Setup Agent Workflow

Set Up Agent Workflow

Make the repository teach the next agent how to work, and make the correct path easy to follow. The outcome is a demonstrated workflow, not a larger pile of instructions.

Work through understand → run → change → check → observe → preserve what was learned. Improve whichever links are missing. For an established repository, keep the working foundation and address its actual friction. For a new repository, establish a small foundation around one representative task before expanding.

This skill is informed by Lauren Tan's talk about engineering an environment for agents. Verification, feature maps, architectural constraints, reusable skills, and maintenance are parts of the same approach. See principles and attribution for the source and the distinction between its ideas and this implementation.

1. Learn the repository and choose a useful task

Read the applicable agent instructions, architecture and contribution docs, package/build commands, CI, and the code involved in the current request. Inspect existing skills, test helpers, dev environments and recent relevant fixes. Follow actual entry points through the code; do not infer the architecture from directory names alone.

Establish:

• Product: who uses it, through which surfaces, and what success looks like. Include APIs, CLI and agent-facing tools as well as screens.
• Structure: package boundaries, canonical implementations, important invariants, generated files, and where durable state lives.
• Development: installation, start/readiness/stop, configuration, authentication, fixtures, focused checks and CI. Observe which commands work.
• Friction: instructions repeatedly supplied by people, agent mistakes that recur, ambiguous paths, throwaway probes, and gaps between passing tests and observed behaviour.

Use the task or reported failure as the first slice. If none is provided, choose a small representative user journey or recurring maintenance task from the repository. Ask only for missing information that materially changes the work; continue independent discovery while waiting.

State the selected slice, observed gaps and intended edits briefly. For a setup request, proceed with concrete implementation within its scope. For an audit or advice request, report findings without silently turning it into a rewrite. Record existing failures separately so new work does not inherit a false green baseline.

2. Put each correction in the strongest suitable place

The codebase itself is the example agents copy. Prefer a correction that survives a missed instruction:

Choose the smallest intervention that addresses evidence you found. This is a preference order, not a demand to redesign the architecture before doing useful work. An invasive migration may be a follow-up; a precise guard can prevent further spread meanwhile. Do not invent generic wrappers, a new framework, lint rules for taste, or tests that repeat implementation details.

3. Make the development loop reliable

Reuse the repository's language, package manager and tools. Establish or repair discoverable commands for setup, running the relevant surface, focused checks, and inspecting failures. Put commands in the existing task runner rather than a parallel command system.

• Validate documented prerequisites and readiness conditions. Use the intended runtime, not a substitute that bypasses its important behaviour.
• Give concurrent work isolated ports, state and test data where needed. Cleanup must stop only resources started by that session, and useful evidence must survive it.
• Use synthetic fixtures and existing local service helpers. Document required configuration by name; do not copy real credentials or customer data into skills, fixtures or proofs.
• Keep external service stand-ins at the external boundary. Preserve product authorization, validation and persistence logic. If a real provider is required, name that prerequisite and the limit of offline checks.
• Failures should identify the unmet prerequisite or failed expectation and give a useful next command. Add diagnostics only where they remove observed uncertainty.

A small library may need only its test command and a public API example. A web app may need a browser and isolated database. A native app may need simulator controls. Do not require daemons, containers or a universal driver for every repository.

4. Make knowledge discoverable without duplicating it

Agent entry point

Update the existing AGENTS.md or equivalent. Preserve useful rules and local scope; merge with what is there. Keep one authoritative source and use imports/links for supported agent hosts rather than copying divergent manuals.

The entry point should answer, using actual paths and commands:

1. What is this product, and where are its major surfaces?
2. How do I run it and check the area I changed?
3. Which constraints are easy to miss, and where are they enforced or explained?
4. Where do I find the feature map, task skills, architecture and operational instructions?
5. What must change alongside code when behaviour or the workflow changes?

