OpenCode is an open-source AI coding agent that helps developers work on software projects. It includes agents for full-access development, read-only exploration and planning, plus a general subagent for complex searches and multistep tasks; the catalogue add-ons extend its workflows.
Borrowing it
Nothing to install: this file belongs to anomalyco/opencode. 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/anomalyco/opencode/dev/.opencode/command/ai-deps.mdgit clone --depth 1 https://github.com/anomalyco/opencodeWrote 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/anomalyco/opencode/ai-deps)<a href="https://agentmods.dev/commands/anomalyco/opencode/ai-deps"><img src="https://agentmods.dev/badge/commands/anomalyco/opencode/ai-deps.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 | $0.00010 | $0.00226 |
| Opus 5 | $0.00005 | $0.00113 |
| Sonnet 5 | $0.00002 | $0.00045 |
| Haiku 4.5 | $0.00001 | $0.00023 |
Grade A, and why
ai-deps 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 today.
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.
What it actually says
Please read @package.json and @packages/opencode/package.json.
Your job is to look into AI SDK dependencies, figure out if they have versions that can be upgraded (minor or patch versions ONLY no major ignore major changes).
I want a report of every dependency and the version that can be upgraded to. What would be even better is if you can give me brief summary of the changes for each dep and a link to the changelog for each dependency, or at least some reference info so I can see what bugs were fixed or new features were added.
Consider using subagents for each dep to save your context window.
Here is a short list of some deps (please be comprehensive tho):
- "ai"
- "@ai-sdk/openai"
- "@ai-sdk/anthropic"
- "@openrouter/ai-sdk-provider"
- etc, etc
DO NOT upgrade the dependencies yet, just make a list of all dependencies and their versions that can be upgraded to minor or patch versions only.
Write up your findings to ai-sdk-updates.md
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.
- today First seen · 25 lines · 10 tokens per session scan A 31bbd0ff021a
ai-deps is a command published in the GitHub repository anomalyco/opencode (204,765 stars, last pushed today), licensed MIT. It adds 10 tokens to every session and 226 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 2026-09-06.
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.