slfg_skill

Overview

This skill implements a full autonomous engineering workflow that runs sequential and parallel phases using swarm mode for high-parallelism execution. It orchestrates planning, optional plan deepening, distributed work via agent swarms, parallel review and browser testing agents, and final resolution and walkthrough recording. The skill finishes by placing a video walkthrough into the PR and signaling completion.

How this skill works

Run the workflow start-to-finish without pausing between steps. It optionally invokes a housekeeping slash command, creates a plan, conditionally deepens that plan when risk or confidence gaps exist, and then launches a swarm of subagents to execute the work tasks. After work, it spawns parallel background swarm agents for review and browser testing, resolves any findings, records a feature walkthrough video into the PR, and outputs a completion promise.

When to use it

• Delivering complex engineering changes that require coordinated parallel tasks and verification.
• When plans touch high-risk areas: auth, security, payments, migrations, external APIs, or major rollouts.
• When you want automated end-to-end execution including review, testing, and final walkthrough recording.
• When you need reproducible, auditable task resolution from plan through PR-ready artifacts.
• When multiple agents or parallel test/review passes will speed delivery and reduce human coordination.

Best practices

• Always run the workflow uninterrupted to ensure step dependencies and parallel joins complete correctly.
• Use the deepen-plan step whenever plan confidence is low or risk is non-trivial; document the reason if skipped.
• Provide clear ARGUMENTS to the plan step so the swarm can generate coherent, prioritized tasks.
• Keep review and test expectations explicit so background agents can surface concrete findings.
• Ensure PR permissions and CI hooks accept uploaded walkthrough videos and artifacts before running finalization.

Example use cases

• Rolling out a payment provider integration with migrations: plan, deepen for safety, implement via swarm, test in browser, and record walkthrough.
• Refactoring auth flows across services where sequencing and rollout strategy need parallel work and careful review.
• Delivering a feature that touches multiple services and UIs, requiring parallel task execution and end-to-end browser verification.
• Handling a security-critical change that demands extra plan grounding, parallel code fixes, and formal review resolution.