| name | maa-project-init |
| description | Scan and initialize a MaaFramework game or app automation project for Maa skills and MaaMCP workflows. Use when asked for maa-project-init, project-pipeline-init, basic_info.md, researching a Maa project, scanning pipeline nodes, finding common Back/Return/Exit/Confirm nodes, mapping node relationships, generating entry flowcharts, summarizing image assets and OCR conventions, or reducing discovery cost for later Maa skill work. |
Maa Project Init
Use this skill to turn a MaaFramework consumer project into a compact onboarding document for future AI sessions. It scans the project's pipeline and image resources, identifies reusable control nodes, and writes basic_info.md at the target project root.
The generated file is a producer/consumer handoff: maa-project-init produces project context, while maa-pipeline-guide, maa-pipeline-generate, maa-pipeline-graph, maa-pipeline-option, and maa-pipeline-testing consume the relevant sections before broad repository discovery.
Do not run this skill against MaaMCP itself unless the user explicitly asks to analyze MaaMCP as a consumer project. MaaMCP is the tool runtime; the normal target is a MaaFramework consumer project containing assets/interface.json or interface.json.
Core Workflow
-
Locate the target project root.
- Prefer a user-provided path.
- Otherwise look for
assets/interface.json, then interface.json.
- Treat
assets/interface.json as the source of resource groups, controller types, task entries, and agent settings.
-
Run the analyzer in summary mode first:
python "<skill-dir>/scripts/analyze_pipeline_project.py" "<project-root>"
-
Inspect the summary for:
- resource groups and task entries from
interface.json
- pipeline file count and unique node count
- high in-degree common nodes
- Back / Return / Exit / Close / Confirm / Wait / Flag nodes
- entry task flowcharts and primary path previews
- unresolved references, isolated nodes, and cycle candidates
- image directory inventory and TemplateMatch usage
- Python
context.run_task() / run_recognition() external entries
- orphan candidates after excluding interface and Python entry points
- agent script paths (declared
child_args resolution + project_root 与 4 层 ancestor 约定入口候选 + 交叉对比)
-
Generate basic_info.md only after the summary looks reasonable:
python "<skill-dir>/scripts/analyze_pipeline_project.py" "<project-root>" --write-basic-info
If basic_info.md already exists, the script refuses to overwrite it. Use --overwrite only after the user explicitly confirms.
-
Report where basic_info.md was written and name the most important sections that still need human review.
-
When handing off to another Maa skill:
- tell it to read
basic_info.md section 0 and the routed sections for its task
- treat the document as a cache, not as source of truth
- re-check every touched node in current JSON/Python and every live claim on the current device
- if relevant source files are newer than
basic_info.md, rerun summary mode and report staleness; do not overwrite silently
What The Analyzer Reads
assets/interface.json or interface.json
- all
assets/resource/**/pipeline/**/*.json
default_pipeline.json under resource roots
- all files under
assets/resource/**/image/**
- static string targets passed to
context.run_task() and context.run_recognition() under agent/**/*.py
@AgentServer.custom_action(...) registrations under agent/**/*.py
interface.json agent.child_args 里每条 .py 的磁盘解析状态(与运行时 maa_mcp/agent_supervisor._build_subprocess_cmd 同步上溯 4 层)
- project_root 与 4 层 ancestor 内
agent/main.py、agent/server.py 等约定入口的候选存在性(AGENT_DIR_NAMES × AGENT_ENTRY_BASENAMES,不递归子目录)
For MaaGumballs-style projects, the script should discover entries such as Start_Up, DailyTask, Reward_Execute, Shop, AutoSky, JJC, Mars, DivineForgeLand_Start, TSD_Entry, AutoCdk, and StopGumballs, then connect them to the pipeline nodes that define them.
Relationship Rules
Parse these pipeline link fields:
Support these node reference forms:
- plain strings:
"ConfirmButton"
- lists:
["A", "B"]
- NodeAttr objects:
{ "name": "A", "jump_back": true }
- prefixed strings:
"[JumpBack]BackText", "[Anchor]SomeNode"
Strip bracket prefixes when resolving the target node, but preserve the prefix in summaries where useful.
Entry Flowcharts
Generate bounded Mermaid flowcharts from each interface.json task entry. These diagrams are meant to orient MaaMCP and future skills quickly, not to replace a full graph database.
- Start from the task
entry node.
- Expand
next, on_error, and interrupt edges with edge labels.
- Preserve branch hints such as
JumpBack, jump_back, and anchor in edge labels.
- Limit depth and edge count so loops and shared utility nodes do not overwhelm
basic_info.md.
- Include a short primary path text summary for agents that cannot render Mermaid.
- When a reachable node uses
action: Custom (including v2 object form), add a
separate Python Agent block. Link the Pipeline node to the block as a
CustomAction call, link the block back as returns, and show the matched
@AgentServer.custom_action(...) handler and source location when available.
Public Node Detection
Treat a node as likely reusable when either condition is true:
- it has high in-degree across the merged graph
- its name or behavior indicates a common UI operation
Important common categories:
- Back / Return / Exit / Close / Logout / Stop
- Confirm / Cancel / Retry
- Wait / Loading / Communicating / PowerLack
- Flag / Check / State probe
ClickKey with Android key 4
- shared TemplateMatch assets such as back buttons, return buttons, confirm buttons, settings buttons
basic_info.md Contents
The generated document must be concise and useful to an AI agent. Include:
- Project overview
- Resource groups and task entries
- Main pipeline inventory
- Common public nodes
- Back / Return / Exit / popup handling
- Node relationship summary
- OCR expected text conventions
- TemplateMatch image inventory
- Resolution and ROI conventions
- Risks and TODOs
- Maa skill handoff routing and live verification status
For section 2, the "Agent script paths" subsection includes three parts:
- Declared: each
child_args .py with Status (resolved / unresolved / non-py / absolute) and the absolute path of resolution (if any).
- Discovered: every
AGENT_DIR_NAMES × AGENT_ENTRY_BASENAMES candidate under root + 4 ancestor levels with an Exists column.
- Cross-check: unresolved declarations, discovered-but-unreferenced candidates, and orphan declarations (resolved paths outside the convention list).
Warnings are emitted when child_args is empty, has no .py entries, has unresolved paths, references a non-convention name, or when a convention candidate exists but is not referenced by child_args. The scanner mirrors maa_mcp/agent_supervisor._build_subprocess_cmd's parent-walk semantics (AGENT_PARENT_WALK_LIMIT = 4); keep the two limits in sync if you change one.
Keep automatically detected facts separate from TODOs. Do not invent game semantics that are not present in the project files.
Optional Live MaaMCP Research
If a device or window is available and the user wants deeper game research, use MaaMCP after file scanning:
- connect to the simulator/window
- take a default
screencap
- infer portrait or landscape from image width/height
- use OCR for visible text and key buttons
- add stable UI facts to
basic_info.md
Live observations must include time, controller/device, screenshot size/orientation, visible page evidence, tested node, score, and whether an action ran. If OCR and screenshot catch different frames during a transition, record them as separate observations and do not promote either one to a stable project fact.
For safe initialization validation, prefer a temporary DoNothing recognition probe derived from a known public node. Run it with start_agent=false when Custom code is unnecessary, inspect recognition.all_results, call stop_pipeline, and remove the temporary file.
This is an enhancement, not a blocker. File scanning must work without a live device.
Do Not
- Do not overwrite an existing non-empty
basic_info.md without explicit confirmation.
- Do not commit generated
basic_info.md from another repository into MaaMCP.
- Do not modify unrelated target-project files while scanning.
- Do not treat OCR/image guesses as facts unless they came from files or live MaaMCP verification.