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/thermiteau/maverick/do-initnpx skills add thermiteau/maverick --skill do-initgit clone --depth 1 https://github.com/thermiteau/maverickWrote 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/thermiteau/maverick/do-init)<a href="https://agentmods.dev/skills/thermiteau/maverick/do-init"><img src="https://agentmods.dev/badge/skills/thermiteau/maverick/do-init.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 | $0.00056 | $0.02235 |
| Opus 5 | $0.00028 | $0.01118 |
| Sonnet 5 | $0.00011 | $0.00447 |
| Haiku 4.5 | $0.00006 | $0.00224 |
Grade A, and why
do-init 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Init Maverick Project
Set up the current repository for Maverick — install the CLI if needed, validate the Maverick GitHub App, write the project-level config, scaffold docs and project skills, run the cybersecurity audit, then commit everything and open a pull request for the user to approve.
Execution Context
This skill runs in its own forked context (context: fork) — you are
executing it directly; there is no separate agent to dispatch. Follow the
process below exactly and end with the structured result in the Return
section. Do not modify the user's project source code, perform git
operations beyond what the process prescribes, or provision
infrastructure.
Process
1. Ensure the Maverick CLI is installed and matches the plugin
Run command -v maverick to check whether the CLI is already on PATH.
If maverick is not on PATH, dispatch the /maverick:do-install skill and wait for it to complete. After the install returns, re-run command -v maverick to confirm the binary is now resolvable. If it still is not (e.g. ~/.local/bin is not on the user's PATH), report the install message verbatim to the user and stop — they need to fix PATH and re-invoke /maverick:do-init manually.
If maverick is on PATH, verify the installed CLI matches the loaded plugin. A stale binary from an earlier plugin version can be missing subcommands the rest of this skill depends on (e.g. maverick integration, maverick preflight).
- Read the installed CLI version:
maverick --version - Read the plugin version:
grep -Po '(?<=^version = ")[^"]+' "${CLAUDE_PLUGIN_ROOT}/pyproject.toml" - If they differ, dispatch /maverick:do-install to refresh the CLI. That skill always runs
uv tool install --forcefrom the plugin root, so it overwrites whatever is currently installed. After it returns, re-runmaverick --versionand confirm it now matches the plugin version. If it still does not, surface the mismatch to the user and stop. - If the versions already match, skip to step 2.
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 · 184 lines · 56 tokens per session scan A 346e2a827f2f
do-init is a skill published in the GitHub repository thermiteau/maverick (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 56 tokens to every session and 2,235 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…