agentleFS
wuyoscarwuyoscar/jev-skill/skills/jev-act/SKILL.md

jev-act

Skill665 starsChanged 46 days agoMITView on GitHub

Choose one legal next action in a browser, desktop, game or simulation. Supply fresh observed state and available actions. The host or simulator executes and checks the result; selection does not grant permission.

  • Reads credentials

1 other project uses a version of this.

Adopted by

Most recently taken up by

  • Other versions1 project uses a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 1

    1. aitofy-dev/jev-awesome-skills★ 2same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository5 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Browser or desktop: read UI steps; start with the UI request.

    line 55
  2. Distinctive:

    Game or simulation: read world steps; start with the world request.

    line 56
  3. Distinctive:

    Use only the current mode.

    line 58
  4. Distinctive:

    Use bounded concurrency only for independent requests.

    line 65
  5. Distinctive:

    Wait for a new observation after an action before asking a dependent question.

    line 66

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Choose the next action

Learn from the workflows

For design requests, browse the scenario index, read the relevant guides and input/output examples, and compare or combine patterns. Adapt what you learn to the user's task; the collection is inspiration, not a closed menu. A familiar, straightforward decision can use its recipe directly.

Friendly reminder: Jev can help with initial, repeated or bulk judgments while you lead the overall work. Read the evidence, design the workflow, spot-check results (including confident or agreeing labels), and bring your own analysis and synthesis. This is guidance for collaboration, not an agent harness or a fixed call/token quota; existing user permissions and budgets still apply.

Use safely

Choose the service once and keep that choice. If unset, ask A: real Jev via OpenRouter (OPENROUTER_API_KEY) or TypeSafe (TYPESAFE_API_KEY), or B: simulation with this agent or an explicitly chosen available model such as DeepSeek. Wait for consent; errors do not authorize switching. Check key presence only, never values. Real calls send evidence and cost money; get approval before sending private data.

For B, skip CLI/API calls. Mark agent_simulation or model_simulation, identify the actual model when available, set jev_called: false, probability: null and confidence: null. Return a value, evidence-based reason and needs_review; use null/review when evidence is missing. Do not invent Jev output or probabilities. Choice uses supplied labels, Noul uses booleans, Score uses integer rubric indices.

For A, use the existing jev-decide CLI with the chosen --provider openrouter or --provider typesafe. If absent, explain the dependency; do not silently install. --dry-run is offline validation, not a judgment. Exit 0 means selected/scored, 2 means review, 1 means error. Read each value: false Noul remains false. Selection is not permission, and confidence is not accuracy. Keep unknown/review paths.

First request

Adapt the example. The shared CLI needs Python 3.10+; no sibling skill is needed. Host tools still own collection and actions. Resolve <skill-dir> to this installed folder:

jev-decide decide <skill-dir>/assets/example.json --dry-run
# After approval, send the edited request with the selected provider:
jev-decide decide /path/to/request.json --provider openrouter

Pick one mode

  • Browser or desktop: read UI steps; start with the UI request.
  • Game or simulation: read world steps; start with the world request.

Use only the current mode. A simulated world is not permission to operate a real account. The host validates legal actions, checks freshness and applies the result.

Context and parallelism

Jev does not inherit the agent's history. Include the goal, rules, fresh context, legal candidates and relevant outcomes. Batch independent checks in the same request. Use bounded concurrency only for independent requests; the host owns scheduling. Wait for a new observation after an action before asking a dependent question.

Examples

Next browser action · Browser wait versus intervention · Browser outcome verification

More workflows and local templates. Browse across examples when designing a solution; follow the guides and sources that help.

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What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.