Keep detailed procedures in their owning docs or skills. Avoid restating syntax, generic engineering advice, or long lists of commands copied from the task runner. Guidance should route an agent to reliable knowledge, not consume the entire context window.

Local task skills

Add a skill when there is a repeatable task with meaningful decisions, or repair an existing one. Examples include reproducing a support report, debugging a latency regression, adding a supported integration, or making and verifying a UI change. Do not manufacture a skill for every function or one-line command.

Each skill needs a clear trigger, prerequisites, the repository's real commands and paths, the important decisions, observable completion criteria, and recovery for known failure cases. Extract reusable scripts from proven work; avoid teaching each agent to write the same script again. Test commands before presenting them as canonical.

Use the repository's existing skill location and host setup. Where none exists, .agents/skills/<task>/SKILL.md is a reasonable canonical location; link it from agent guidance. Add host links only for hosts the repository uses. Do not change global agent settings or introduce telemetry as part of repository setup.

Feature knowledge and verification

For product work, map the chosen area in the user's language: what it does, the ways people or agents reach it, important states, expected results and gotchas. Connect that map to repeatable checks and retained evidence. File lists alone do not explain a product.

If a driver/map already exists, improve it. If runtime proof needs reusable controls, use the installed verify-harness skill for that part. If it is unavailable, build the smallest equivalent recipe around existing CLI, API, browser or device tools; do not block setup on installing another skill.

When asked for the whole product, inventory all user and agent entry points and map them, then report the actual proof depth separately. Keep driver code in normal application tooling and fixtures with tests; the skill teaches the workflow. A whole-product map can expose explicit unverified paths while controls grow from real use.

Every proof names its layer, source/build identity, actions, expectations, observed results and fixtures. Distinguish tests, component/integration checks, a running local application and live-provider checks. Observe the user-visible result and persisted state when the behaviour has both. Keep inaccessible paths as gaps. A detector passing does not prove a build works; a build passing does not prove the user journey works.

5. Demonstrate the loop, then maintain it

Work through the selected task using the repository entry point and commands you just established:

1. Start from a realistic user report or change request. Find the area through the documented map and routing.
2. Run the relevant surface or check in fresh state. Capture the baseline or reproduce the failure.
3. Make the smallest warranted change. For a regression, show the relevant check fails before the fix when feasible. A healthy setup can demonstrate an existing behaviour without inventing a product change.
4. Run focused checks, required repository checks, and the observable recipe. Retain the evidence and confirm cleanup.
5. Follow the instructions again with fresh state. Correct missing prerequisites and undocumented assumptions. If an independent agent is already available and appropriate, a cold start with only repository guidance is useful; otherwise identify the rehearsal as self-run.

Use acceptance scenarios to review setup quality. A documentation pass is not proof that a new workflow executes. When credentials or infrastructure block a path, complete the independent work and report the precise unproven path; do not weaken product controls to make the demonstration pass.

Keep the maintenance loop part of normal changes:

• When behaviour changes, update its feature recipe and check in the same change.
• When a command or boundary changes, update its single authoritative definition and the referring skill.
• When a correction repeats, decide whether it belongs in code, enforcement, tooling or guidance. Retire the obsolete workaround.
• Add narrow CI checks for concrete drift risks, such as broken map references or forbidden imports. Do not introduce a generic skill scoring system or blanket coverage quota.

Start scheduled maintenance, issue-to-agent automation or wider parallel work only when it solves an observed need and is within the user's request. The reliable local loop comes first. Do not silently configure bots, trackers or external posting.

Handoff

Report what changed and the problem each change addresses; the canonical entry point and commands; what actually ran and its proof layer; and the most important remaining gap. Link the artifacts. State whether the rehearsal was self-run or independent.

Do not declare the whole repository “agent ready” because instructions and tests exist. Success is that the next agent can perform the demonstrated task with less hidden knowledge, and the repository has a clear path for improving the next task.

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