This skill is responsible for creating and editing mask paths, feathering, and local mattes. It hands off to skills in the same package by name, can be used when installed alone, and does not read sibling directories. It invokes the fixed official effectcraft-cli and preserves the native editing project.
Inputs and Deliverables
The inputs are the user-confirmed task, assets, project or object, output directory, and scope of modification; when an existing project is needed, verify the summary first. Return the actual CLI results, the saved native project, any required derived outputs, and verification records. Report installation success, command directory existence, and creative task completion separately.
Default Managed Entry Point
Execution implementation is consolidated in the built-in effectcraft-harness skill (installed bundled with the plugin; for standalone installation use npx skills add full-aigc-skills/effectcraft-skills --skill effectcraft-harness). Set HARNESS_DIR to the directory where that skill is actually loaded (absolute path); this skill's SKILL_DIR is only used to read its own examples/ and references/。
Set from the skill directory actually loaded by the host SKILL_DIR。Creative writing defaults to using launch.sh / launch.ps1, automatically preparing a fixed Python and EffectCraft inside the user directory, without changing the global PATH. The raw Python/CLI examples below are retained for compatibility and diagnostic purposes and must not bypass the task recovery ledger. See the complete operating contract in Task Control, Evaluation, and Revision。
/bin/sh "$HARNESS_DIR/scripts/launch.sh" plan --mode workflow --plan PLAN.json --output /absolute/new-result
/bin/sh "$HARNESS_DIR/scripts/launch.sh" run --mode workflow --plan PLAN.json --output /absolute/new-result
Windows PowerShell：& "$HARNESS_DIR/scripts/launch.ps1" run --mode workflow --plan PLAN.json --output "C:\Users\me\result".
The native command plan appends --mode commands, and owned desktop sessions use --mode desktop. After delivery, you must review and actually inspect the media; report as acceptable only if both technical verification and creative evaluation pass, and keep user acceptance status independent. When automatic revision is needed, declare it in the initial plan revisionScope; by default, at most 2 rounds, with parent and child tasks sharing 30 minutes. For unknown tasks, only inspect/reconcile; do not redo with a changed task ID.
First Use and Public Entry Point
Locate the real directory of the current SKILL.md. Native artifacts cover macOS arm64/x86_64, Windows x86/x64/arm64, and Linux x86_64/aarch64; no preinstalled Python is required; the fixed CLI installs into the user data directory. When an existing task authorization already covers the necessary dependencies, run this skill's installer directly and do not create a separate approval process.
Set SKILL_DIR to the directory where this SKILL.md actually loaded by the host is located (absolute path). User-level installations may be located at ~/.agents/skills/effectcraft-cli-masks, project-level may be located at .agents/skills/effectcraft-cli-masks, and plugins may be located in their skills/effectcraft-cli-masks or host cache directory; use the actual loaded path, do not guess based on the current working directory, and do not search and then arbitrarily select a duplicate version. The skill directory and the CLI's user data installation directory are two independent locations.
: "${SKILL_DIR:?请先设置为本 SKILL.md 的实际所在目录}"
python3 -I -B "$HARNESS_DIR/scripts/bootstrap.py"
python3 -I -B "$HARNESS_DIR/scripts/cli.py" -- --version
python3 -I -B "$HARNESS_DIR/scripts/cli.py" -- commands --json
After -- in the CLI argv, the native subcommand must come first. Installation parameters go before the separator;--runtime-home can isolate the cache. Stop when locking the artifact digest fails, the installation is corrupted, or the platform is unsupported; do not change PATH and do not perform floating upgrades. The native help entry point is --help; the launcher's own --help only describes launch parameters.
Scenario Operations
Verify this scenario's inputs, native project, target object, version, and output boundaries. According to Scenario Guide , select the current command, save an independent checkpoint, then execute; after completion, reopen the native project and inspect the actual output and non-target content.
commands --filter <关键词> --json verify parameters; property read/write uses props/get/set, commands use exec <id> --params <JSON> or paired run <id> <JSON> ...; use --save-as to preserve a new project.
