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BC dogecoin-node

A skill to set up and operate a Dogecoin Core full node with RPC access, blockchain tools, and optional tipping functionality.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 2 932 tokens Open the sourcegithub.com↗ analyzed 2 d ago

A skill to set up and operate a Dogecoin Core full node with RPC access, blockchain tools, and optional tipping functionality.

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

IntegrationSoftware developmentWriting and documentstype 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%
90
Quality 40%
72
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
When it triggers w 12
20
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 · 6

✓ No critical or high findings

Medium and low: 6
  • medium Broad scope meta-agent-memory-dump HEARTBEAT.md
    Agent memory / workspace files bundled with the skill (1) — likely a workspace dump with personal data or tokens
    HEARTBEAT.md
  • low Secrets in code secret-high-entropy-token SKILL.md:168
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    * `/dogecoin-node balance D8nL…Tix`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:170
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    * `/dogecoin-node send D8nL…Tix 10`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:172
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    * `/dogecoin-node txs D8nL…Tix`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:542
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    tipping.db.add_user("alice", "D6c9…nfj")
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:544
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    tipping.db.add_user("bob", "DA2S…54g")
    quoted

Files scanned: 4. 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 53/100

  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 7 mutating operations with no state check
  • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 20 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2932 tokens
  • 100Progress reporting. Reports progress
  • low The response is described with custom markup (3 tags): a typed call is more reliable

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 126: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 20 items
  • +4Has examples (13 code blocks)

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