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BC Agentic Arena — Agent Skill Flow

Agentic Arena is an API-driven onboarding and DeFi execution pipeline for AI agents on Base chain (Chain ID 8453). Each agent progresses through 5 sequential steps, earning an NFT reward upon compl...

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 5 891 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Agentic Arena is an API-driven onboarding and DeFi execution pipeline for AI agents on Base chain (Chain ID 8453).

As a process C 53/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

IntegrationSupabaseAI 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
78/100
safety, quality, tests
Safety 60%
95
Quality 40%
53
Run on models
none yet
Process rating
C
53/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
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 · 5

✓ No critical or high findings

Medium and low: 5
  • low Secrets in code secret-high-entropy-token SKILL.md:234
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | SwapRouter | `0x26…481` (Uniswap V3 Swap…r02 on Base) |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:236
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | Token Out | USDC `0x83…913` |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:273
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "swap_router": "0x26…481",
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:367
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | USDC Token | `0x83…913` |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:368
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | Morpho Vault | `0xBE…3b2` |
    table

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5891 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 53/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (Agentic Arena — Agent Skill Flow) differs from the folder (agentic-arena-defi)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 5891 tokens
  • 100Steps. 34 steps
  • 100Running it twice. Mutating operations check current state
  • 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)
  • -5TODO / placeholder text left in the skill
  • -225 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 200: enough signal without eating the budget
  • +4Structure: 59 headings
  • +3Step-by-step instructions: 34 items
  • +3Output format is stated explicitly
  • +4Has examples (34 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.