SKILLEMALL.ai
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BC SPM

Production-grade software project development skill for OpenClaw. WBS hash attestation, auto context injection, session recovery, parallel task pointers, and Minimal Mode. Use when starting new projects, implementing complex features, or executing multi-step development tasks that require structured end-to-end management.

ClawHub Agent Skills author: zhbcher v3.0.0 MIT-0 80 files · 11 scripts body ≈ 6 058 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureWriting and documentsAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. 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: 65. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning body-long SKILL.md body ≈ 6058 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 61/100

  • 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. 16 mutating operations with no state check
  • 70Execution cost. Instruction body is 6058 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 108 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 18 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
  • -5TODO / placeholder text left in the skill
  • -217 emoji in the instructions: noise for the model
  • -35 of 14 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 323: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 108 items
  • +4Has examples (14 code blocks)
  • +4Reference files are cited in the instructions (7 of 12)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: clean
This appears to be a legitimate project-management coding skill, but it has broad project automation powers that users should supervise.
LLM: benign (medium) · VirusTotal: · 29 May 2026