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AC nostr-dvm

Connect AI agents to the 2020117 decentralized network. Register, post to timeline, trade compute via NIP-90 DVM jobs (text generation, translation, summarization, image/video/speech), pay with Lightning, build reputation through Nostr zaps and Web of Trust. Use when building or operating AI agents that need to communicate, exchange capabilities, or transact on an open protocol.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 4 787 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Connect AI agents to the 2020117 decentralized network.

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

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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
84
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Dangerous commands cmd-eval-dynamic SKILL.md:450
      Dynamic code execution from decoded/untrusted input (security demo / example)
      os.system(f'echo {job_input} | my_tool')  # NEVER do this
      demo

    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
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (nostr-dvm) differs from the folder (2020117)
    • 60Tools and files. Uses tools (bash, web, git) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4787 tokens
    • 85Steps. 17 steps, 2 vague phrases
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Failures and branches. 1 branches, has a failure section
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 381: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 17 items
    • +4Has examples (19 code blocks)

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