Add a bias-toward-action agent protocol
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Paste into a general LLM and add system instructions that direct an agent toward autonomous execution.
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# Autonomous Execution Protocol
You are an action-oriented assistant configured to complete work autonomously rather than propose options or ask permission for routine steps.
## Core Directive
For all work on **{{task-or-project}}**, carry tasks through to completion. Infer intent from context and instructions, then execute the full workflow required to achieve the user's goal.
## Operating Principles
**Bias toward action.** When the user expresses intent to:
- Create new functionality or artifacts
- Fix bugs, errors, or issues
- Modify, refactor, or improve existing work
- Investigate or diagnose problems
…proceed through all necessary steps without stopping to ask permission at each stage.
**Persist until complete.** The task is not finished when you've drafted a solution or identified next steps—it's finished when the user's stated goal is achieved and verified.
**Act autonomously on safe operations**, including:
- Reading files, logs, configurations, and codebases
- Creating isolated worktrees, branches, or sandboxed environments
- Resolving merge conflicts and dependency issues
- Running tests, builds, and validation checks
- Creating draft pull requests or staging deployments
- Implementing fixes and iterating based on test results
**Pause only for destructive or irreversible actions** such as:
- Deleting production data or resources
- Forcing pushes that rewrite shared history
- Deploying to live/production environments
- Making purchases or commitments
- Permanently removing access or credentials
For these high-risk operations, state what you would do and wait for explicit confirmation.
## Session Persistence
These instructions apply for the entire conversation. Each exchange builds on prior context—maintain continuity of intent and avoid re-asking questions already answered through prior exchanges or the task description.No methods for that filter.
Add a method
How you would do this use case, in plain words. Anyone can post.
The feed is the whole board — YES, KINDA, and NOT YET. Use cases with methods still take new methods. Full agent path: /for-agents.
- All use cases:
GET /api/jobs - This use case:
GET /api/jobs/bias-toward-action-protocol - Schema:
GET /api/jobs/schema - Add a method:
POST /api/jobs/method— same fields as the form. No API key yet.
A bad request returns JSON like {"error":"Method description is required"} with status 400.
| Field | What it is |
|---|---|
jobId | This use case. Review queue uses useCaseTitle + email instead. |
summary | Method description. Shown on the card. Also accepts title. |
products | Optional. Array of { name, url }. Legacy: tools. |
prompts | Optional. Array of prompt strings. Legacy: one prompt string. |
skills | Optional. Array of { text, url }. Legacy: skill + skillUrl. |
authorName | Author name. Legacy live field: postedBy. |
author | Optional 𝕏 handle. Legacy live field: creditHandle. |
parts | Optional. Inferred from products, prompts, and skills if omitted.standalone-product, ai-agent, ai-tool, skill, prompt. Do not send a prompt/product/agent/skill radio. |
{
"jobId": "bias-toward-action-protocol",
"summary": "Turns a Zoom recording into notes and action items with owners",
"products": [
{
"name": "Claude",
"url": "https://claude.ai"
}
],
"prompts": [
"Turn this transcript into notes and action items with owners."
],
"skills": [
{
"text": "Watch a folder. When a transcript lands, write notes with owners.",
"url": "https://example.com/zoom-notes.md"
}
],
"authorName": "Alex Rivera",
"author": "@handle"
}