Agent skill

Batching Patterns

by MadAppGang in MadAppGang/claude-code

Batch all related operations into single messages for maximum parallelism and performance.

MITAuto-check passed

Install Batching Patterns

skills CLI
$ npx skills add MadAppGang/claude-code --skill batching-patterns -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install MadAppGang/claude-code batching-patterns --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/MadAppGang/claude-code.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/multimodel/skills/batching-patterns .claude/skills/batching-patterns && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
batching-patterns
GitHub stars
283
Token cost
~3.3k tokens
SKILL.md length
709 words
Files
1
Skills in repo
69
Repo updated
First seen
Licence
MIT

At a glance

Batch all related operations into single messages for maximum parallelism and performance.

  • Works in 3 steps: Within ONE message: Same tool type calls… → Across SEPARATE messages: Tool calls… → Mixed tool types in ONE message: May…
  • Launching multiple agents
  • SKILL.md covers Overview, The Batching Principle, Batching Patterns by Tool Type and The 4-Message Pattern…, plus 7 more sections
  • Calls npm

What it does

Batching Patterns is an agent skill from MadAppGang/claude-code. Batch all related operations into single messages for maximum parallelism and performance. Use when launching multiple agents, reading multiple files, running parallel searches, optimizing workflow speed, or avoiding sequential execution bottlenecks. Trigger keywords - "batching", "parallel", "single message", "golden rule", "concurrent", "performance", "sequential bottleneck", "speed optimization".

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: claude code plugins marketplace. The licence is MIT.

When your agent uses it

  • Launching multiple agents
  • Reading multiple files
  • Running parallel searches
  • Optimizing workflow speed

Example prompts

  • “batching”
  • “parallel”
  • “single message”
  • “/batching-patterns”

Requirements

  • Node.js

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Within ONE message: Same tool type calls execute in parallel (no data dependencies)
  2. Across SEPARATE messages: Tool calls execute sequentially
  3. Mixed tool types in ONE message: May force sequential execution

What it can do on your machine

Read from SKILL.md and the folder at commit 6097ad4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Batching Patterns loads about 3.3k tokens when it runs. Until then it costs about 105 tokens; SKILL.md has 709 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~105
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from MadAppGang/claude-code at commit 6097ad4, republished under its MIT licence (© MadAppGang). 709 words, ~3,339 tokens.

Download SKILL.mdSave it as .claude/skills/batching-patterns/SKILL.md (or your agent's skills folder).
name
batching-patterns
description
Batch all related operations into single messages for maximum parallelism and performance. Use when launching multiple agents, reading multiple files, running parallel searches, optimizing workflow speed, or avoiding sequential execution bottlenecks. Trigger keywords - "batching", "parallel", "single message", "golden rule", "concurrent", "performance", "sequential bottleneck", "speed optimization".
version
0.1.0
tags
orchestration, batching, parallel, performance, golden-rule, single-message
keywords
batching, parallel, single-message, golden-rule, concurrent, performance, anti-pattern, sequential, optimization
plugin
multimodel
updated
2026-01-28

Batching Patterns

Version: 1.0.0 Purpose: The Golden Rule of Claude Code execution -- batch operations for maximum parallelism Status: Production Ready

Overview

The Golden Rule of Claude Code performance:

"1 MESSAGE = ALL RELATED OPERATIONS"

Every tool call within a single message executes in parallel (when no dependencies exist). Every separate message introduces a sequential round-trip. This distinction is the difference between a 2-minute workflow and a 10-minute one.

Sequential (5 separate messages):
  Message 1: Task(agent-1)  →  2 min
  Message 2: Task(agent-2)  →  2 min (waits for agent-1!)
  Message 3-5: ...           →  2 min each
  Total: ~10 minutes (serial)

Batched (1 message):
  Message 1: Task(agent-1) + Task(agent-2) + ... + Task(agent-5)
  Total: ~2 minutes (parallel, limited by slowest agent)
  Speedup: 5x

Each message carries API round-trip overhead. Batching eliminates N-1 round-trips.


The Batching Principle

Claude Code has a simple execution model:

  1. Within ONE message: Same tool type calls execute in parallel (no data dependencies)
  2. Across SEPARATE messages: Tool calls execute sequentially
  3. Mixed tool types in ONE message: May force sequential execution
Same tool type in one message = PARALLEL
  Task(A) + Task(B) + Task(C)  →  All run simultaneously

Different tool types in one message = SEQUENTIAL (often)
  TaskCreate(...) + Task(A) + Bash(...)  →  May run one at a time

Separate messages = ALWAYS SEQUENTIAL
  Message 1: Task(A)  →  completes first
  Message 2: Task(B)  →  starts only after A finishes

Why Same Tool Type Signals Independence:

Multiple calls of the same tool type (all Task, all Read, all Grep) signal independent operations that can run concurrently. Mixing tool types breaks this signal because different types often have implicit ordering requirements.


