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BD kenoodl-synthesis

kenoodl is an orthogonal injection engine — it synthesizes patterns from domains outside your agent's training data and maps them onto whatever problem it's working on. When your agent's outputs start circling, going safe, or hedging, kenoodl gives it structure it couldn't generate on its own.

modbender/skill-library-mcp Agent Skills author: modbender MIT 11 files body ≈ 672 tokens Open the sourcegithub.com↗ analyzed 2 d ago

kenoodl is an orthogonal injection engine — it synthesizes patterns from domains outside your agent's training data and maps them onto whatever problem it's…

As a process D 41/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

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
B
88/100
safety, quality, tests
Safety 60%
92
Quality 40%
81
Run on models
none yet
Process rating
D
41/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
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 · 8

    ✓ No critical or high findings

    Medium and low: 8
    • low Secrets in code secret-high-entropy-token instructions.md:84
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      First call returns 402 with payment instructions. Send $0.49 USDC on Base to `0x3A…743`, then retry with `X-Payment-Tx: <txHash>` header. Poll `GET /api/x402/status/<
      quoted
    • low Secrets in code secret-high-entropy-token instructions.md:88
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      Send ≥0.00012 ETH with your working context as UTF-8 calldata to `0x3A…743` on Base. Synthesis returned as calldata in the response transaction. On-chain calldata is
      quoted
    • low Secrets in code secret-high-entropy-token mcp-server/package-lock.json:78
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…asG+Fkr6…2vM+P9eO…k5W+1WL8w==",
      detector
    • low Secrets in code secret-high-entropy-token mcp-server/package-lock.json:88
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…GLi+2W/6ao+6Y7gu/RCwR…Kng==",
      detector
    • low Secrets in code secret-high-entropy-token mcp-server/package-lock.json:267
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…GLw+xYSd…cqA==",
      detector
    • low Secrets in code secret-high-entropy-token mcp-server/package-lock.json:322
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…FrF+LTRo…W3g==",
      detector
    • low Secrets in code secret-high-entropy-token mcp-server/package-lock.json:331
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…A6j+hAmM…GbS+kf5c…csw==",
      detector
    • low Secrets in code secret-high-entropy-token SKILL.md:40
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      x402: `POST https://kenoodl.com/api/x402/breakthrough` with `{ "context": "..." }`. First call returns 402 with payment instructions. Send $0.49 USDC on Base to `0x3A…6be
      quoted

    Files scanned: 11. 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 41/100

    • 0Steps. Prose only: no discrete steps
    • 0Result and completion. Does not say what the result is
    • 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
    • 30Running it twice. 6 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 672 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 294: enough signal without eating the budget
    • +4Structure: 6 headings
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.