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AC inclawnch-staking

Stake and unstake INCLAWNCH tokens in the on-chain UBI staking contract on Base. Query treasury stats, wallet positions, APY estimates, and top stakers. All write operations require wallet signatures (on-chain transactions). Read operations use a public API — no API key needed. Use when: (1) user wants to stake INCLAWNCH, (2) checking staking positions or rewards, (3) unstaking tokens, (4) claiming rewards, (5) enabling auto-compound, (6) comparing staking yields.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 790 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Stake and unstake INCLAWNCH tokens in the on-chain UBI staking contract on Base.

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

IntegrationAI 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%
95
Quality 40%
84
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token SKILL.md:46
      High-entropy token-like string (may be an id, hash or a credential)
      Contract: 0x20…5e6  (InclawnchStaking proxy)
    • low Secrets in code secret-high-entropy-token SKILL.md:47
      High-entropy token-like string (may be an id, hash or a credential)
      Token:    0xB0…808  (INCLAWNCH ERC-20)
    • low Secrets in code secret-high-entropy-token SKILL.md:56
      High-entropy token-like string (may be an id, hash or a credential)
      To:       0xB0…808  (INCLAWNCH token)
    • low Secrets in code secret-high-entropy-token SKILL.md:59
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      Args:     spender = 0x20…5e6, amount = tokens in wei
      detector
    • low Secrets in code secret-high-entropy-token SKILL.md:64
      High-entropy token-like string (may be an id, hash or a credential)
      To:       0x20…5e6  (InclawnchStaking)

    Files scanned: 1. 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 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 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
    • 100Tools and files. No external tools needed
    • 100Steps. 11 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1790 tokens
    • 100Running it twice. No mutating operations

    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 468: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (11 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.