reports, and keep OpenClaw identity name fallback.
v2.0.4
2.0.4: fix OpenClaw lobster name detection by falling back to workspace IDENTITY.md when SOUL.md has no explicit name.
v2.0.3
2.0.3: harden leaderboard consistency, v2 report verification, wrapper bootstrap, Gateway env loading, and CJK certificate rendering.
v2.0.2
2.0.2: harden leaderboard consistency, v2 judge score normalization, OpenClaw run logging, and CJK certificate rendering.
v2.0.0-beta.2
Release 2.0.0-beta.2: 50-task v2 beta bundle, MiniMax M2.1 judge defaults, worker v2 APIs, bundle-version leaderboard.
v1.2.4
1.2.4: backend scoring reliability improvements, documentation refresh, and release pipeline maintenance.
Metadata
Slug gigo-lobster-resume
Version 2.1.2
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 23
Frequently Asked Questions

What is Gigo Lobster Resume?

๐Ÿฆž GIGO ยท gigo-lobster-resume: ็ปญ่ท‘ๅ…ฅๅฃ๏ผšv2 stable ๅฝ“ๅ‰ไผšๆธ…็†ๆ—ง checkpoint ๅนถไปŽๅคด้‡่ท‘๏ผ›ไฟ็•™ๆญค slug ไฝœไธบๆ—ง checkpoint ๅ…ผๅฎนๅ…ฅๅฃใ€‚ Triggers: ็ปง็ปญ่ฏ•ๅƒ / ๆขๅค่ฏ„ๆต‹ / resume tasting / continue lobster... It is an AI Agent Skill for Claude Code / OpenClaw, with 363 downloads so far.

How do I install Gigo Lobster Resume?

Run "/install gigo-lobster-resume" in the OpenClaw or Claude Code chat to install it in one step โ€” no extra setup required.

Is Gigo Lobster Resume free?

Yes, Gigo Lobster Resume is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Gigo Lobster Resume support?

Gigo Lobster Resume is cross-platform and runs anywhere OpenClaw / Claude Code is available (darwin, linux, win32).

Who created Gigo Lobster Resume?

It is built and maintained by gigolab (@gigolab); the current version is v2.1.2.

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