Agent Execution Template

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A 30-second execution protocol for AI coding agents. Install it into any repository, let the agent bootstrap project context from existing docs, confirm the task contract, and keep execution auditable.

npx -y @wnlen/agent-execution-template init --lang en

Then tell your coding agent:

Start initializing this project

Agent Execution Template is not another agent framework. It is the missing execution layer between your repository and tools like Codex, Claude Code, Cursor, Aider, or any other AI coding agent.

It turns AI coding from:

chat prompt -> ad hoc edits -> unclear verification -> lost context

into:

bootstrap project -> confirm context -> draft task -> confirm contract -> execute -> recorded result

Why It Exists

AI coding agents are powerful, but most teams still run them through loose chat context. That creates predictable failure modes:

• You repeat the same project background every session.
• The agent drifts beyond the task boundary.
• Risk, permissions, and acceptance criteria stay implicit.
• Code changes are made without reliable verification records.
• Useful execution history disappears into chat logs.
• Template upgrades accidentally overwrite project-specific context.
• Cheap and strong models are used without a clear division of labor.
• The two files that define execution precision are often written by hand.
• Execution can become stable while still lacking a direction layer for judging whether a task is worth doing or whether the project is drifting.

Agent Execution Template fixes this with a small, installable file protocol:

ai/template/  reusable execution protocol
ai/project/   project-specific working context and direction layer

update can refresh the protocol, while your project workspace stays protected.

Quick Start

Install the protocol into the current repository:

npx -y @wnlen/agent-execution-template init --lang en

Ask your agent to bootstrap project context from existing docs and manifests:

Start initializing this project

The agent will generate project context and summarize what needs confirmation, risks, and the recommended next step in chat:

ai/project/project.md
ai/project/refs/*

Reply with corrections, or confirm and continue:

Continue this project

The agent will draft or execute from current context:

ai/project/task.md

After the task draft is confirmed, you can also say:

Continue this project

Review the execution output:

ai/project/result.md
ai/project/result.json
ai/project/metrics.json

Check the installation:

npx -y @wnlen/agent-execution-template doctor

When you forget what to do next:

npx -y @wnlen/agent-execution-template next --lang en

Resummarize and improve the project context:

npx -y @wnlen/agent-execution-template refresh --lang en

Upgrade only the reusable protocol files:

npx -y @wnlen/agent-execution-template update --lang en

Print the direction-amendment entrypoint:

npx -y @wnlen/agent-execution-template strategy --lang en

What You Get

Capability	What it means
Installable protocol	Add an AI execution contract to any repository in seconds.
Agent agnostic	Works with Codex, Claude Code, Cursor, Aider, and other coding agents.
Bootstrap mode	Reads approved docs/manifests, falls back to bounded code inference, drafts project.md and refs, then stops for confirmation.
Project North Star	Stores final shape, task-worthiness criteria, and drift criteria in ai/project/refs/final-shape.md.
Strategy amendment gate	New direction goes through inbox/ideas/, a proposal, human confirmation, then an explicit apply task.
Protected project context	update refreshes ai/template/** without overwriting ai/project/**.
Project context refresh	refresh backs up old ai/project/**, creates a fresh project context, and imports the old context into the inbox for reconciliation.
Automatic continuous execution	The agent decomposes L1/L2/L3 before execution; 2+ L1 tasks automatically enable bounded continuous execution, and only Red risk stops for confirmation.
Auditable results	Every run can leave human-readable output, machine-readable facts, and metrics.
Token-efficient model policy	Cheap models handle bounded work; strong models are reserved for judgment points.
Upgradeable template	Reuse protocol improvements without losing local project memory.
Doctor checks	Validate required files and template version before running the agent.

