SKILLEMALL.ai

BD coding-agent

通用的编码代理技能,封装 Codex、Claude Code、OpenCode 等工具。 使用场景:(1) 构建/创建新功能或应用 (2) 重构大型代码库 (3) Bug 修复 (4) 代码审查 (5) 迭代式编码。 不适用于:简单的单行修改(直接用 edit),读取代码(用 read 工具)。

ClawHub Agent Skills author: whisky v1.0.0 MIT-0 6 files body ≈ 1 089 tokens Open the sourceclawhub.ai analyzed 2 d ago

通用的编码代理技能,封装 Codex、Claude Code、OpenCode 等工具。 使用场景:(1) 构建/创建新功能或应用 (2) 重构大型代码库 (3) Bug 修复 (4) 代码审查 (5) 迭代式编码。 不适用于:简单的单行修改(直接用 edit),读取代码(用 read 工具)。

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

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
  • 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
  • 40Consistency. Frontmatter name (coding-agent) differs from the folder (coding-agent-common)
  • 100Tools and files. No external tools needed
  • 100Steps. 15 steps
  • 100Execution cost. Instruction body is 1089 tokens
  • 100Running it twice. No mutating operations
  • low 13 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
  • -215 emoji in the instructions: noise for the model
  • -43 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 148: enough signal without eating the budget
  • +4Structure: 32 headings
  • +3Step-by-step instructions: 15 items
  • +4Has examples (17 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: suspicious
This instruction-only coding-agent skill is coherent, but it prominently normalizes permission-bypass, auto-approval, background execution, and PR publishing workflows that can change real projects without enough user gating.
LLM: suspicious (high) · VirusTotal: · 29 May 2026