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BC b2b-pm-workbench-international

【B2B PM Super Workbench】 —— A full-stack intelligent workbench for B2B (enterprise) product managers. Integrates 50+ methodology frameworks, 30+ standard deliverables, 12 editable chart types, 3 interactive prototype types, 5 PPT types, and full-stack AI product design capabilities.

ClawHub Agent Skills author: yinjianheng v1.2.0-intl MIT-0 19 files body ≈ 19 967 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

【B2B PM Super Workbench】 —— A full-stack intelligent workbench for B2B (enterprise) product managers.

As a process C 59/100 · Has gaps — weak spots: when it triggers, execution cost, running it twice

ProcedureOperations and projectstype 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%
99
Quality 40%
66
Run on models
none yet
Process rating
C
59/100
Has gaps
Progress reporting w 2
0
Execution cost w 6
10
When it triggers w 12
20
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.
  2. 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Risky intent intent-offensive-security references/complete-product-lifecycle.md:799
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
    | **Safety** | Harmful content rate | Auto scanning + red team testing |
    detector

Files scanned: 19. 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")
  • warning body-long SKILL.md body ≈ 19967 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "language"
  • note frontmatter-key unknown frontmatter key "contact"

Process rating: all ten parameters 59/100

  • 0Progress reporting. Says nothing while it works
  • 10Execution cost. Instruction body is 19967 tokens: crowds the task out of the window
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 12 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Steps. 33 steps, 5 vague phrases
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Result and completion. Output format and completion criterion are stated
  • 100Consistency. Name and required fields are in place
  • low 38 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)
  • -253 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 283: enough signal without eating the budget
  • +4Structure: 155 headings
  • +3Step-by-step instructions: 33 items
  • +3Output format is stated explicitly
  • +4Has examples (49 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +1License stated

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

External checks

ClawHub: clean
This is a documentation-heavy B2B product management skill with no executable code or hidden data access, though users should be careful with enterprise data when following its AI-tool and interview guidance.
LLM: benign (high) · VirusTotal: · 10 Jul 2026