AC prediction-markets-research
Pulls prediction-market odds, order books, and forecast questions from Polymarket, Kalshi, and Metaculus via the Crawlora API, returning clean JSON. Use when the user asks what the market thinks will happen, wants current odds/prices for an event, wants a market's price history or order book, or wants a forecasting community's aggregate prediction on a question.
Pulls prediction-market odds, order books, and forecast questions from Polymarket, Kalshi, and Metaculus via the Crawlora API, returning clean JSON.
As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
The same skill appears in 1 more place: ClawHub
How to improve
- 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 Exfiltration
net-credential-usescripts/crawlora.sh:16Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host; quoted — discussed, not commanded): "${CRAWLORA_API_KEY:?Set CRAWLORA_API_KEY first — get a free key at https://crawlora.net?utm_source=…&utm_medium=…&utm_campaign=…"vendor-hostquoted -
low Exfiltration
net-credential-usescripts/crawlora.sh:135Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)printf 'header = "x-api-key: %s"\n' "$CRAWLORA_API_KEY" >"$curl_config"
quoted
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 54/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 70When it triggers. States when to use, but not when not to
- 85Steps. 18 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 960 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 364: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 18 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.