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.
npx agentmods add commands/mistydew/tokenicode-deepseek-alpha/parallelgit clone --depth 1 https://github.com/mistydew/tokenicode-deepseek-alphaWhat 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 | $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 2d 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 — 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.
- 2d ago First seen · 194 lines · 0 tokens per session scan A f4c81fe1a468
parallel is a command published in the GitHub repository mistydew/tokenicode-deepseek-alpha (367 stars, last pushed 28d ago), licensed Apache-2.0. 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
multi-repo-swarm
Coordinate AI swarms across multiple repositories, enabling organization-wide automation and intelligent cross-project collaboration.
swarm-issue
Transform GitHub Issues into intelligent swarm tasks, enabling automatic task decomposition and agent coordination.
launch-worker
Manually launch headless workers against one or more GitHub issues.
polyphony-spawn
Create a new task in the Polyphony orchestrator and route it to an agent.
build-department
Use when an operator wants to create or refine a department, its head agent, intake posture, linked tools and workflows, and its role in the AI shadow department model.
swarms
Lists all active swarms working on the project, showing what each is working on and coordination opportunities.