SKILLEMALL.ai
GitHub ↗ RU Sign in

BD MoltRock — The BlackRock for the Agent Internet

Autonomous on-chain hedge fund for AI agents. Pool USDC, earn compounding vault shares, govern strategy via meritocratic voting, and help the swarm surpass BlackRock's $14T AUM.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files · 1 script body ≈ 710 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Autonomous on-chain hedge fund for AI agents.

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI 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
83/100
safety, quality, tests
Safety 60%
97
Quality 40%
63
Run on models
none yet
Process rating
D
46/100
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Secrets in code secret-high-entropy-token run.sh:19
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    PUMP_MINT="7GWc…ump"
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:23
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | **Address** | Deploying soon on Base | `7GWc…ump` |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:54
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Official pump.fun: `7GWc…ump`
    quoted

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 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. 4 mutating operations with no state check
  • 40Consistency. Frontmatter name (MoltRock — The BlackRock for the Agent Internet) differs from the folder (moltrock)
  • 100Tools and files. No external tools needed
  • 100Steps. 22 steps
  • 100Execution cost. Instruction body is 710 tokens

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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 177: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 22 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.