AC skill-quality-scorer
Deterministic TRACE+ quality scorer for Agent Skills (SKILL.md): six dimensions T-R-F-S-I-E, 30 sub-items, merges TRACE, good-skill authoring reverse-rubric (Extra08/skill-creator), and ClawHub meta-skills (skill-reviewer, skill-quality-audit). Use when scoring, rating, auditing, or comparing skills; or says「给技能打分」「技能评分」「和同类 skill 差在哪」「是否符合规范」. Runs static_audit.py first; outputs JSON + Markdown. Not TRACE-only.
Deterministic TRACE+ quality scorer for Agent Skills (SKILL.md): six dimensions T-R-F-S-I-E, 30 sub-items, merges TRACE, good-skill authoring reverse-rubric…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- 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 · 2
✓ No critical or high findings
Medium and low: 2
-
medium Dangerous commands
cmd-pipe-to-shellreferences/scoring-engine-deterministic.md:92Downloads and executes remote code from an unrecognised host (pipe to shell) (documentation of a security skill)| T1 | 无远程任意执行 | 无 curl\|bash 等 | 网络读文档不执行 | 远程代码执行 |
security skill -
low Dangerous commands
cmd-pipe-to-shellexamples/sample-score-v2.json:11Downloads and executes remote code from an unrecognised host (pipe to shell) (string literal in code, not executed; test fixture / example file)"T1_n…xec": { "score": 2, "evidence": "无 curl|bash；static_audit 无 critical", "source": "static" },code literalfixture
Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 53/100
- 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
- 100Tools and files. No external tools needed
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 615 tokens
- 100Running it twice. No mutating operations
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 415: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.