Doing A Simple Two Stage Fanout
Fan out a large corpus across Worker, Critic, and Summarizer subagents with computed layout
Test report
- Verdict
- Tested · Works
- Score
- Tested
- Jul 31, 2026
- Environment
- Claude Code 2.x (agent harness)
- Upstream re-checked
- Aug 10, 2026 · 13e70cd
Fetched SKILL.md plus both referenced files (compute_layout.py and diagram-templates.md, each HTTP 200); the script scanned clean (no curl|sh, base64, network, or eval). Ran compute_layout.py on the SKILL's own documented example (--corpus-chars 800000 --segments-per 3 --reviews-per 2) and got exactly the numbers the skill claims: 6 segments, 2 workers, 4 critics (C01-C04) matching its assignment table. OUTPUT test on a 500k-char prose corpus: baseline naive reasoning (125k tokens fits in a 200k window) gives 1 agent / no fan-out, whereas the skill's script reserves 35% and divides by SEGMENTS_PER to yield 4 segments / 2 workers / 3 critics / 6 agents - a concrete, defensible divergence I verified by running it. I did not execute the full multi-subagent dispatch end-to-end (no live Worker/Critic/Summarizer run), only the deterministic layout-computation slice.
Scored on four weighted criteria — install, triggering, output vs. baseline, docs. How scoring works
- Installs cleanly 5/5
- Triggers reliably 5/5
- Output vs. baseline 7/10
- Docs & honesty 5/5
What Doing A Simple Two Stage Fanout does
Orchestrates a two-stage fan-out for analyzing a corpus larger than one agent's context: parallel Worker subagents each analyze a slice, Critic subagents review the worker reports for gaps and contradictions, and a single Summarizer synthesizes a final report. Triggers when the user asks to analyze, review, or summarize a large body of text, code, or data that exceeds a single agent's context, and includes a bundled Python script that computes segment/worker/critic counts plus task-graph and failure-recovery guidance.
How to install Doing A Simple Two Stage Fanout
git clone --depth 1 https://github.com/ed3dai/ed3d-plugins.git /tmp/doing-a-simple-two-stage-fanout-src
mkdir -p ~/.claude/skills
cp -R /tmp/doing-a-simple-two-stage-fanout-src/plugins/ed3d-basic-agents/skills/doing-a-simple-two-stage-fanout ~/.claude/skills/doing-a-simple-two-stage-fanout
# Bundled compute_layout.py needs only python3 (stdlib argparse/math/os) - no pip deps.
# Runtime needs: Task tool (subagent dispatch), TaskCreate/TaskUpdate, AskUserQuestion, plan mode.
# Plugin-marketplace alternative: add the ed3dai/ed3d-plugins marketplace, install the ed3d-basic-agents plugin.
Skills live in ~/.claude/skills/ (global) or .claude/skills/
(per-project). Restart Claude Code after installing.
Commands — how to trigger Doing A Simple Two Stage Fanout
-
/doing-a-simple-two-stage-fanoutFan out a large corpus across Worker, Critic, and Summarizer subagents with computed layout
It also activates on plain-language prompts like these:
-
Analyze this entire codebase across many subagents -
Fan out this large dataset to worker agents -
Summarize this huge document set in parallel
Frequently asked questions
- Is the Doing A Simple Two Stage Fanout skill free?
- Yes. The skill itself is free from ed3dai/ed3d-plugins. SkillProof publishes the install command and an independent test verdict at no cost.
- Does Doing A Simple Two Stage Fanout work with Claude Code?
- We tested it with Claude Code 2.x (agent harness) on Jul 31, 2026. Verdict: Tested · Works. Fetched SKILL.md plus both referenced files (compute_layout.py and diagram-templates.md, each HTTP 200); the script scanned clean (no curl|sh, base64, network, or eval). Ran compute_layout.py on the SKILL's own documented example (--corpus-chars 800000 --segments-per 3 --reviews-per 2) and got exactly the numbers the skill claims: 6 segments, 2 workers, 4 critics (C01-C04) matching its assignment table. OUTPUT test on a 500k-char prose corpus: baseline naive reasoning (125k tokens fits in a 200k window) gives 1 agent / no fan-out, whereas the skill's script reserves 35% and divides by SEGMENTS_PER to yield 4 segments / 2 workers / 3 critics / 6 agents - a concrete, defensible divergence I verified by running it. I did not execute the full multi-subagent dispatch end-to-end (no live Worker/Critic/Summarizer run), only the deterministic layout-computation slice.
- What is the Doing A Simple Two Stage Fanout SkillProof Score?
- 8.8/10 — installs cleanly 5/5, triggers reliably 5/5, output vs. baseline 7/10, docs & honesty 5/5.
- How do I install Doing A Simple Two Stage Fanout?
- Copy the install command from this page, run it in your terminal, and restart Claude Code. Skills live in ~/.claude/skills/ (global) or .claude/skills/ inside a project.
- Can I use Doing A Simple Two Stage Fanout with Cursor, Copilot, Gemini CLI, Codex or other AI tools?
- The SKILL.md format is native to Claude (Claude Code, Desktop, claude.ai). The instructions inside adapt to other assistants: Cursor rules, GitHub Copilot instructions, Windsurf rules, Custom GPTs, AGENTS.md for OpenAI Codex, and GEMINI.md for Google Gemini CLI — our conversion guides cover each, and the free converter on the tools page does the wrapping for you.