SKILLEMALL.ai

BC wdp-work-mgr

长时/批量脚本开发与任务跟踪:目标规划、TODO、逐任务验证标记、进度可见、断点续跑、并发受控、失败日志可见。生成的批处理脚本会嵌入可恢复机制(checkpoint 断点续跑、失败日志、并发限流、错误重试、信号处理)并读写工作目录文件。检测到「处理大量文件/长任务」时启用;发现已有 work/state.json 时先报进度再续跑。

ClawHub Agent Skills author: wdp v1.0.1 MIT-0 7 files body ≈ 337 tokens Open the sourceclawhub.ai analyzed 4 d ago

长时/批量脚本开发与任务跟踪:目标规划、TODO、逐任务验证标记、进度可见、断点续跑、并发受控、失败日志可见。生成的批处理脚本会嵌入可恢复机制(checkpoint 断点续跑、失败日志、并发限流、错误重试、信号处理)并读写工作目录文件。检测到「处理大量文件/长任务」时启用;发现已有 work/state.json…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedurePersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
C
51/100
Has gaps
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: 7. 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 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
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (wdp-work-mgr) differs from the folder (wdp-script-gen)
  • 100Tools and files. No external tools needed
  • 100Steps. 16 steps
  • 100Execution cost. Instruction body is 337 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress

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
  • +4No input/output examples
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 167: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 16 items
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: clean
This skill is a disclosed batch-work manager that writes scoped progress, logs, checkpoints, and generated batch-script helpers for long-running file tasks.
LLM: benign (high) · VirusTotal: · 11 Aug 2026