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/togo-framework/cabrain-cli/doctorgit clone --depth 1 https://github.com/togo-framework/cabrain-cliWhat 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 | $0.00019 | $0.00301 |
| Opus 5 | $0.00010 | $0.00151 |
| Sonnet 5 | $0.00004 | $0.00060 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade C, and why
doctor scanned grade C with 2 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
1. **CLI present** — run `command -v cabrain` and `cabrain version`. If missing, tell the user to install it: `npm i -g cabrain-cli` (or `curl -fsSL https://cabrain.fadymondy.com/install.sh | sh`). The plugin's MCP serve Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. **CLI present** — run `command -v cabrain` and `cabrain version`. If missing, tell the user to install it: `npm i -g cabrain-cli` (or `curl -fsSL https://cabrain.fadymondy.com/install.sh | sh`). The plugin's MCP serve What it actually says
Diagnose the CaBrain plugin setup and fix what's broken. Check, in order:
- CLI present — run
command -v cabrainandcabrain version. If missing, tell the user to install it:npm i -g cabrain-cli(orcurl -fsSL https://cabrain.fadymondy.com/install.sh | sh). The plugin's MCP server is thecabrainbinary, so it must be on PATH. - Up to date — compare
cabrain versionagainst the latest; if behind, suggestcurl -fsSL https://cabrain.fadymondy.com/upgrade.sh | sh. - Authenticated — run
cabrain auth whoami. If no token/endpoint, the plugin'suserConfig(api_token) may be unset — tell them to reconfigure the plugin or runcabrain auth login --token <cbt_…>. - MCP live — call the brain_list tool. If it returns brains, the end-to-end path (client → cabrain mcp → API) works. If it fails with
unauthorized, the token is missing or wrong.
Report each check as ✓/✗ with the exact fix command for any failure.
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 · 13 lines · 19 tokens per session scan C 55a14f904dfe
doctor is a command published in the GitHub repository togo-framework/cabrain-cli (0 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 301 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.