SKILLEMALL.ai
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BD trading-card-specialist

Advanced AI assistant for trading card dealers, collectors, and shops. Provides market analysis, eBay listing optimization, PSA/BGS grading insights, market research, and automated card valuation. Use when working with sports cards, Pokemon, or collectibles for: (1) pricing analysis and market research, (2) eBay listing creation and optimization, (3) market tracking, (4) grading submission planning, (5) inventory management, or any trading card business operations.

modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files body ≈ 3 004 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Advanced AI assistant for trading card dealers, collectors, and shops.

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches

ProcedureDiscordSecurityInfrastructureMarketingtype 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
88/100
safety, quality, tests
Safety 60%
96
Quality 40%
75
Run on models
none yet
Process rating
D
49/100
Unfinished process
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

    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 README.md:93
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - ❌ No system exploitation or privilege escalation
    • low Risky intent intent-offensive-security README.md:96
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      - ❌ No malicious payload delivery
      detector
    • low Risky intent intent-offensive-security SECURITY.md:57
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - ❌ No system privilege escalation
    • low Risky intent intent-offensive-security SECURITY.md:96
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | **System Security** | Low | No system-level access or privilege escalation |

    Files scanned: 8. 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 49/100

    • 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. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 111 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3004 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
    • -227 emoji in the instructions: noise for the model
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 469: enough signal without eating the budget
    • +4Structure: 43 headings
    • +3Step-by-step instructions: 111 items
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.