Batching Patterns by Tool Type

Task Tool Batching

The most impactful tool to batch -- each agent runs for minutes.

❌ Sequential (3 separate messages):
  Message 1: Task(security-reviewer)   → 3 min
  Message 2: Task(perf-reviewer)       → 2 min (waits!)
  Message 3: Task(a11y-reviewer)       → 2 min (waits!)
  Total: ~7 minutes

✅ Batched (1 message):
  Task(security-reviewer) + Task(perf-reviewer) + Task(a11y-reviewer)
  Total: ~3 minutes (3 agents parallel)
  Speedup: 2.3x

Agents are independent when they: read same input but don't modify it, write to different output files, perform different analysis, don't need each other's results.

File Operations Batching
❌ Sequential Reads:
  Message 1: Read("src/auth.ts")        → round-trip
  Message 2: Read("src/middleware.ts")   → round-trip
  Message 3: Read("src/routes.ts")      → round-trip
  Total: 3 round-trips

✅ Batched Reads:
  Read("src/auth.ts") + Read("src/middleware.ts") + Read("src/routes.ts")
  Total: 1 round-trip (3x faster)
❌ Sequential Searches:
  Message 1: Grep("TODO", path="src/")
  Message 2: Grep("FIXME", path="src/")
  Message 3: Glob("**/*.test.ts")

✅ Batched Searches:
  Grep("TODO") + Grep("FIXME") + Glob("**/*.test.ts")
  All parallel in 1 round-trip
⚠️ Write/Edit Caution:
  ❌ Edit("src/auth.ts", change_1) + Edit("src/auth.ts", change_2)  ← Conflict!
  ✅ Edit("src/auth.ts", change_1) + Edit("src/routes.ts", change_2)  ← Safe
  Rule: Different files = safe to batch. Same file = must be sequential.
Tasks Batching
❌ Individual Calls (5 round-trips):
  TaskCreate({ id: "1", title: "Step 1", status: "pending" })
  TaskCreate({ id: "2", title: "Step 2", status: "pending" })
  ...

✅ Single Call (1 round-trip):
  TaskCreate({ id: "1", title: "Step 1", status: "pending" })
  TaskCreate({ id: "2", title: "Step 2", status: "pending" })
  TaskCreate({ id: "3", title: "Step 3", status: "pending" })
  # All in same message = parallel execution
Bash Batching
❌ Sequential Independent Commands:
  Message 1: Bash("npm run lint")
  Message 2: Bash("npm run typecheck")
  Message 3: Bash("npm run test")

✅ Parallel Independent Commands (1 message):
  Bash("npm run lint") + Bash("npm run typecheck") + Bash("npm run test")

✅ Dependent Commands Chained (1 Bash call):
  Bash("mkdir -p ai-docs && cp template.md ai-docs/plan.md")

The 4-Message Pattern (Reference)

The canonical batching template from multi-agent-coordination:

Message 1: Preparation (Bash/Write only)
  - Create directories, write context files, validate inputs
  - NO Task calls, NO Tasks

Message 2: Parallel Execution (Task only)
  - ALL agents in SINGLE message, ONLY Task calls
  - Same tool type = true parallel execution

Message 3: Consolidation (Task only)
  - Consolidation agent reads all output files

Message 4: Present Results
  - Show user final consolidated results

Why 4 messages: Each depends on the previous (agents need context, consolidation needs agent outputs, presentation needs consolidated result). This is the minimum sequential steps.


Anti-Patterns (Critical)

Anti-Pattern 1: Sequential Task Launches
❌ Message 1: Task(agent-1)     // 2 min
❌ Message 2: Task(agent-2)     // 2 min (waits for agent-1!)
❌ Message 3: Task(agent-3)     // 2 min (waits for agent-2!)
   Total: 6 minutes

✅ Message 1: Task(agent-1) + Task(agent-2) + Task(agent-3)  // All parallel!
   Total: 2 minutes (3x speedup)
Anti-Pattern 2: Mixing Tool Types in Execution Message
❌ Mixed Tools (sequential):
  TaskCreate({...})             // Tool type A
  Task(security-reviewer)       // Tool type B
  Bash("echo 'starting'")      // Tool type C
  Task(perf-reviewer)           // Tool type B

✅ Separated (parallel execution):
  Message 1: TaskCreate({...}) + Bash("echo 'starting'")   // Preparation
  Message 2: Task(security-reviewer) + Task(perf-reviewer) // Execution (parallel)
Anti-Pattern 3: Individual Tasks Calls
❌ 5 separate TaskCreate calls across messages = 5 round-trips
✅ 5 TaskCreate calls in 1 message = 1 round-trip (parallel)
Anti-Pattern 4: Sequential File Reads
❌ 5 separate Read messages = 5 round-trips
✅ 5 Read calls in 1 message = 1 round-trip (5x faster)
Anti-Pattern 5: Unnecessary Dependencies
❌ False Dependencies:
  Message 1: Grep("authentication")  // Wait...
  Message 2: Grep("authorization")   // Wait...
  Message 3: Glob("**/*.middleware.ts")  // Wait...

