workflow-orchestration

workflow-orchestration is a cursor rule for coding agents from robotaitai/project-bedrock. It costs 772 tokens per session, scanned A, original, MIT.

Rules for planning, delegating, implementing, and checking work done with AI coding agents.

In plain words
What is it for?
Use them to plan multi-step changes, assign focused work to other agents, record lessons after corrections, and verify code with tests, logs, or behavior comparisons.
Why use it?
They reduce ambiguity on larger tasks and require the result to be checked before it is treated as complete.

Cursor rule

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add rules/robotaitai/project-bedrock/workflow-orchestration
Clone the repo
git clone --depth 1 https://github.com/robotaitai/project-bedrock

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 workflow-orchestration

README.md
[![agentmods](https://agentmods.dev/badge/rules/robotaitai/project-bedrock/workflow-orchestration.svg)](https://agentmods.dev/rules/robotaitai/project-bedrock/workflow-orchestration)
Your own site
<a href="https://agentmods.dev/rules/robotaitai/project-bedrock/workflow-orchestration"><img src="https://agentmods.dev/badge/rules/robotaitai/project-bedrock/workflow-orchestration.svg" alt="Measured on agentmods" height="20"></a>
Per session 772 This file is loaded in full into every session.
When invoked 772 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
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 $0.00772 $0.00772
Opus 5 $0.00386 $0.00386
Sonnet 5 $0.00154 $0.00154
Haiku 4.5 $0.00077 $0.00077

Measured 4d ago against content hash ae0039fe25d6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

workflow-orchestration 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 4d ago.

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.

assets/rules/workflow-orchestration.mdc · 94 lines

How it starts

The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Workflow Orchestration

1. Plan First (Default)

  • Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions)
  • If something goes sideways, STOP and re-plan immediately - do not keep pushing
  • Use plan mode for verification steps, not just building
  • Write detailed specs upfront to reduce ambiguity

2. Subagent Strategy

  • Use subagents liberally to keep the main context window clean
  • Offload research, exploration, and parallel analysis to subagents
  • For complex problems, throw more compute at it via subagents
  • One task per subagent for focused execution

3. Self-Improvement Loop

  • After ANY correction from the user: update tasks/lessons.md with the pattern
  • Write rules for yourself that prevent the same mistake
  • Ruthlessly iterate on these lessons until mistake rate drops
  • Review lessons at session start for relevant project

4. Verification Before Done

  • Never mark a task complete without proving it works
  • Diff behavior between main and your changes when relevant
  • Ask yourself: "Would a staff engineer approve this?"
  • Run tests, check logs, demonstrate correctness

5. Demand Elegance (Balanced)

  • For non-trivial changes: pause and ask "is there a more elegant way?"
  • If a fix feels hacky: "Knowing everything I know now, implement the elegant solution"
  • Skip this for simple, obvious fixes - do not over-engineer
  • Challenge your own work before presenting it

6. Autonomous Bug Fixing

  • When given a bug report: just fix it. Do not ask for hand-holding
  • Point at logs, errors, failing tests - then resolve them
  • Zero context switching required from the user
  • Go fix failing CI tests without being told how

Task Management

  1. Plan First: Write plan to tasks/todo.md with checkable items
  2. Verify Plan: Check in before starting implementation
  3. Track Progress: Mark items complete as you go
  4. Explain Changes: High-level summary at each step
  5. Document Results: Add review section to tasks/todo.md
  6. Capture Lessons: Update tasks/lessons.md after corrections

Read the full file on GitHub · 94 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. 4d ago First seen · 94 lines · 772 tokens per session scan A ae0039fe25d6

Subscribe to this mod's changes

workflow-orchestration is a cursor rule published in the GitHub repository robotaitai/project-bedrock (45 stars, last pushed 14d ago), licensed MIT. It adds 772 tokens to every session, about $0.0039 per session on Opus 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 2026-08-30.