Borrowing it
Nothing to install: this file belongs to lie5860/openai-search-mcp. 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/lie5860/openai-search-mcp/main/.claude/commands/trellis/parallel.mdgit clone --depth 1 https://github.com/lie5860/openai-search-mcpWrote 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/commands/lie5860/openai-search-mcp/parallel)<a href="https://agentmods.dev/commands/lie5860/openai-search-mcp/parallel"><img src="https://agentmods.dev/badge/commands/lie5860/openai-search-mcp/parallel.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01220 |
| Opus 5 | $0.00000 | $0.00610 |
| Sonnet 5 | $0.00000 | $0.00244 |
| Haiku 4.5 | $0.00000 | $0.00122 |
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 6d 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.
This is a copy
100% identical to parallel — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 194 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 agent: finding specs, analyzing code structure
Operation Types
Operations in this document are categorized as:
| Marker | Meaning | Executor |
|---|---|---|
[AI] |
Bash scripts or Task calls executed by AI | You (AI) |
[USER] |
Slash commands 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]
cat .trellis/spec/frontend/index.md # Frontend guidelines index
cat .trellis/spec/backend/index.md # Backend guidelines index
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>"
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.
- 6d ago First seen · 194 lines · 0 tokens per session scan A f4c81fe1a468
parallel is a command published in the GitHub repository lie5860/openai-search-mcp (13 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,220 tokens. A static security scan graded it A with 0 findings. It is 100% identical to parallel, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.