AD solscan-data
Use this skill to query Solana blockchain data via the Solscan Pro API. Triggers: look up wallet address, check token price, analyze NFT collection, inspect transaction, explore DeFi activities, get account metadata/label/tags, fetch block info, monitor API usage, search token by keyword.
As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, consistency
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 2
✓ No critical or high findings
Medium and low: 2
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:92High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)> - `--token`: Filter by token address(es) (max 5, comma-separated). Use `So11…111` for native SOL
quoted -
low Secrets in code
secret-high-entropy-tokenSKILL.md:148High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)> - `--token`: Filter by token address (use `So11…111` for native SOL)
quoted
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5958 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 48/100
- 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. 6 mutating operations with no state check
- 40Consistency. Frontmatter name (solscan-data) differs from the folder (exploring-solana-with-solscan)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 5958 tokens
- 100Steps. 18 steps
- low The response is described with custom markup (5 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
- +2Single-language instructions
- +3Description length 289: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 18 items
- +4Has examples (1 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.