✅ Recognize Independence:
  Message 1: Grep("authentication") + Grep("authorization") + Glob("**/*.middleware.ts")

Dependency Detection Checklist:

Before splitting across messages, ask:

  1. Does B need the OUTPUT of A? If no, batch.
  2. Do both write to the SAME file? If no, batch.
  3. Does the ORDER matter? If no, batch.
  4. Is B CONDITIONAL on A's result? If no, batch.

If all four are "no," operations are independent -- batch them.


When NOT to Batch

  • Data dependencies: Developer needs architect's plan before implementing
  • Same-file modifications: Two edits to the same file must be sequential
  • Sequential phases: Plan -> Implement -> Test -> Review (each depends on previous)
  • Order-dependent operations: npm install && npm build && npm test
  • Conditional execution: Next action depends on previous result (test pass/fail)
CORRECT - Sequential:
  Message 1: Task(architect)   → writes plan.md
  Message 2: Task(developer)   → reads plan.md (depends on Message 1)

CORRECT - Chained:
  Bash("npm install && npm run build && npm run test")

Performance Impact

Scenario: 5-Agent Code Review
  Sequential: 10 min + 5 round-trips  |  Batched: 3 min + 1 round-trip  |  3.3x

Scenario: 7 Codebase Searches
  Sequential: ~21 seconds             |  Batched: ~3 seconds              |  7x

Scenario: 10 File Reads
  Sequential: ~20 seconds             |  Batched: ~2 seconds              |  10x

Speedup Formula:

Sequential = N * (execution + round_trip)
Batched    = max(executions) + round_trip
Speedup    = approximately N (for identical operations)

Context Benefits: Fewer messages = less context consumed = more room for useful work.


Show full SKILL.md (309 more words)Show less

Best Practices

Do:

  • ✅ Launch ALL independent agents in a single message (biggest speedup)
  • ✅ Read all needed files in one message before processing
  • ✅ Run all independent searches (Grep + Glob) in parallel
  • ✅ Create all task items in a single message
  • ✅ Use the same tool type for parallel operations
  • ✅ Check the dependency checklist before splitting across messages
  • ✅ Follow the 4-Message Pattern for multi-agent workflows
  • ✅ Chain dependent Bash commands with && in a single call

Don't:

  • ❌ Launch agents in separate messages (forces sequential)
  • ❌ Mix tool types in execution messages (breaks parallelism)
  • ❌ Read files one at a time across separate messages
  • ❌ Create individual TaskCreate calls across separate messages
  • ❌ Batch operations that write to the same file
  • ❌ Batch operations where B needs A's output
  • ❌ Assume all operations can be batched (check dependencies)

Examples

Example 1: Multi-Model Code Review (Batch 5 Reviewers)
Message 1: Preparation
  Bash("mkdir -p ai-docs/reviews")
  Write("ai-docs/review-context.md", code_context)

Message 2: Parallel Execution (5 Task calls)
  Task(security-reviewer)      → ai-docs/reviews/security.md
  Task(performance-reviewer)   → ai-docs/reviews/performance.md
  Task(accessibility-reviewer) → ai-docs/reviews/accessibility.md
  Task(code-quality-reviewer)  → ai-docs/reviews/quality.md
  Task(architecture-reviewer)  → ai-docs/reviews/architecture.md
  ALL 5 execute simultaneously

Message 3: Consolidation
  Task(review-consolidator) → ai-docs/consolidated-review.md

Message 4: Present Results
  Sequential: 5 * 2 min = 10 min  |  Batched: ~4 min  |  2.5x speedup
Example 2: Codebase Exploration (Batch Searches)
Message 1: Parallel Searches (7 operations, 1 message)
  Grep("authenticate")  + Grep("authorize")  + Grep("jwt|token")
  + Grep("middleware.*auth") + Glob("**/auth*.ts")
  + Glob("**/middleware/**/*.ts") + Glob("**/*.test.ts")

