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CD crabbox

Use the Crabbox wrapper for validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.

openclaw/clawsweeper Agent Skills author: openclaw 1 file body ≈ 9 870 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Use the Crabbox wrapper for validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof.

As a process D 48/100 · Unfinished process — References files that are not bundled: scripts/e2e/*-docker.sh

IntegrationDockerAWSGitHubInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
99
Quality 40%
68
Run on models
none yet
Process rating
D
48/100
Unfinished process
References files that are not bundled: scripts/e2e/*-docker.sh
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
30
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  2. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration net-credential-use SKILL.md:794
    Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
    `printf '%s' "$CRABBOX_COORDINATOR_TOKEN" | crabbox login --url https://crabbox.openclaw.ai --provider aws --token-stdin`.
    quoted

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 9870 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: scripts/e2e/*-docker.sh

Process rating: all ten parameters 48/100

Will not run. References files that are not bundled: scripts/e2e/*-docker.sh
  • 0Tools and files. 1 referenced file(s) missing: scripts/e2e/*-docker.sh
  • 0Result and completion. Does not say what the result is
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 25 mutating operations with no state check
  • 40Execution cost. Instruction body is 9870 tokens: crowds the task out of the window
  • 60Steps. 106 steps, 4 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 7 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • low 15 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (12 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 159: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 106 items
  • +4Has examples (33 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.