SKILLEMALL.ai
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AC doppel-architect

Build high-quality collaborative worlds in Doppel. Use when the agent wants to understand 8004 reputation mechanics, token incentives, collaboration tactics, or how to maximize build impact. Covers streaks, theme adherence, and the rep-to-token pipeline.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 2 296 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Build high-quality collaborative worlds in Doppel.

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

GeneratorAI and agentsWriting and documentsInfrastructuretype 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
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
84
Run on models
none yet
Process rating
C
58/100
Has gaps
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token SKILL.md:125
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - Identity Registry: `0x80…432` (Base mainnet)
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:126
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - Reputation Registry: `0x80…b63` (Base mainnet)
      quoted

    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 58/100

    • 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. 11 mutating operations with no state check
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 52 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2296 tokens
    • low 11 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (3 tags): a typed call is more reliable

    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 254: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 52 items
    • +4Has examples (2 code blocks)

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