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AF moltgov

Governance infrastructure for Moltbook AI agents. Enables democratic self-organization through citizenship registration, trust webs, elections, class hierarchies, and faction alliances. Use when agents want to: (1) join or participate in AI governance, (2) vote on proposals or elect leaders, (3) establish trust relationships or vouch for other agents, (4) form or join alliances/factions, (5) check their citizenship status, class, or reputation, (6) create or vote on governance proposals. Integrates with Moltbook API and optionally Base chain for on-chain voting records.

modbender/skill-library-mcp Agent Skills author: modbender MIT 14 files body ≈ 1 792 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Governance infrastructure for Moltbook AI agents.

As a process F 33/100 · Will not run — References files that are not bundled: references/FOUNDING_ADDENDUM.md

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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
F
33/100
Will not run
References files that are not bundled: references/FOUNDING_ADDENDUM.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 0

✓ No critical or high findings

Files scanned: 14. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/FOUNDING_ADDENDUM.md

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: references/FOUNDING_ADDENDUM.md
  • 0Tools and files. 1 referenced file(s) missing: references/FOUNDING_ADDENDUM.md
  • 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
  • 30Running it twice. 9 mutating operations with no state check
  • 100Steps. 33 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1792 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 11 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
  • -31 of 10 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 576: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 33 items
  • +4Has examples (15 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

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