delegation-patterns

delegation-patterns is a skill for Claude Code, Codex from baphuongna/pi-crew. It costs 11 tokens per session (982 once invoked), scanned A, original, MIT.

A guide to delegating coding and investigation work among AI subagents or team members. It covers foreground, background, parallel, chained, and isolated work arrangements.

In plain words
What is it for?
Use it to decide when to delegate, prepare focused task instructions, choose an execution pattern, isolate risky changes, and pass results between dependent tasks.
Why use it?
It helps split work safely when tasks involve several parts or need independent review. It also warns against parallel edits that could conflict in the same files or state.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to decide when to delegate, prepare focused task instructions, choose an execution pattern, isolate risky changes, and pass results between dependent tasks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/baphuongna/pi-crew/delegation-patterns
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add baphuongna/pi-crew --skill delegation-patterns
Clone the repo
git clone --depth 1 https://github.com/baphuongna/pi-crew

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for delegation-patterns

README.md
[![agentmods](https://agentmods.dev/badge/skills/baphuongna/pi-crew/delegation-patterns.svg)](https://agentmods.dev/skills/baphuongna/pi-crew/delegation-patterns)
Your own site
<a href="https://agentmods.dev/skills/baphuongna/pi-crew/delegation-patterns"><img src="https://agentmods.dev/badge/skills/baphuongna/pi-crew/delegation-patterns.svg" alt="Measured on agentmods" height="20"></a>
Per session 11 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 982 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium MCP Rug Pull · line 117
    npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
    Fix: Pin the version: npx @scope/[email protected]
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00011 $0.00982
Opus 5 $0.00005 $0.00491
Sonnet 5 $0.00002 $0.00196
Haiku 4.5 $0.00001 $0.00098

Measured 8d ago against content hash 815ed0377697, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

delegation-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/delegation-patterns/SKILL.md · 121 lines

How it starts

The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.

delegation-patterns

Use this skill when deciding how to delegate work.

Source patterns distilled

  • pi-subagents: foreground/background/parallel/chain execution, fork/fresh context, worktree isolation, result watcher
  • pi-crew: src/extension/team-tool/run.ts, src/runtime/team-runner.ts, src/runtime/task-graph-scheduler.ts, builtin teams/*.team.md, workflows/*.workflow.md
  • Existing pi-crew skill: task-packet

Rules

  • Delegate when tasks span multiple files/subsystems, need planning/review/verification, or can be independently researched.
  • Do not parallelize edits to the same file, symbol, migration path, manifest/lockfile, or generated schema unless explicitly sequenced.
  • Use read-only explorer/reviewer roles for source audit; implementation workers should receive narrow task packets.
  • For async/background work, provide concrete objective, scope, constraints, outputs, and verification. Do not spin in wait loops; retrieve results when notified or when needed.
  • For chain-style work, pass dependency outputs forward explicitly and require downstream workers to read upstream artifacts first.
  • Use worktree isolation for risky parallel code-changing tasks when repository cleanliness and merge plan allow it.
  • Require workers to report blockers and smallest recoverable next action rather than making broad assumptions.

Escalation Matrix (from SOC operations)

Define severity tiers and escalation paths for team tasks:

escalation:
  tiers:
    - level: P1
      name: Critical
      sla_response: 15m
      sla_resolution: 1h
      owner: lead
      notify: [manager, stakeholders]
      criteria: [data_loss, security_breach, complete_outage, customer_facing]
    - level: P2
      name: High
      sla_response: 1h
      sla_resolution: 4h
      owner: senior_dev
      notify: [lead]
      criteria: [partial_outage, significant_bug, regression]
    - level: P3
      name: Medium
      sla_response: 4h
      sla_resolution: 24h
      owner: mid_dev
      notify: [team]
      criteria: [minor_bug, feature_break, ux_issue]
    - level: P4
      name: Low
      sla_response: 24h
      sla_resolution: 1w
      owner: junior_dev
      notify: []
      criteria: [enhancement, low_priority, tech_debt]
  escalation_path: [P4 → P3 → P2 → P1]
  override_conditions: [security, data_loss, customer_facing]

Read the full file on GitHub · 121 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 121 lines · 11 tokens per session scan A 815ed0377697

Subscribe to this mod's changes

delegation-patterns is a skill published in the GitHub repository baphuongna/pi-crew (51 stars, last pushed 4d ago), licensed MIT. It adds 11 tokens to every session and 982 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens