slfg

SkillDev tools

Full autonomous research workflow using swarm mode for parallel execution

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the slfg skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/11-James-Traina-compound-science/skills/slfg/SKILL.md and read by ahel’s review.

Swarm-enabled LFG. Run these steps in order, parallelizing where indicated. Do not stop between steps — complete every step through to the end.

Sequential Phase

  1. /workflows:brainstorm $ARGUMENTS Gate: must produce a file in docs/brainstorms/ before proceeding.

  2. /workflows:plan Gate: must produce a file in docs/plans/ before proceeding.

  3. /workflows:work — Use swarm mode: Break the plan into independent tasks and launch parallel subagents via Task tool to build them concurrently. Each subagent handles one task from the plan. See references/orchestration-patterns.md for parallel dispatch patterns. Gate: must produce at least one code change (committed or staged) before proceeding. If work fails with no changes, stop and report the failure.

Parallel Phase

After work completes, launch steps 4 and 5 as parallel swarm agents (both only need completed code to operate):

  1. /workflows:review — spawn as background Task agent
  2. /workflows:compound — spawn as background Task agent

Wait for both to complete before finishing.

Output

When all steps are done, output:

Research workflow complete.

Brainstorm: [brainstorm file path]
Plan: [plan file path]
Work: [summary of implementation]
Review: [summary of findings]
Documentation: [docs/solutions/ path if created]

Start with step 1 now.

Signals

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Sep 2026
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Item type
skill
Key
slfg
Source
github.com/brycewang-stanford/auto-empirical-research-skills