How Automatic Continuous Execution Works

The user can still give a natural-language goal, for example:

Build the settings page with profile editing, notification toggles, and export entrypoint

Before execution, the AI decomposes L1 tasks. Each L1 must be an independently acceptable vertical slice, not a mechanical step checklist:

- [ ] L1-1 Profile editing Green
- [ ] L1-2 Notification toggles Green
- [ ] L1-3 Export entrypoint Yellow

Because there are two or more L1 tasks, the protocol automatically uses bounded continuous execution. Before each L1, the AI plans naturally derived L2/L3 work. After completing an L1, it checks and strikes the item. task_tree is written back only at L1 start/done, Red/blocked, scope changes, or final wrap-up, so tiny steps do not churn files.

Only Red risk stops for confirmation. Green continues automatically, and Yellow only permits local low-risk correction inside the current L1/L2; it must not change public interfaces, data models, permissions, security, architecture direction, or acceptance. By default, users see L1, risk conclusions, evidence, Red confirmations, and final results; internal protocol details are not shown.

If the AI just created or rewrote ai/project/task.md in the current run, it must stop for confirmation. Execution is allowed only when an existing task is explicitly ready_to_execute.

Installed Layout

ai/
  README.md

  template/
    VERSION
    bootstrap.md
    execution-policy.md
    prompt.md
    reconcile.md
    protocol.md
    rules/
      core.md
      output.md
    schemas/
      result.schema.json
      metrics.schema.json

  project/
    project.md
    runtime.md
    task.md
    result.json
    result.md
    metrics.json
    inbox/
      ideas/
      raw/
    proposals/
      final-shape-updates/
    refs/
      final-shape.md
      module-map.md
      roadmap.md
    archive/

The split is the core design:

• ai/template/** is reusable protocol. It can be safely updated from this package.
• ai/project/** is your project workspace. It stores local context, tasks, references, results, and metrics.

Commands

init

npx -y @wnlen/agent-execution-template init --lang en

Creates ai/ in the current project.

• Updates or creates ai/template/**.
• Creates missing ai/project/** files.
• Keeps existing ai/project/** files intact.
• Use --lang zh or omit --lang for the Chinese template.

next

npx -y @wnlen/agent-execution-template next --lang en

Prints the next step based on the current project state:

• If the template is not installed, it tells you to run init.
• If ai/project/inbox/ has material, it routes to context reconcile.
• If ai/project/inbox/ideas/ has ideas, it routes to a direction amendment proposal.
• If a direction proposal exists, it asks for human review and confirmation.
• If no intake is waiting, it tells the agent to continue the project.

update

npx -y @wnlen/agent-execution-template update --lang en

Updates only ai/template/**.

Use this when the protocol improves but your project context should remain untouched. Without --lang, update follows the installed language in ai/template/LANG.

refresh

npx -y @wnlen/agent-execution-template refresh --lang en

Resummarizes and improves project context.

• Renames old ai/project/** to ai/project.backup.<timestamp>.
• Generates a fresh ai/project/**.
• Copies the old context into ai/project/inbox/raw/old-project/.
• Prints the next prompt to give your agent.

You can also use the more explicit alias:

npx -y @wnlen/agent-execution-template improve-context --lang en

doctor

npx -y @wnlen/agent-execution-template doctor

Checks the installed template version and required files.

It reports:

• [OK] for present and usable files.
• [WARN] for empty required project context files.
• [MISSING] for missing required files.

reconcile

npx -y @wnlen/agent-execution-template reconcile --lang en

Prints the shortest context-reconcile instructions.

strategy

npx -y @wnlen/agent-execution-template strategy --lang en

Prints the shortest direction-amendment instructions. New ideas go to ai/project/inbox/ideas/, then the agent generates a strategy_update proposal. After human confirmation, apply_strategy_update merges it.

Execution Model

Agent Execution Template defines a simple loop:

Project Bootstrap -> Project Confirm -> Task Draft -> Task Confirm -> Plan -> Execute -> Review -> Result

The point is not to build a complex orchestrator. The point is to make one AI-assisted coding run clear enough to execute, verify, rerun, and audit.

