BC dual-thinking
Second-opinion consultation plus automatic skill-engineering escalation for reviews, rewrites, hardening, weak-model optimization, packaging, testing, and publish readiness.
As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, execution cost
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 0
✓ No critical or high findings
Files scanned: 30. 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") - warning
body-longSKILL.md body ≈ 22099 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 62/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 10Execution cost. Instruction body is 22099 tokens: crowds the task out of the window
- 40Result and completion. Does not say what the result is
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 100Steps. 323 steps
- 100Failures and branches. 81 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 29 top-level sections: this looks like several domains in one skill
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
- -31 of 1 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 173: enough signal without eating the budget
- +4Structure: 57 headings
- +3Step-by-step instructions: 323 items
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (5 of 23)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.
External checks
ClawHub: clean
This skill is broad and powerful for skill review workflows, but its file inspection, external consultation, patching, and validation behavior are disclosed and aligned with that purpose.
LLM: benign (high) · VirusTotal: · 29 May 2026