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BD subgraph-registry-mcp

Same abilities as graphops/subgraph-mcp with better discovery. Search 15,337+ classified subgraphs; real 30-day query volume on every hit; opt-in schema and execute under the official tool names. Discovery tools never auto-query.

ClawHub Agent Skills author: PaulieB14 v0.10.2 MIT-0 14 files body ≈ 2 084 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Same abilities as graphops/subgraph-mcp with better discovery.

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches

IntegrationGitHubAI 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
82/100
safety, quality, tests
Safety 60%
88
Quality 40%
73
Run on models
none yet
Process rating
D
44/100
Unfinished process
Result and completion w 14
0
Failures and branches w 10
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.

Exfiltration 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 instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

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 · 8

✓ No critical or high findings

Medium and low: 8
  • medium Exfiltration net-redirectable-api-key src/index.js:1106
    Helper sends the API key to a host configured by an environment variable — the key can be redirected to another server
    API key + configurable base URL from environment
  • low Secrets in code secret-high-entropy-token README.md:60
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "asset_contract": "0x83…913",
    quoted
  • low Secrets in code secret-high-entropy-token README.md:63
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "pay_to": "0x79…cCB",
    quoted
  • low Secrets in code secret-high-entropy-token README.md:354
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    subgraph_id: "5zvR…NFV",
    quoted
  • low Secrets in code secret-high-entropy-token src/index.js:85
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    asset_contract: "0x83…913", // USDC on Base
    quoted
  • low Secrets in code secret-high-entropy-token src/index.js:88
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    pay_to: "0x79…cCB", // Graph x402 gateway
    quoted
  • low Secrets in code secret-high-entropy-token src/index.js:1996
    High-entropy token-like string (may be an id, hash or a credential)
    function getD…nts(args = {}) {
  • low Secrets in code secret-high-entropy-token src/index.js:2442
    High-entropy token-like string (may be an id, hash or a credential)
    get_…ts: getD…nts,

Files scanned: 12. 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 44/100

  • 0Result and completion. Does not say what the result is
  • 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. 5 mutating operations with no state check
  • 40Consistency. Frontmatter name (subgraph-registry-mcp) differs from the folder (subgraph-registry)
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 27 steps, 1 vague phrases
  • 100Execution cost. Instruction body is 2084 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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
  • +2Single-language instructions
  • +3Description length 229: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 27 items
  • +4Has examples (3 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
The skill is mostly coherent for subgraph discovery, but its optional remote server can expose API-key-backed query tools without authentication and some README run commands are unpinned.
LLM: suspicious (high) · 11 Sept 2026