Task driven ai dev

Task-Driven AI Dev

Use this skill to create or operate a repo-level AI development harness. Keep the loop explicit: define the system, choose one task, implement it, validate it, record evidence, then stop.

Quick Start

1. Read the current artifact set. Prefer architecture.md, task.json, progress.txt, and project-config.json when they exist.
2. If the repo does not have those files, create them from assets/templates/.
3. Load references/workflow-contract.md before running an iteration.
4. Use scripts/select_next_task.py to choose the next ready task instead of scanning passes: false by hand.
5. Execute exactly one task unless the user explicitly asks for a broader phase.
6. Use scripts/validate_iteration.py before marking a task complete.

Operating Modes

• Bootstrap a new repo: create the artifact set, define commands, and seed the first backlog.
• Run one delivery iteration: select one ready task, implement, test, update artifacts, and stop.
• Unblock a stuck repo: diagnose why a task cannot be safely completed, record the block, and stop without marking success.
• Harden the harness: tighten testing gates, task structure, and async-state handling after regressions.

Core Workflow

1. Assess the repo state

• Identify whether the repo already has architecture, task, progress, and project-config artifacts.
• Identify the execution surface: app root, bootstrap commands, test commands, browser-test requirements, deployment blockers.
• Read recent git history to separate planned work from post-task bugfix work.

2. Normalize the artifact contract

• Keep one architecture document that explains the system and major constraints.
• Keep one task file that is the source of truth for backlog state.
• Keep one progress log with dated entries and explicit testing evidence.
• Keep one project-config file that tells future agents how to bootstrap and validate the repo.
• If a project also uses tool-specific memory files such as CLAUDE.md or AGENTS.md, treat them as adapters, not the canonical workflow definition.

See references/artifact-model.md for the detailed contract and template usage.

3. Select work conservatively

• Prefer one ready task with satisfied dependencies.
• Do not take a blocked task unless the user explicitly asks to unblock it.
• Do not bundle unrelated tasks only to reduce session count.
• When the backlog is complete and new defects appear, record them as explicit bugfix tasks or clearly separate them in progress history.

4. Decide: local or delegated?

Before implementing, check the task nature:

• Research with multiple independent targets → Launch parallel subagents (e.g., "research Cursor, Windsurf, and Continue UX")
• Isolated implementation with clear scope → Consider worker subagent
• Tiny or tightly coupled to current context → Stay local

See references/subagent-orchestration.md for recommended subagent roles (selection, worker, verification).

5. Execute one guarded iteration

• Bootstrap the environment only as needed.
• Implement the chosen task.
• Run the required validation commands.
• Do browser-driven validation when the task changes user-facing flows or core interactions.
• Update the task file and progress log only after the validation evidence is real.
• Stop after the task is complete or blocked.

See references/workflow-contract.md for the full iteration protocol.

Guardrails

• Never mark a task complete without evidence in testing artifacts or commands.
• Never hide a block by flipping passes to true.
• Never rely on client-side polling alone for long-running async workflows; persist or recover state server-side when possible.
• Never assume a public URL works for private storage.
• Never let progress.txt become fiction; it is memory for future agents, not marketing copy.

Load references/lessons-from-history.md when hardening an existing harness or debugging regressions that appeared after the main backlog was finished.

Bundled Resources

• assets/templates/architecture.md: starting point for the architecture contract.
• assets/templates/task.json: backlog template with dependencies, blocked state, and validation notes.
• assets/templates/progress.txt: durable progress and blocking log template.
• assets/templates/project-config.json: repo-specific command and artifact map.
• scripts/select_next_task.py: choose the next ready task deterministically.
• scripts/validate_iteration.py: validate task/progress consistency before completion.
• references/workflow-contract.md: one-task iteration protocol and done gate.
• references/artifact-model.md: artifact semantics and field expectations.
• references/subagent-orchestration.md: recommended subagent roles and delegation patterns.
• references/lessons-from-history.md: failure modes extracted from a real long-running AI-built repo.

Output Expectations

• When bootstrapping, leave the repo with a coherent artifact set and explicit commands.
• When iterating, leave the repo with one completed task or one clearly documented block.
• When hardening, prefer small protocol changes that improve truthfulness and recoverability over flashy automation.