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/continuedev/continue/cn-checknpx skills add continuedev/continue --skill cn-checkgit clone --depth 1 https://github.com/continuedev/continueWhat 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 | $0.00049 | $0.01329 |
| Opus 5 | $0.00024 | $0.00665 |
| Sonnet 5 | $0.00010 | $0.00266 |
| Haiku 4.5 | $0.00005 | $0.00133 |
Grade A, and why
cn-check 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 yesterday.
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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
cn check — Local AI Agent Checks
Run AI-powered code checks locally against your working tree changes using the Continue CLI. Each check is an agent (defined in markdown) that reviews your diff, identifies issues, and optionally suggests fixes as a patch.
When to Use
- User asks to run AI checks on their code changes
- User wants to set up
cn checkin a project - User needs to create custom check agents
- User wants to apply AI-suggested fixes locally
- User asks about Continue CI or agent-based code review
Installation
Prerequisites
- Node.js 18+
- A git repository with uncommitted or branched changes
Install the CLI
npm install -g @continuedev/cli
Authenticate (required for Hub checks, optional for local-only)
cn login
This opens a browser for authentication. After login, Hub-configured checks are available automatically.
Usage
Basic: Run all discovered checks
cn check
This auto-detects checks from three sources (in priority order):
- Hub API — checks configured for your repo on continue.dev
- Local agents — markdown files in
.continue/agents/*.md
Specify agents explicitly
# Run a single local agent
cn check --agent .continue/agents/security-review.md
# Run a Hub-published agent
cn check --agent myorg/code-style
# Run multiple agents
cn check --agent .continue/agents/security.md --agent .continue/agents/docs.md
Compare against a specific base branch
cn check --base develop
Default: auto-detects main or master.
Output formats
# JSON output (for CI pipelines or scripting)
cn check --format json
# Unified patch output (pipe to git apply)
cn check --patch | git apply
# Stop on first failure
cn check --fail-fast
Auto-fix mode
cn check --fix
Runs all checks, then applies any suggested patches directly to the working tree. Patches that conflict are reported but skipped.
Creating a Check Agent
Create a markdown file at .continue/agents/<name>.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.
- yesterday First seen · 201 lines · 49 tokens per session scan A 63d7241f9cc0
cn-check is a skill published in the GitHub repository continuedev/continue (35,702 stars, last pushed 2d ago), licensed Apache-2.0. It adds 49 tokens to every session and 1,329 once invoked, about $0.0002 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 2026-08-30.
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