sanad-agent: Skill for Claude Code

.agents/skills/jev-dual-brain-automation/SKILL.md

jev-dual-brain-automation is a skill for Claude Code, Codex from EastStarAI/sanad-agent. It costs 105 tokens per session (1,880 once invoked), scanned A, original, MIT.

A browser and desktop interface automation approach that uses fast, typed actions for ordinary cases and a larger reasoning model when the interface becomes difficult. It is intended for web pages and Flutter desktop applications.

In plain words
What is it for?
Use it to automate browser tasks, repetitive forms and Flutter desktop UI tests, especially when the interface may contain unexpected steps.
Why use it?
It addresses the trade-off between slow, costly step-by-step reasoning and fragile automation that cannot handle unexpected screens. Escalation is used for obstacles such as banners, calendars and other complex flows.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is EastStarAI/sanad-agent's own configuration. It tells Claude Code and Codex how to work on sanad-agent itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything sanad-agent configures →

Reuse

Borrowing it

Nothing to install: this file belongs to EastStarAI/sanad-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/EastStarAI/sanad-agent/main/.agents/skills/jev-dual-brain-automation/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/EastStarAI/sanad-agent

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for jev-dual-brain-automation

README.md
[![agentmods](https://agentmods.dev/badge/skills/eaststarai/sanad-agent/jev-dual-brain-automation/github.svg)](https://agentmods.dev/skills/eaststarai/sanad-agent/jev-dual-brain-automation)
Your own site
<a href="https://agentmods.dev/skills/eaststarai/sanad-agent/jev-dual-brain-automation"><img src="https://agentmods.dev/badge/skills/eaststarai/sanad-agent/jev-dual-brain-automation/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for jev-dual-brain-automation

Your own site · 80×15
<a href="https://agentmods.dev/skills/eaststarai/sanad-agent/jev-dual-brain-automation"><img src="https://agentmods.dev/badge/skills/eaststarai/sanad-agent/jev-dual-brain-automation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 105 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,880 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00105 $0.01880
Opus 5.5 $0.00042 $0.00752
Sonnet 5.5 $0.00021 $0.00376
Haiku 4.5 $0.00011 $0.00188

Measured 1mo ago against content hash ab3ade5b9de1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

jev-dual-brain-automation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/dual_brain_browser.py, scripts/jev_client.py, scripts/workflow_engine.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/jev-dual-brain-automation/SKILL.md · 149 lines

How it starts

The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Jev Dual-Brain Automation

Use Jev as a fast judgment primitive inside a workflow owned by software. Jev is not an agent: it does not own phases, retries, irreversible actions, arithmetic, dates, safety policy, or completion.

Ownership model

Owner Responsibilities
Deterministic code Phase order, structured state, candidate IDs, thresholds, exact dates/numbers, action execution, irreversible-action admission, event subscriptions, timeouts, assertions, and audit history
Jev (System 1) One narrow semantic Choice, Score, or Noul judgment over supplied state
System 2 Diagnose blockers, ambiguity, unknown screens, low confidence, oscillation, and policy-sensitive decisions
UI adapter Observe the current surface and execute an exact action selected by code

Do not ask Jev to “complete the task,” infer a multi-step plan, calculate totals, compare exact dates, or decide whether a risky action is allowed. Ask one snap judgment per question and compose the answers in code.

Required workflow

  1. Define phases before acting. Mark submit, send, purchase, delete, permission, and similar effects as irreversible.
  2. Observe structured state. Include the goal, current phase, screen/page mode, relevant candidates, completed phases, and bounded action history as named JSON fields.
  3. Prune candidates deterministically. Send stable IDs and concise descriptions. Never let Jev invent a selector.
  4. Classify hierarchically when decisions depend on each other. First classify page/screen mode. In a later request, ask for the action among candidates valid for that mode. Questions in one request are independent; one answer cannot condition another answer in the same batch.
  5. Inspect the complete answer. Choice and Score return probabilities plus confidence. Route low confidence or an invalid target to System 2 before acting. Noul is itself a yes probability and has no separate confidence field.
  6. Reserve the phase before an irreversible action. A completed phase cannot re-enter. An attempted irreversible phase cannot be retried automatically after an ambiguous result.
  7. Snapshot immediately before acting. Execute one exact adapter action, once.
  8. Wait for events, not repeated model calls. Subscribe to navigation, lifecycle, response, or UI-transition events and block until a relevant event or timeout. Do not poll Jev while waiting.
  9. Verify after the event. Use deterministic assertions for exact strings, IDs, dates, counts, and numeric values. Use a separate narrow Jev judgment only for semantic evidence.
  10. Escalate fail-closed. Stop on low confidence, blockers, invalid candidates, unchanged UI, repeated targets, oscillation, timeout, or unknown state. Give System 2 bounded state, candidates, phase ledger, and recent actions.

Read the full file on GitHub · 149 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · -13 lines ab3ade5b9de1
  2. 1mo ago Changed · +2 lines · -29 tokens per session 6d64e2da1676
  3. 1mo ago First seen · 160 lines · 134 tokens per session scan A cd0b58cbca06

Subscribe to this mod's changes

jev-dual-brain-automation is a skill published in the GitHub repository EastStarAI/sanad-agent (47 stars, last pushed yesterday), licensed MIT. It adds 105 tokens to every session and 1,880 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-22.

Related

jev-dual-brain-automation compared with skills from other repositories

jev-dual-brain-automation next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
jev-dual-brain-automationEastStarAI/sanad-agentSkill47yesterdayMITA105Claude Code, Codex
e2esgaabdu4/building-flutter-appsSkill254d agoMITA42Claude Code, Codex
flutter-mcp-automation-chainArenukvern/mcp_flutterSkill39520d agoMITC176Claude Code
simpodcscoderr/simpodSkill274mo agoApache-2.0A63Claude Code, Codex
flutterpledgeandgrow/pledge-skillsSkill103mo agoMITA126Claude Code, Codex
webapp-testingHmbown/CodeWhaleSkill41k5d agoMITA32Claude Code, Codex
computer-usebojieli/ai-agent-bookSkill57k29d agoApache-2.0A139Claude Code, Codex