AC chromia-skill
Guides AI agents through Chromia blockchain dApp development using the Rell language, Chromia CLI (chr), and Postchain nodes. Covers chromia.yml configuration, FT4 accounts and auth descriptors, ICCF cross-chain proofs, ICMF async messaging, EIF EVM integration, CRC2 NFT standard, RID/BRID identity, Filehub decentralized storage, Postchain client initialization, AI extensions (vector DB, inference, Stork oracle), TypeScript client alignment with postchain-client and @chromia/ft4, and deployment pipelines. Do NOT use for Ethereum/Solidity, Solana/Rust, or other non-Chromia blockchain development.
As a process C 51/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
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 · 4
✓ No critical or high findings
Medium and low: 4
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low Secrets in code
secret-high-entropy-tokenreferences/ft4-integration.md:278High-entropy token-like string (may be an id, hash or a credential)crea…ore,
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low Secrets in code
secret-high-entropy-tokenreferences/ft4-integration.md:290High-entropy token-like string (may be an id, hash or a credential)const evmKeyStore = await crea…ore(window.ethereum);
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low Secrets in code
secret-high-entropy-tokenreferences/postchain-client.md:176High-entropy token-like string (may be an id, hash or a credential)import { createKeyStoreInteractor, crea…ore } from "@chromia/ft4"; -
low Secrets in code
secret-high-entropy-tokenreferences/postchain-client.md:178High-entropy token-like string (may be an id, hash or a credential)const evmKeyStore = await crea…ore(window.ethereum);
Files scanned: 11. 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 51/100
- 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. 31 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
- 70Execution cost. Instruction body is 4801 tokens
- 100Steps. 57 steps
- 100Consistency. Name and required fields are in place
- 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 (9 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
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 602: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 57 items
- +4Has examples (14 code blocks)
- +4Reference files are cited in the instructions (9 of 9)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.