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AC agentic-commerce-forthecult

Agentic Commerce skills enables agents to autonomously browse and search for quality lifestyle, wellness, and tech products and gifts, view details, create orders with multi-chain payments (Solana, Ethereum, Base, Polygon, Arbitrum, Bitcoin, Dogecoin, Monero), apply CULT token-holder discounts, and track orders from payment to delivery. Use when a user wants to buy products for humans and AI, browse a store, find gifts, place an order, or track a shipment.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 3 896 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Agentic Commerce skills enables agents to autonomously browse and search for quality lifestyle, wellness, and tech products and gifts, view details, create…

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureLogistics and warehouseAI 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
A
91/100
safety, quality, tests
Safety 60%
93
Quality 40%
88
Run on models
none yet
Process rating
C
55/100
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

    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 · 7

    ✓ No critical or high findings

    Medium and low: 7
    • low Secrets in code secret-high-entropy-token references/API.md:78
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      { "symbol": "USDC", "name": "USD Coin", "type": "spl", "decimals": 6, "mint": "EPjF…t1v" },
      quoted
    • low Secrets in code secret-high-entropy-token references/API.md:79
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      { "symbol": "USDT", "name": "Tether", "type": "spl", "decimals": 6, "mint": "Es9v…NYB" },
      quoted
    • low Secrets in code secret-high-entropy-token references/API.md:88
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      { "symbol": "USDC", "name": "USD Coin", "type": "erc20", "decimals": 6, "mint": "0xA0…B48" },
      quoted
    • low Secrets in code secret-high-entropy-token references/API.md:89
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      { "symbol": "USDT", "name": "Tether", "type": "erc20", "decimals": 6, "mint": "0xdA…ec7" }
      quoted
    • low Secrets in code secret-high-entropy-token references/API.md:382
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "walletAddress": "7xKX…AsU"
      quoted
    • low Secrets in code secret-high-entropy-token references/CHECKOUT-FIELDS.md:127
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "walletAddress": "0x74…D18"
      quoted
    • low Secrets in code secret-high-entropy-token references/CHECKOUT-FIELDS.md:141
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "address": "7xKX…AsU",
      quoted

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 55/100

    • 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. 15 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 43 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3896 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 460: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.