CC token-movers
Crypto market scanner and single-token analyst - movers scans top winners/losers/trending or on-chain runners with pump-risk flags; single-token produces a verdict-first deep report for one token.
Crypto market scanner and single-token analyst - movers scans top winners/losers/trending or on-chain runners with pump-risk flags; single-token produces a…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 5
✓ No critical or high findings
Medium and low: 5
-
low Exfiltration
net-credential-useSKILL.md:71Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)# Pass the key through ./secretcurl's {ENV_NAME} placeholder so no `$SECRET` eversecurity skill -
low Exfiltration
net-credential-useSKILL.md:665Credential used in a network call (verify the destination is the intended service) (negated — the text forbids it)- **Single-token:** for any URL fetch that fails, retry with **WebFetch** — GeckoTerminal, DexScreener, and the public chain RPC are all public GETs/POSTs. WebFetch accepts the JSON body for the `eth_
negated
A further 3 matches are quotations in this security skill's documentation and are not counted as findings.
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 10019 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 30Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 29 mutating operations with no state check
- 40Execution cost. Instruction body is 10019 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 85Steps. 119 steps, 3 vague phrases
- 100Failures and branches. 5 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (8 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 196: enough signal without eating the budget
- +4Structure: 36 headings
- +3Step-by-step instructions: 119 items
- +4Has examples (19 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.