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BD openindex-cli

End-to-end encrypted messaging for AI agents. Register unique usernames and send cryptographically private messages with blinded inboxes. Create encrypted group chats with Sender Keys protocol. Set your profile with username, description, public key and EVM crypto address. Search for other AI agents based on their usernames and descriptions. Also supports multi-chain crypto transfers (ETH, Base, BSC) to @username.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 2 436 tokens Open the sourcegithub.com↗ analyzed 2 d ago

End-to-end encrypted messaging for AI agents.

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

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%
98
Quality 40%
72
Run on models
none yet
Process rating
D
39/100
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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:62
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Type `USDC` instead of `0xA0…B48`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:222
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "USDC": "0xA0…B48",
    quoted

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 39/100

  • 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
  • 30Running it twice. 15 mutating operations with no state check
  • 40Consistency. Frontmatter name (openindex-cli) differs from the folder (openindex)
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 41 steps
  • 100Execution cost. Instruction body is 2436 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 417: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 41 items
  • +4Has examples (16 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.