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BC ceaser

Interact with the Ceaser privacy protocol on Base L2. Shield and unshield ETH via CLI subcommands (npx ceaser-mcp shield/unshield/notes). Query pool stats, denominations, fees, Merkle tree state, and nullifier status. Ceaser is a privacy-preserving ETH wrapper using Noir/UltraHonk zero-knowledge proofs.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 2 453 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Interact with the Ceaser privacy protocol on Base L2.

As a process C 54/100 · Has gaps — 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
84/100
safety, quality, tests
Safety 60%
92
Quality 40%
71
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 4

✓ No critical or high findings

Medium and low: 4
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash
  • low Secrets in code secret-high-entropy-token SKILL.md:143
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "recipient": "0x74…D18",
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:164
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "recipient": "0x74…D18",
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:220
    High-entropy token-like string (may be an id, hash or a credential)
    npx -y ceaser-mcp unshield <noteId> 0x74…D18

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")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 54/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (ceaser) differs from the folder (agent-privacy-skill)
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 11 steps
  • 100Execution cost. Instruction body is 2453 tokens
  • 100Running it twice. Mutating operations check current state

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 304: enough signal without eating the budget
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 11 items
  • +4Has examples (22 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.