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 skills add KhazixW2/Everything-Maa --skill maa-workflow-buildgit clone --depth 1 https://github.com/KhazixW2/Everything-MaaWrote 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/khazixw2/everything-maa/maa-workflow-build)<a href="https://agentmods.dev/skills/khazixw2/everything-maa/maa-workflow-build"><img src="https://agentmods.dev/badge/skills/khazixw2/everything-maa/maa-workflow-build.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00103 | $0.02345 |
| Opus 5 | $0.00051 | $0.01172 |
| Sonnet 5 | $0.00021 | $0.00469 |
| Haiku 4.5 | $0.00010 | $0.00234 |
Grade A, and why
maa-workflow-build 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 8d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Maa Workflow Build
Own an end-to-end Maa automation request from ambiguous intent through evidence-backed completion. Treat the other Maa skills as specialist capabilities; keep this skill responsible for goal compilation, phase state, routing, recovery, and acceptance.
Operating contract
- Do not jump from a vague request directly to Pipeline nodes.
- Do not plan or implement against a scene that has not been observed. When a start or success state rests on a guess, explore the real UI first and write nodes from what was observed.
- Maintain one task contract and one current run state throughout the task.
- Separate observed facts, user decisions, working assumptions, and unresolved questions.
- Ask only about choices that materially change behavior, safety, or acceptance. Make reversible, low-risk assumptions explicit and continue.
- Define verification before implementation. Do not declare completion because files were written or a single smoke test passed.
Read references/task-contract.md before finalizing the goal. Read references/run-state.md before the first action and at every phase transition. Read references/exploration-first.md whenever a start or success state is not backed by a screenshot or recognition result captured in this task.
Specialist roles
- Keep this orchestrator responsible for the task contract, state-machine design, failure routing, and final acceptance. The orchestrator owns state-machine assembly and integration; specialist outputs do not become a finished workflow until they are connected and verified against the contract.
- Treat
$maa-pipeline-guideas a reference and constraint source, not a sequential execution phase or node producer. Load only the sections needed to design, edit, or review the current control flow. - Treat
$maa-pipeline-generateas the primary producer for recognition and action nodes, especially OCR, TemplateMatch, ColorMatch, ROI selection, and screenshot-derived snippets. Integrate its output into the designed state machine instead of treating generated nodes as task completion. - Invoke
$maa-pipeline-optiononly when the task contract requires a user-facing toggle, selector, checkbox, switch, or input. Do not create options merely because the skill is available. - Run
$maa-pipeline-testingafter each coherent implementation increment and again against the integrated end-to-end flow. Use its evidence to route a failure back to the specialist or orchestrator phase that owns the defect. - Treat
$maa-cli-operateas an execution backend for repeatable checks or guarded runtime operations, not a mandatory business phase. - Invoke
$maa-diagnoseonly fromRECOVER, and only when a failure exists whose owner is still unknown after focused testing. It is a read-only evidence producer, never a routine phase, and never a repair actor. - Treat
$maa-wikias an official-knowledge reference provider. Use it when the task contract, design, or acceptance criteria depend on MaaFramework documentation, schema, API, binding, release, or semantic-change facts; navigate to original sources before treating those facts as authoritative.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 137 lines · 103 tokens per session scan A 18cdee0f968d
maa-workflow-build is a skill published in the GitHub repository KhazixW2/Everything-Maa (12 stars, last pushed today), licensed MIT. It adds 103 tokens to every session and 2,345 once invoked, about $0.0005 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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