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/spillwavesolutions/agent-brain/ag-check-versionsgit clone --depth 1 https://github.com/SpillwaveSolutions/agent-brainWrote 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/spillwavesolutions/agent-brain/ag-check-versions)<a href="https://agentmods.dev/commands/spillwavesolutions/agent-brain/ag-check-versions"><img src="https://agentmods.dev/badge/commands/spillwavesolutions/agent-brain/ag-check-versions.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 | $0.00000 | $0.00292 |
| Opus 5 | $0.00000 | $0.00146 |
| Sonnet 5 | $0.00000 | $0.00058 |
| Haiku 4.5 | $0.00000 | $0.00029 |
Grade C, and why
ag-check-versions 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 5d 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.
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.
curl -s https://pypi.org/pypi/agent-brain-rag/json | python3 -c "import sys,json; print(json.load(sys.stdin)['info']['version'])" Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s https://pypi.org/pypi/agent-brain-rag/json | python3 -c "import sys,json; print(json.load(sys.stdin)['info']['version'])" What it actually says
ag-check-versions
Check Agent Brain package versions (read-only).
Task
Query and compare versions:
-
PyPI versions (latest published):
curl -s https://pypi.org/pypi/agent-brain-rag/json | python3 -c "import sys,json; print(json.load(sys.stdin)['info']['version'])" curl -s https://pypi.org/pypi/agent-brain-cli/json | python3 -c "import sys,json; print(json.load(sys.stdin)['info']['version'])" -
Local versions:
grep '^version = ' agent-brain-server/pyproject.toml | cut -d'"' -f2 grep '^version = ' agent-brain-cli/pyproject.toml | cut -d'"' -f2 -
CLI dependency type:
grep 'agent-brain-rag' agent-brain-cli/pyproject.toml
Expected Result
Report in table format:
- PyPI server version
- PyPI CLI version
- Local server version
- Local CLI version
- CLI dependency:
pathorPyPI ^X.Y.Z - Alignment status: aligned / misaligned / local ahead
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.
- 5d ago First seen · 40 lines · 0 tokens per session scan C 2d94c0e2731b
ag-check-versions is a command published in the GitHub repository SpillwaveSolutions/agent-brain (117 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 292 tokens. 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 2026-08-30.
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.