SKILLEMALL.ai

AC ercdata

Store, verify, and manage AI data on the Ethereum blockchain (Base network) using the ERCData standard. Use when an agent needs to store data fingerprints on-chain, verify data integrity, create audit trails, manage access control for private data, or interact with the ERCData smart contract. Supports public and private storage, EIP-712 verification, snapshots, and batch operations.

ClawHub Agent Skills author: 0xReisearch v1.0.0 4 files body ≈ 779 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

AnalyzerInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
98
Quality 40%
91
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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-token references/api.md:5
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      **Base Mainnet:** `0x15…a8c`
      quoted
    • low Secrets in code secret-high-entropy-token scripts/ercdata-cli.py:37
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      DEFAULT_CONTRACT = os.environ.get("ERCDATA_CONTRACT", "0x15…a8c")
      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 51/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 14 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 779 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 385: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 14 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.

    External checks

    ClawHub: suspicious
    This skill is purpose-aligned for blockchain data storage, but it under-warns users about irreversible public exposure and encourages risky private-key handling.
    LLM: suspicious (high) · VirusTotal: benign · 10 Sept 2026