Skip to main content

AI Coding

What separates AI coding environments that amplify output from ones that slow you down?

The answer is signal discipline — less context, not more. Most setups fail by adding too much.

Providers​

  • Config Architecture — Agent-agnostic setup: rules, hooks, skills
  • Claude Code — Hooks, settings, Claude-specific patterns
  • Gemini CLI — 1M+ context window, repo-wide analysis
  • Codex CLI — AGENTS.md standard, second-opinion verification

Principles​

  • Agent Context Prompts
  • Command Prompts
  • Skills

Context Files​

Agent MD files (CLAUDE.md, AGENTS.md) shape what the model sees. Used well, they correct consistent errors. Overused, they add noise: LLM-generated context files decrease performance by 3% on average and increase costs by 20%+.

PrincipleWhat to Do
Minimize contextInclude only essential information — irrelevant context distracts
Correct consistent errors onlyAdd rules when the model reliably makes the same mistake
Prioritize codebaseIf the model struggles, fix the codebase first — move confused elements, improve tests
Read agent strugglesConfusion signals an unclear codebase, not a prompting problem
Watch outputsNote what files the agent reads and how long tasks take — builds intuition

Agent Prompts​

System prompt (Mindset)

Command Prompts​

Instructions (JTBD)

Skills​

  • skills.sh — Open source AI coding skills library

Hooks​

Dev Workflow​

AI agents operate in the same two-stream pattern as human engineers. See Dev Workflow for the full worktrees pattern.

StreamAgent readsAgent produces
BuildPRD spec → project-from-prdNew capabilities
FixIssues Log (src/pages/priorities/) → fix-from-issuesBug fixes

Rule: One session, one stream. Never mix building new features with fixing existing issues in the same agent context.

Agent-Operable Codebases​

If the same agent workflow happens twice, do not keep it as a chat habit. Turn it into an operating surface: a skill, command, hook, test, log query, documentation sweep, or bounded loop with a stop condition.

Use the Agent-Operable Codebase checklist before scaling from one agent to many. The checklist is especially important once the bottleneck moves from code generation to review, CI, merge order, and deploy coordination.

Failure Modes​

  • Adding context rules that should be tests.
  • Treating agent confusion as a prompting problem when the codebase is unclear.
  • Scaling to many agents before the merge path is visible.

Context​

  • Dev Workflow — Two-worktree pattern: build vs fix
  • Agent-Operable Codebase — checks for safe bounded agent loops
  • Priorities — Active PRDs and build queue
  • Business Factory — PRD specs for the build stream
  • Config Architecture — Rules, hooks, skills setup
  • Reality Scoreboard — proof that the workflow improved

Questions​

What happens when the agent's context file corrects errors the codebase should prevent?

  • If agent confusion signals an unclear codebase, how do you measure codebase clarity?
  • When does adding a rule to CLAUDE.md become a substitute for fixing the actual code?