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BD poe-api-orchestrator

Use Poe API to call specialized AI models (Claude-Opus, Claude-Sonnet, Gemini, GPT-Codex). Automatically spawn subagents for coding, UI/UX design, data analysis, and complex reasoning tasks. Main agent decides which specialized subagent to use based on task type.

modbender/skill-library-mcp Claude Code author: modbender MIT 8 files body ≈ 1 850 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Use Poe API to call specialized AI models (Claude-Opus, Claude-Sonnet, Gemini, GPT-Codex).

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentsSoftware developmenttype 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
89/100
safety, quality, tests
Safety 60%
96
Quality 40%
79
Run on models
none yet
Process rating
D
44/100
Unfinished process
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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Secrets in code secret-high-entropy-token README.md:42
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      export POE_API_KEY="w0wo…Wck"
      quoted
    • low Secrets in code secret-high-entropy-token README.md:129
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      POE_API_KEY="w0wo…Wck" python3 scripts/poe_client.py
      detector
    • low Secrets in code secret-high-entropy-token scripts/poe_client.py:26
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      api_key: str = os.getenv("POE_API_KEY", "w0wo…Wck")
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:67
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      export POE_API_KEY="w0wo…Wck"
      quoted

    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 44/100

    • 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
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 85Steps. 45 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1850 tokens
    • 100Running it twice. No mutating operations
    • low 10 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
    • -232 emoji in the instructions: noise for the model
    • -33 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 263: enough signal without eating the budget
    • +4Structure: 30 headings
    • +3Step-by-step instructions: 45 items
    • +4Has examples (14 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.