Open Agents is an open-source reference application for running background coding agents through a web interface, durable workflows, isolated Vercel sandboxes, and GitHub integration. Developers fork and adapt it to let agents make code changes without keeping a local computer running. The catalogue entries provide skills, agents, and instructions for its workflow.
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 skills/vercel-labs/open-agents/plan-modenpx skills add vercel-labs/open-agents --skill plan-modegit clone --depth 1 https://github.com/vercel-labs/open-agentsWrote 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/vercel-labs/open-agents/plan-mode)<a href="https://agentmods.dev/skills/vercel-labs/open-agents/plan-mode"><img src="https://agentmods.dev/badge/skills/vercel-labs/open-agents/plan-mode.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 | $0.00060 | $0.01426 |
| Opus 5 | $0.00030 | $0.00713 |
| Sonnet 5 | $0.00012 | $0.00285 |
| Haiku 4.5 | $0.00006 | $0.00143 |
Grade A, and why
plan-mode 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 4d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
For non-trivial implementation tasks, plan before you code. Treat every change as a system change, not an isolated patch. Your job is not just to find a working implementation; it is to find the smallest implementation that remains coherent with the architecture, lifecycle, and future evolution of the system.
Planning Doctrine
- Think broadly, implement narrowly. Analyze the surrounding system before deciding where the change belongs.
- Every change has system implications. Consider how the request affects data flow, ownership of state, interfaces, lifecycle, failure modes, observability, and future extension.
- Prefer authoritative, derivable designs. If UI state or control flow can be derived from backend events, persisted state, shared protocols, or existing abstractions, prefer that over inventing transient client-only state.
- Choose the smallest coherent solution. Do not default to the smallest local patch if it distorts the architecture or adds hidden follow-on complexity.
- Expand scope only when it simplifies the system. Widen the implementation only when a local fix would create duplicated state, fragile coupling, lifecycle mismatches, or recurring complexity.
- Do not gold-plate. Make bounded architectural improvements that materially improve coherence for the task at hand.
Plan File
Write your plan to a PLAN.md file in the project root. Build this file incrementally as you progress through the steps below -- do not wait until the end to write it all at once. If a plan file already exists, read it first and decide whether the current request is a new task (overwrite) or a continuation (revise).
Step 1: Explore
Thoroughly explore the codebase to understand the request before designing anything.
- Read the relevant files and understand existing patterns, architecture, and conventions.
- Trace the end-to-end flow, not just the local implementation point.
- Identify the current source of truth, state ownership, boundaries between subsystems, and any existing invariants.
- Identify the full lifecycle of the behavior: trigger, processing, intermediate states, completion, side effects, failure, retry, and cleanup.
- Search for similar features and prior art in the codebase.
- Launch parallel explorations when the scope is uncertain or multiple areas of the codebase are involved. Give each exploration a specific, distinct search focus (e.g., one searches for existing implementations of similar features, another explores related components, a third investigates testing patterns). Use a single agent when the task is isolated to known files or the user provided specific file paths.
- Do not start implementing yet.
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.
- 4d ago First seen · 113 lines · 60 tokens per session scan A aa0ac4bab69a
plan-mode is a skill published in the GitHub repository vercel-labs/open-agents (5,802 stars, last pushed 6d ago), licensed MIT. It adds 60 tokens to every session and 1,426 once invoked, about $0.0003 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 2026-08-31.
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