BD zero-cover-mode
零稀泥模式：bug修复→自动生成测试+跑全量回归+标注根因+周报+重复bug重构警报
零稀泥模式：bug修复→自动生成测试+跑全量回归+标注根因+周报+重复bug重构警报
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
IntegrationSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
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: 31. 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 ≈ 5971 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 49/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 6 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, git, python) that frontmatter does not declare
- 70Execution cost. Instruction body is 5971 tokens
- 100Steps. 80 steps
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- low 14 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (7 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)
- +3Description length 42: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -4Absolute local paths (C:\Users, /home/…): not portable
- +1No license
- +2Single-language instructions
- +4Structure: 53 headings
- +3Step-by-step instructions: 80 items
- +4Has examples (34 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.
External checks
ClawHub: suspicious
This bug-fixing automation skill has a coherent purpose, but it asks agents to run live code against real project systems and make persistent or destructive local changes without enough guardrails.
LLM: suspicious (high) · 5 Jul 2026