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BB greenhelix-enterprise-agent-commerce

Enterprise Agent Commerce Playbook: Fortune 500 Adoption Guide for Autonomous B2B Transactions. Comprehensive enterprise playbook for adopting agent commerce: C-suite business case, procurement integration, compliance framework (SOC2/GDPR/EU AI Act), vendor risk assessment, multi-vendor strategy, identity federation, audit requirements, SLA design, cost modeling, phased rollout, organizational change management, and ROI measurement.

ClawHub Agent Skills author: mirni v1.3.1 MIT-0 2 files body ≈ 43 387 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 66/100 · Nearly there — weak spots: result and completion, when it triggers, execution cost

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
82/100
safety, quality, tests
Safety 60%
96
Quality 40%
60
Run on models
none yet
Process rating
B
66/100
Nearly there
Execution cost w 6
10
When it triggers w 12
20
Result and completion w 14
40
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 · 4

✓ No critical or high findings

Medium and low: 4
  • low Risky intent intent-offensive-security SKILL.md:314
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | S-8 | CC7.3: Remediation of identified vulnerabilities | Quarterly agent security review | Penetration test agent endpoints, review decision logic for manipulation | [ ] |
  • low Risky intent intent-offensive-security SKILL.md:494
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    **2. Security posture (weight: 20%).** SOC2 Type II certification, penetration test results, vulnerability management program, incident response history. Score 5 for vendors with current SOC2 Type II
  • low Risky intent intent-offensive-security SKILL.md:604
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | Penetration testing | 3% | No pen testing | Annual pen test | Quarterly pen test by reputable firm, findings shared with customers | ___ | Pen test summary report |
  • low Risky intent intent-offensive-security SKILL.md:719
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
    card.add_score("Compliance", "Penetration testing", 3, 4)
    quoted

Files scanned: 2. 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 ≈ 43387 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "type"
  • note frontmatter-key unknown frontmatter key "price_usd"
  • note frontmatter-key unknown frontmatter key "content_type"
  • note frontmatter-key unknown frontmatter key "executable"
  • note frontmatter-key unknown frontmatter key "install"
  • note frontmatter-key unknown frontmatter key "credentials"

Process rating: all ten parameters 66/100

  • 10Execution cost. Instruction body is 43387 tokens: crowds the task out of the window
  • 20When it triggers. No condition that starts the skill
  • 40Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 325 steps
  • 100Failures and branches. 3 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 15 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
  • +2Single-language instructions
  • +3Description length 436: enough signal without eating the budget
  • +4Structure: 82 headings
  • +3Step-by-step instructions: 325 items
  • +4Has examples (16 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: clean
This is a documentation-only enterprise playbook; its sensitive examples are relevant to the topic but should be treated as implementation guidance, not ready-to-run code.
LLM: benign (high) · VirusTotal: · 29 May 2026