swarm

Spawns parallel blind subagents to exhaustively search a corpus and unions their deduplicated findings.

1|Updated Jun 1, 2026
One-click install
npx skills add https://github.com/kridaydave/My_Skills --skill swarm-kridaydave
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: swarm
Source: https://github.com/kridaydave/My_Skills/tree/main/swarm
Command: npx skills add https://github.com/kridaydave/My_Skills --skill swarm-kridaydave

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? A single search pass over a codebase, document set, or research area has blind spots and quits early, so rare bugs, edge cases, and prior art get missed. Swarm fans out many independent finder subagents across distinct slices or angles, then dedups and unions their findings until no new results appear. ## Core Features & Use Cases - Parallel blind finders: Dispatches batches of subagents in a single message, each searching a different slice (module, file batch) or angle (data-flow, error-handling, spec-conformance) with no visibility into each other. - Loop-until-dry convergence: Runs rounds of searches, dedups every find against a running seen-set, and stops only after K consecutive rounds produce zero fresh findings. - Structured findings and coverage reporting: Forces a uniform find schema (location, what, severity, evidence) and outputs a severity-sorted deduped table plus a convergence curve and an explicit list of what was not searched. - Use Case: Ask for an exhaustive security audit of src/auth — swarm launches finders sliced by data-flow, error paths, and spec deviation, loops until two dry rounds, and returns every deduped vulnerability with evidence. ## Quick Start Ask the agent to swarm this codebase and find every security bug, running rounds of parallel finders until no new findings appear.

Frequently Asked Questions about swarm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run an exhaustive search for all bugs in a codebase?▼

Frame the hunt with a clear target and severity bar, slice the corpus by module or analysis angle, then dispatch parallel finder subagents in one message per round. Dedup each round's finds against everything seen and repeat until two consecutive rounds return nothing new.

What is the difference between swarm and council multi-agent patterns?▼

Council spawns different personas to diff their opinions on a decision, while swarm spawns many finders to union their findings on one search target. Council answers what should we do; swarm answers what is all in here.

How does swarm know when the search is complete?▼

It tracks a dry-round counter: when a round produces zero fresh finds after dedup against all prior rounds, the counter increments. The search stops after K consecutive dry rounds, with K defaulting to 2.

Why must swarm finders run in parallel with different slices?▼

Serial finders waste wall-clock time and risk context leakage between agents. Identical slices just re-find the same results, so each finder must get a distinct slice or angle to make coverage compound instead of overlap.

Can swarm fix the bugs it finds?▼

No. Swarm only enumerates and reports deduplicated, severity-sorted findings with evidence. Fixes are handed off to the owning persona or workflow after the sweep completes.