Take ideas to implementation with a planning-command pipeline
I'm working for myself (own product, decently profitable SaaS, been in production for 7 years now and used by actual customers), so I'm not sure if I fit your "employed" category or not. Task: The author implemented a 5-command pipeline (intent, impact analysis, iterative planning, execution, review) for their SaaS product, updating a shared Git plan file. Claude Code executes tasks guided by deterministic guardrails: thousands of custom ESLint rules and a Bash 'regulator' script that parses commands to approve or reject actions. The human reviews the plan file during the pipeline and inspects final code via git diff, while an orchestrator script converts plans into dynamic workflows to reduce LLM overhead.; tools: Claude Code. Human role: The original post describes the human role this way: so they all build on each other, and update the same plan file that sits in git and I review it as it goes through the pipeline2.; limitations: This setup requires a mature codebase with extensive custom linting rules and a pre-existing plan file format. The 'regulator' script needs deep parsing logic for Bash commands, which may be complex to maintain. Source reliability is secondary (single user anecdote); results may vary based on project size and agent behavior. Human review is mandatory for plan files and code diffs..
Tools
Published on Agent Work Atlas:
Overview
I'm working for myself (own product, decently profitable SaaS, been in production for 7 years now and used by actual customers), so I'm not sure if I fit your "employed" category or not.
Workflow
The author implemented a 5-command pipeline (intent, impact analysis, iterative planning, execution, review) for their SaaS product, updating a shared Git plan file. Claude Code executes tasks guided by deterministic guardrails: thousands of custom ESLint rules and a Bash 'regulator' script that parses commands to approve or reject actions. The human reviews the plan file during the pipeline and inspects final code via git diff, while an orchestrator script converts plans into dynamic workflows to reduce LLM overhead.
Reported result
The orchestrator script reduced token spend to approximately 20% of previous levels and cut workflow time from 5-6 hours to 20-30 minutes. The author reports feeling productive with mostly clean code output due to strict linting rules. No failures are explicitly detailed, but the system relies on continuous rule refinement when agents make errors.
Human role
The original post describes the human role this way: so they all build on each other, and update the same plan file that sits in git and I review it as it goes through the pipeline2.
Limitations
This setup requires a mature codebase with extensive custom linting rules and a pre-existing plan file format. The 'regulator' script needs deep parsing logic for Bash commands, which may be complex to maintain. Source reliability is secondary (single user anecdote); results may vary based on project size and agent behavior. Human review is mandatory for plan files and code diffs.
Try it
I am setting up a 5-command pipeline for my [replaceable: project type, e.g., SaaS] using Claude Code. First, create a shared plan file in Git. Implement commands for: 1) Intent capture (free text to structured goals), 2) Impact analysis (map to codebase), 3) Iterative planning (split into verifiable tasks), 4) Execution, and 5) Review. Add a 'regulator' script as a pre-tool hook for Bash commands to approve/reject actions based on rules. Use custom ESLint rules as post-write hooks. Run a sample task: [replaceable: specific feature request]. Show me the plan file updates and the regulator's decision log before proceeding. Do not execute real deployments without my confirmation.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.