BC command-center
Mission control dashboard for OpenClaw - real-time session monitoring, LLM usage tracking, cost intelligence, and system vitals. View all your AI agents in one place.
Mission control dashboard for OpenClaw - real-time session monitoring, LLM usage tracking, cost intelligence, and system vitals.
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 5
✓ No critical or high findings
Medium and low: 5
-
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:79High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…cjV+2Z+GK+EEY7…R4x+N3TA…nIr+TMcC…z6Q==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:151High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha512-oGB+Uxlg…BKZ+GTy0…lih/NSHS…cSg==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:209High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…JbG+vnnQ…1TK+nxAp…hhm+kzE4…g4g==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:259High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…FFX/+gVeY…NlM++NqRc…bqg==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:360High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…vZS+VIDU…jX4+qx9M…saQ==",
detector
Files scanned: 45. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 10 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 408 tokens
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
- -2localhost URLs: will not work for another user
- -43 reference files, but SKILL.md never points to them: the model will not open them
- -38 of 8 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 166: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.