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AC dune-analytics-api

Dune Analytics API for blockchain data queries. Use for: (1) Discovering tables and inspecting schemas, (2) Executing/refreshing Dune queries, (3) SQL query optimization for Solana/EVM chains, (4) Understanding dex.trades vs dex_aggregator.trades, (5) Working with Solana transactions and log parsing, (6) Managing query parameters and results, (7) Uploading CSV/NDJSON data to Dune tables, (8) Finding decoded tables by contract address, (9) Searching Dune documentation. Triggers on: Dune query, blockchain data, DEX trades, Solana transactions, on-chain analytics, query optimization, data upload, CSV upload, table discovery, find table, schema inspection, contract address lookup, decoded tables, search docs.

modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files body ≈ 1 411 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Dune Analytics API for blockchain data queries.

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationData and analyticstype 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
A
93/100
safety, quality, tests
Safety 60%
97
Quality 40%
87
Run on models
none yet
Process rating
C
59/100
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

How to improve

    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 references/table-discovery.md:225
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      tables = find_tables_by_contract(client, "0x1f…984", "ethereum")
      quoted
    • low Secrets in code secret-high-entropy-token references/wallet-analysis.md:246
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      'So11…112',  -- WSOL
      quoted
    • low Secrets in code secret-high-entropy-token references/wallet-analysis.md:247
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      '0xC0…Cc2'   -- WETH
      quoted

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 59/100

    • 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
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 5 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1411 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 714: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 5 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.