Closed paths, composite coordinates, and feather ranges must be verified; do not call untested automatic roto reliable subject recognition.
Native composition and source project revisions use the effectcraft-harness skill's scripts/workflow.py(reused internally by the managed launcher); read workflow contract, the templates are in this skill's examples. Modify only authorized objects, preserve native projects and dependent assets, and read derived-format loss exchange report。
Verification and recovery
A non-zero command exit does not count as completion; you need to inspect the actual saved project, export, dimensions, and media decoding. Mark timeout results as unknown, check the original task/project first, and do not automatically replay edits. Existing native workflows support save-as revision; refuse to overwrite when the corresponding project summary does not match.
On-demand references and handoff
- Actual command evidence: pinned-version observations, used only as routing and parameter references; real-time results take priority, and disabled items are not executed.
- Workflow contract: composition operations, native saving, dependency collection, and revision.
- Hand off to effectcraft-cli-setupwhen installation/diagnostics are needed, and hand off to effectcraft-usefor full task routing; when the skill is missing, use
npx skills add full-aigc-skills/effectcraft-skills --skill <skill-name>. Do not load other skills through adjacent file paths.
This skill does not provide a fictitious login interface; local headless does not require a cloud account. State full GUI, cross-editor fidelity, and creative quality according to actual evidence.
Full native command usage
The query examples below require an existing Python; if no interpreter is prepared, first use the managed launcher doctor for diagnostics. Write examples automatically prepare isolated dependencies through the launcher; on Windows use & "$HARNESS_DIR/scripts/launch.ps1" instead of /bin/sh "$HARNESS_DIR/scripts/launch.sh", keeping the same mode and parameters. The current skill includes a full catalog of parameter descriptions and same-session entry points, and is not restricted by the creative template allowlist. Read full usage guide, query on demand command reference; each instruction has a skill route, prior observation, and acceptance status.
python3 -I -B "$HARNESS_DIR/scripts/commands.py" list --filter QUERY
python3 -I -B "$HARNESS_DIR/scripts/commands.py" describe COMMAND_ID
/bin/sh "$HARNESS_DIR/scripts/launch.sh" run --mode commands --plan "$SKILL_DIR/examples/commands-advanced.json" --output /absolute/new-command-result
/bin/sh "$HARNESS_DIR/scripts/launch.sh" run --mode desktop --plan "$SKILL_DIR/examples/desktop-first-use.json" --output /absolute/new-desktop-result
Before executing a new entry point, check the actual registry and current executable state, and retain return value references and step-by-step receipts; do not pass off semantic errors or timeouts as success. Catalog coverage and direct native use do not mean all instructions, GUI, delivery, or Art orchestration have been accepted.
For the full workflow command gateway, see usage instructions. For the pinned CLI version and artifact digest, refer to the effectcraft-harness skill's scripts/runtime.lock.json ; the skill package version is based on the corresponding release tag. Independent installation re-verification and full per-command acceptance are recorded separately, and release is not a substitute for acceptance.
Parent-child layers, property expressions, dynamic previews, and source project rework: use the current skill's expression parenting guide。
GUI tasks can first use this skill's built-in pinned desktop installation; installation, startup, and actual GUI editing are verified separately.
Full attribution commands are listed by command family in categorized usage list, and each item can be viewed via this skill's commands.py describe parameters; the general CLI list also retains all commands not assigned to specialized skills.
Business tasks start from scenario operations manual , and are executed according to input checks, template adaptation, native delivery, local rework, and result verification.
When installation fails, read First-use diagnostics, locate the current skill's own setup entry point based on the receipt; handle installation failures and native call failures separately.
Related creative tasks read Shot Tracking Scenarios, verify native parameters, object context, local rework, and delivery boundaries.
Same-target execution protection
Before calling a public workflow, read this skill's Execution Registration and Interruption Handling. In case of contention or an unknown state, do not delete the registration, automatically replay, or switch targets to bypass verification.