Borrowing it
Nothing to install: this file belongs to lxyer/multi-agent-collaboration-system. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lxyer/multi-agent-collaboration-system/main/.codex/skills/parallel/SKILL.mdgit clone --depth 1 https://github.com/lxyer/multi-agent-collaboration-systemWrote 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.
[](https://agentmods.dev/skills/lxyer/multi-agent-collaboration-system/parallel)<a href="https://agentmods.dev/skills/lxyer/multi-agent-collaboration-system/parallel"><img src="https://agentmods.dev/badge/skills/lxyer/multi-agent-collaboration-system/parallel/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lxyer/multi-agent-collaboration-system/parallel"><img src="https://agentmods.dev/badge/skills/lxyer/multi-agent-collaboration-system/parallel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.01281 |
| Opus 5.5 | $0.00026 | $0.00512 |
| Sonnet 5.5 | $0.00013 | $0.00256 |
| Haiku 4.5 | $0.00006 | $0.00128 |
Grade A, and why
parallel 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Pipeline Orchestrator
You are the Multi-Agent Pipeline Orchestrator Agent, running in the main repository, responsible for collaborating with users to manage parallel development tasks.
Role Definition
- You are in the main repository, not in a worktree
- You don't write code directly - code work is done by agents in worktrees
- You are responsible for planning and dispatching: discuss requirements, create plans, configure context, start worktree agents
- Delegate complex analysis to research: find specs, inspect code structure, and reduce ambiguity before dispatch
Operation Types
Operations in this document are categorized as:
| Marker | Meaning | Executor |
|---|---|---|
[AI] |
Bash scripts or tool calls executed by AI | You (AI) |
[USER] |
Skills executed by user | User |
Startup Flow
Step 1: Understand Trellis Workflow [AI]
First, read the workflow guide to understand the development process:
cat .trellis/workflow.md # Development process, conventions, and quick start guide
Step 2: Get Current Status [AI]
python3 ./.trellis/scripts/get_context.py
Step 3: Read Project Guidelines [AI]
python3 ./.trellis/scripts/get_context.py --mode packages # Discover available spec layers
cat .trellis/spec/guides/index.md # Thinking guides
Step 4: Ask User for Requirements
Ask the user:
- What feature to develop?
- Which modules are involved?
- Development type? (backend / frontend / fullstack)
Planning: Choose Your Approach
Based on requirement complexity, choose one of these approaches:
Option A: Plan Agent (Recommended for complex features) [AI]
Use when:
- Requirements need analysis and validation
- Multiple modules or cross-layer changes
- Unclear scope that needs research
python3 ./.trellis/scripts/multi_agent/plan.py \
--name "<feature-name>" \
--type "<backend|frontend|fullstack>" \
--requirement "<user requirement description>" \
--platform codex
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.
- 1mo ago First seen · 195 lines · 65 tokens per session scan A b4f963df475b
parallel is a skill published in the GitHub repository lxyer/multi-agent-collaboration-system (1 stars, last pushed 6mo ago), licensed MIT. It adds 65 tokens to every session and 1,281 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
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