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Use these guides to make plans, specifications, agent instructions, review evidence, and handoffs easier to inspect and maintain. Learn pages explain a durable question. Product commands stay in Open Source, and reusable artifacts stay in Templates.

Planning and specifications

What is a product requirements document?

Define product intent, review it with people and agents, and stop before technical design.

Approve an AI coding plan before execution

Choose the right planning depth, annotate the proposal, and inspect revisions before implementation.

Plan Diff: see what changed

Compare revised plans without rereading every unchanged section.

HTML vs Markdown for agent work

Choose durable text or an interactive explainer based on the decision readers need to make.

Living documentation for AI agents

Keep current system truth separate from the plans and decisions that produced it.

Agent instructions

Write a good AGENTS.md

Give agents exact commands, boundaries, and references without creating an instruction dump.

Compound plan feedback

Find repeated local feedback and turn the useful patterns into maintained instructions.

Design a coding-agent handoff

Connect returned work to the request, revision, evidence, risks, and next action.

Markdown and document collaboration

Annotate a website or HTML file

Import a URL or render local HTML for agent feedback, or use App Notes to annotate a live page.

Understand AI-generated code before shipping

Connect intended behavior, implementation, validation, and current documentation.

Code review

Local diff review for coding agents

Return line-specific feedback from a local browser diff.

What is a revision diff?

Understand cumulative review across pushes, rebases, and force-pushes.

What are code review artifacts?

Use screenshots, video, HTML, Markdown, and other evidence beside code.

Review pull request artifacts

Check whether visual and operational evidence matches the current revision.

Why AI pull requests are harder to review

Control review load without trusting a generated summary as proof.

AI-development workflows

Stay in the loop and own quality

Keep human attention at the points where intent becomes code and code becomes a shipping decision.

What is an AI artifact?

Distinguish inspectable outputs from chat responses and source records.

What is context rot?

Recognize when more context reduces an agent’s ability to use the right evidence.

What is an LLM codebase wiki?

Use generated repository maps without confusing them with maintained authority.

Long-horizon tasks for AI agents

Preserve state, evidence, and stop conditions across long-running work.

Run an AI agent night shift

Prepare a bounded goal and inspect real outputs the next morning.
Reviewed July 18, 2026. Maintained by the Plannotator documentation team.
Last modified on July 19, 2026