AC telegraph
Use Telegraph Protocol for verified AI inference. Activate when the user asks for weather forecasts or climate data; deepfake or AI-content detection; LLM completions, image generation, or embeddings via a decentralized network; AI text detection; autonomous signal monitoring across categories like POLITICS, TECHNOLOGY, CLIMATE, HEALTH, ECONOMICS, or GEOPOLITICS; or any task where the user explicitly wants to route inference through the Telegraph network or pay via x402 USDC micropayments. Requires the Telegraph MCP server running with a USDC-funded EVM or Solana wallet.
As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting
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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:123High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)- **Network**: Base Sepolia (testnet) — USDC `0x03…F7e`
quoted
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 61/100
- 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
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 100Steps. 5 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1709 tokens
- 100Running it twice. No mutating operations
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 577: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 5 items
- +4Has examples (4 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.