Message 2: Parallel File Reads (all discovered files)
  Read("src/auth/authenticate.ts") + Read("src/auth/authorize.ts")
  + Read("src/middleware/auth.middleware.ts")
  + Read("src/services/token.service.ts")
  + Read("src/routes/auth.routes.ts") + Read("tests/auth/auth.test.ts")

Total: 2 messages, ~5 seconds
Sequential: 13 messages, ~30+ seconds  |  Speedup: 6x+
Example 3: Multi-Phase Workflow (Mixed Batching/Sequential)
Message 1 - Preparation (batch reads + setup):
  Bash("mkdir -p ai-docs/feature") + Read("src/existing-module.ts")
  + Read("package.json") + Glob("**/*.test.ts")

Message 2 - Planning (depends on Message 1):
  Task(architect) → ai-docs/feature/plan.md

Message 3 - Implementation (3 agents parallel, depends on Message 2):
  Task(backend-developer) → src/feature/api.ts
  Task(frontend-developer) → src/feature/ui.tsx
  Task(test-developer) → tests/feature/

Message 4 - Validation (3 checks parallel, depends on Message 3):
  Bash("npm run test -- tests/feature/")
  Bash("npm run lint -- src/feature/")
  Bash("npm run typecheck")

Message 5 - Review (depends on Message 4):
  Task(code-reviewer)

Total: 5 messages (minimum for dependency chain)
Without batching: 10+ messages  |  Speedup: 2-3x

Troubleshooting

Parallel agents executing sequentially? Mixed tool types in execution message. Use ONLY Task calls.

File reads arriving one at a time? Each Read in separate message. Batch all Reads into one message.

Conflicting edits? Batched Edit calls targeting same file. Sequence same-file edits, batch different-file edits.

Workflow slower than expected despite batching? Audit dependency chain. Ask at each message boundary: "Does this TRULY depend on the previous one?"


Summary

  • The Golden Rule: 1 message = all related operations
  • Same tool type in one message enables true parallel execution
  • Mixed tool types may force sequential execution
  • 3-5x speedup from batching Task launches (biggest impact)
  • Up to 10x speedup from batching file reads and searches
  • The 4-Message Pattern is the canonical batching template
  • Check dependencies before batching (output deps, same-file conflicts)

Master batching and every workflow runs at maximum speed.


Inspired By:

  • claude-flow concurrent execution mandate
  • 4-Message Pattern from multi-agent-coordination skill
  • Production performance analysis of sequential vs parallel workflows
  • Claude Code tool execution model observations

© MadAppGang, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/multimodel/skills/batching-patterns of MadAppGang/claude-code.

Open the folder on GitHubat commit 6097ad4

Compare with similar skills

Batching Patterns next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Batching Patterns

What does Batching Patterns do?

Batch all related operations into single messages for maximum parallelism and performance. Batching Patterns is an agent skill from MadAppGang/claude-code. Batch all related operations into single messages for maximum parallelism and performance.

When should I use Batching Patterns?

Batching Patterns fits situations like: launching multiple agents; reading multiple files; running parallel searches; optimizing workflow speed.

How do I install Batching Patterns in Claude Code?

Run `npx skills add MadAppGang/claude-code --skill batching-patterns -a claude-code`. Or copy the skill folder (plugins/multimodel/skills/batching-patterns in MadAppGang/claude-code) into .claude/skills/batching-patterns in your project. Claude Code loads it when a task matches its description.

How do I install Batching Patterns in Codex?

Run `npx skills add MadAppGang/claude-code --skill batching-patterns -a codex`. Or copy the skill folder (plugins/multimodel/skills/batching-patterns in MadAppGang/claude-code) into .agents/skills/batching-patterns in your project. Codex loads it when a task matches its description.

Can I use Batching Patterns in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add MadAppGang/claude-code --skill batching-patterns -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/batching-patterns, .gemini/skills/batching-patterns, .github/skills/batching-patterns and .opencode/skills/batching-patterns in your project.

What does Batching Patterns need to run?

Going by SKILL.md and its folder, Batching Patterns needs the command-line tools its instructions call (npm). Our summary lists: Node.js.

Does Batching Patterns access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Batching Patterns safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Batching Patterns use?

Batching Patterns is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Batching Patterns use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Batching Patterns?

Skills that share tags, products or a category with Batching Patterns: Messages Ops (affaan-m/ECC, 274k stars), Batch (asgeirtj/system_prompts_leaks, 69k stars), Batch (codewhale-hq/Codewhale, 41k stars) and Commit Message Storyteller (github/awesome-copilot, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Batching Patterns?

MadAppGang (a GitHub organization) maintains it in MadAppGang/claude-code, which has 283 GitHub stars. The repository holds 69 skills in this directory. The repository was last updated on March 15, 2026.

Source: MadAppGang/claude-code on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.