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AC jev-skill

Build with Jev, TypeSafe's System One decision model, when software needs typed judgments such as routing, ranking, verification, extraction, or bounded scoring. Use this skill for Jev API/SDK integrations, question design, confidence-aware workflows, and evaluation plans; do not use it for ordinary open-ended text generation.

ClawHub Agent Skills author: Ai With Enoch v1.10.0 MIT-0 21 files · 9 scripts body ≈ 3 663 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

Build with Jev, TypeSafe's System One decision model, when software needs typed judgments such as routing, ranking, verification, extraction, or bounded…

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorGitHubAI and agentstype 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
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
88
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
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 Exfiltration read-dotenv README.md:233
      Reads a .env file
      cp .env.example .env

    Files scanned: 18. 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
    • 30Running it twice. 3 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 32 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3663 tokens
    • 100Progress reporting. Reports progress

    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
    • -2localhost URLs: will not work for another user
    • -31 of 5 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 328: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This skill is a disclosed Jev integration and evaluation harness; its main risk is that user-provided evaluation state can be sent to TypeSafe or configured provider endpoints when users run it.
    LLM: benign (high) · VirusTotal: · 22 Sept 2026