The protocol records:

• approved bootstrap sources,
• the task contract,
• assumptions and risk,
• verification attempts,
• human-readable results,
• machine-readable execution facts,
• model tier and cost signals.

Context Reconcile

When a more complete or more authoritative document appears after the project has been using the template, put it in:

ai/project/inbox/

Then ask your agent:

Reconcile the new material in ai/project/inbox/

The agent must produce a reconciliation plan first, wait for confirmation, then merge long-lived facts into project.md, runtime.md, and refs/*. After reconciliation, processed material is moved to ai/project/inbox/processed/ for traceability. By default, only ai/project/inbox/*.md and ai/project/inbox/raw/*.md are absorbed; processed/** is not reconciled again, and ideas/** goes through the direction amendment proposal flow.

Project North Star

Long-term direction does not belong in the current task. Agent Execution Template stores the direction layer inside protected ai/project/** files:

ai/project/refs/final-shape.md       # project North Star / final shape
ai/project/refs/module-map.md        # current module map
ai/project/refs/roadmap.md           # staged roadmap
ai/project/inbox/ideas/              # new idea intake
ai/project/proposals/final-shape-updates/
ai/project/proposals/final-shape-updates/_template.md

Routine execution tasks must not edit the North Star, module map, or roadmap directly. Direction changes should follow:

idea -> strategy_update proposal -> human confirm -> apply_strategy_update

That keeps task.md as the current work order while final-shape.md explains why a task is worth doing and where the project should grow.

Token-Efficient by Design

The optional token-efficient profile gives agents a model division rule:

• Use cheap models for bounded reads, small edits, drafts, repetitive checks, and mechanical cleanup.
• Use standard models for moderate implementation work.
• Use strong models for planning, architecture review, risk judgment, failure review, and acceptance disputes.

The goal is not fewer tokens at any cost. The goal is more acceptable work per unit of model cost.

Read more in Token-Efficient AI Execution Protocol v0.1.

Works With

Agent Execution Template is intentionally tool-neutral. Any agent that can read project files and follow instructions can use it.

Common pairings:

• Codex
• Claude Code
• Cursor
• Aider
• custom coding agents
• low-cost model runners for bounded execution

Designed For

• Developers who run AI coding agents across many repositories.
• Teams that want repeatable AI execution without adopting a full agent platform.
• Projects where task boundaries, verification, and audit trails matter.
• Workflows that use cheap models by default and escalate only for judgment.
• Repositories that need AI context to live in files, not only in chat history.
• Users who want AI to draft the critical context files while humans confirm the final boundary.

Not This

Agent Execution Template is not:

• an IDE,
• an agent platform,
• a multi-agent scheduler,
• a cloud service,
• a prompt collection,
• a replacement for Codex, Claude Code, Cursor, or Aider.

It is a small file protocol for making those tools behave more consistently inside real software projects.

Specification

• Full specification
• Token-efficient protocol profile

Current package:

Package:  @wnlen/agent-execution-template
Protocol: v0.8
License:  MIT

Development

Run the self-test:

npm test

Run the release consistency check:

npm run check:release

The test suite verifies the core CLI contract:

• init creates the expected protocol and project files.
• update does not overwrite ai/project/**.
• doctor reports missing and empty required files correctly.
• check:release verifies versions, template shape, installed protocol state, and the spec's package version.

When maintaining this npm package source checkout, test the local CLI with node bin/agent-execution-template.js <command>. Use npx -y @wnlen/agent-execution-template <command> in user projects only. Maintainer-local ai/project/** bootstrap content should not be committed as product changes.

Contributing

Issues and pull requests are welcome.

Good contributions usually improve one of these areas:

• clearer task contracts,
• safer project/template boundaries,
• better result schemas,
• better metrics for model cost and verification,
• stronger examples for real coding-agent workflows,
• sharper documentation for team adoption.

License

MIT