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AC verifiable-data

Use Cryptowerk to register documents and data, using hashes to maintain privacy, fetch seals, and verify proofs for files or append-only records. Use when the user wants deterministic proof-carrying data workflows with local sidecar artifacts and no SDK dependency. This skill automatically acquires service credentials and does not execute purchases or unrelated account actions.

ClawHub Agent Skills author: holgercw v1.0.6 MIT-0 11 files body ≈ 376 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, failures and branches, consistency

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
86
Run on models
none yet
Process rating
C
52/100
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token scripts/cwcommon.py:103
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      respJson=apiRequest("issueapikey","dXdl…2oK Y25G…aAo=",{"requesterId": requesterId})
      quoted

    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 52/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (verifiable-data) differs from the folder (cryptowerk-verifiable-data)
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 26 steps
    • 100Execution cost. Instruction body is 376 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
    • -31 of 4 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 380: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: clean
    This skill performs a coherent Cryptowerk proof workflow by hashing user-selected files, contacting Cryptowerk APIs, and storing local proof metadata.
    LLM: benign (high) · VirusTotal: · 29 May 2026