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BC openclaw-workspace-pro

Production-ready workspace setup for OpenClaw agents. Implements artifact workflows, secrets management, memory compaction, and long-running agent patterns based on OpenAI's Shell + Skills best practices. One-command installation transforms your workspace into a production-ready environment.

modbender/skill-library-mcp Agent Skills author: modbender MIT 9 files · 1 script body ≈ 1 382 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Production-ready workspace setup for OpenClaw agents.

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

ProcedureAI 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
85/100
safety, quality, tests
Safety 60%
96
Quality 40%
69
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
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 · 4

✓ No critical or high findings

Medium and low: 4
  • low Secrets in code secret-password-literal README.md:100
    Hard-coded password / key literal (may be an example) (placeholder value)
    API_KEY=sk-a…...  ❌ Plaintext, exposed
    placeholder
  • low Secrets in code secret-password-literal README.md:106
    Hard-coded password / key literal (may be an example) (placeholder value)
    API_KEY=sk-a…...
    placeholder
  • low Secrets in code secret-password-literal SKILL.md:100
    Hard-coded password / key literal (may be an example) (placeholder value)
    API_KEY=sk-a…...  ❌ Plaintext, exposed in git
    placeholder
  • low Secrets in code secret-password-literal SKILL.md:106
    Hard-coded password / key literal (may be an example) (placeholder value)
    API_KEY=sk-a…...
    placeholder

Files scanned: 9. 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")
  • note frontmatter-key unknown frontmatter key "repository"

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 30Running it twice. 6 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 41 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1382 tokens
  • low 12 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 292: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 41 items
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.