This skill is responsible for organizing text, shapes, asset layers, and hierarchical compositing. It hands off to skills in the same package by name, can be used when installed on its own, 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 modification scope; when an existing project is needed, verify the summary first. Return the actual CLI results, the saved native project, the required derived outputs, and verification records. Report installation success, command directory existence, and creation 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。Creation writes use by default launch.sh / launch.ps1, automatically preparing the fixed Python and EffectCraft within the user directory, without changing the global PATH. The original Python/CLI examples below are retained for compatibility and diagnostic purposes and cannot bypass the task recovery ledger. For the complete operation contract see 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：& "$HARNESS_DIR/scripts/launch.ps1" run --mode workflow --plan PLAN.json --output "C:\Users\me\result".
The native command plan appends --mode commands, and a native desktop session uses --mode desktop. After delivery you must review and actually view the media; only report acceptance-ready if both technical verification and creation evaluation pass, and the user acceptance state is retained independently. When automatic revision is needed, declare it in the first plan revisionScope; by default at most 2 rounds, with parent and child tasks sharing 30 minutes. For unknown tasks only inspect/reconcile, and do not redo with a different task ID.
First use and public entry point
Locate the real directory of the current SKILL.md. Native artifacts cover macOS arm64/x86_64, Windows x86/x64/arm64, and Linux x86_64/aarch64; no preinstalled Python is required; the fixed CLI is installed into the user data directory. When an existing task authorization already covers the necessary dependencies, run this skill's installer directly and do not create another approval process.
Set SKILL_DIR to the directory (absolute path) containing this SKILL.md actually loaded by the host. User-level installations may be located at ~/.agents/skills/effectcraft-cli-layers, project-level ones may be located at .agents/skills/effectcraft-cli-layers, and plugins may be located in their skills/effectcraft-cli-layers or host cache directory; go by the actual loaded path, do not guess based on the current working directory, and do not search and then arbitrarily choose a duplicate version. The skill directory and the CLI's user data installation directory are two independent locations.
: "${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
CLI argv after -- , the native subcommand must come first. Installation arguments 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 explains startup arguments.
Scenario operations
Verify this scenario's inputs, native project, target object, version, and output boundaries. Select the current command according to the scenario guide , save an independent checkpoint, then execute; after completion, reopen the native project and check the actual output and non-target content.
commands --filter <关键词> --json verify the arguments; for property reads and writes use props/get/set, and for commands use exec <id> --params <JSON> or paired run <id> <JSON> ...; use --save-as to preserve the new project.
Use real layer/comp IDs; text changes keep other animations, record the fonts used and substitutions.
Native composition and source project revisions use the effectcraft-harness skill's scripts/workflow.py(reused internally by the managed launcher); read the workflow contract, templates are in this skill's examples. Modify only authorized objects; native projects and dependent assets are preserved; derived-format loss is read from the exchange report。
Verification and recovery
A non-zero command exit does not count as completion; you need to inspect the actually saved project, export, dimensions and media decoding. Mark timeout results as unknown; check the original task/project first, and do not automatically replay edits. The existing native workflow supports save-as revisions; 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; live 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 full task routing to effectcraft-use; when a skill is missing use
npx skills add full-aigc-skills/effectcraft-skills --skill <skill-name>. Do not load other skills via adjacent file paths.
This skill does not provide a fictitious login interface; local headless does not require a cloud account. Full GUI, cross-editor fidelity and creative quality are stated according to actual evidence.
Full native command usage
The query examples below require an existing Python; if no interpreter is prepared, first diagnose with the managed launcher doctor . The write examples automatically prepare isolated dependencies via the launcher; on Windows use & "$HARNESS_DIR/scripts/launch.ps1" in place of /bin/sh "$HARNESS_DIR/scripts/launch.sh", keeping the same mode and parameters. This skill currently includes parameter descriptions for the full catalog and same-session entry points, not limited by the creative template allowlist. Read the full usage guideand query the command referenceon demand; each instruction has skill routing, prerequisite observations and acceptance status.
python3 -I -B "$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 new entry points, check the actual registry and current executable status, and keep return-value references and step-by-step receipts; semantic errors or timeouts are not passed off as success. Catalog coverage and direct native use do not equal acceptance of all instructions, GUI, delivery or Art orchestration.
For the full workflow command gateway see usage instructions. For the pinned CLI's version and artifact digest, refer to the effectcraft-harness skill's scripts/runtime.lock.json ; the skill package version is governed by the corresponding release tag. Independent installation re-verification and full per-command acceptance are recorded separately; release is not a substitute for acceptance.
Parent-child layers, property expressions, dynamic previews and source project rework: use this skill's expression parenting guide。
For GUI tasks you can first use this skill's built-in pinned desktop installation; verify installation, launch and actual GUI editing separately.
Full attribution commands are listed by command family in the categorized usage list; each item's parameters can be viewed via this skill's commands.py describe ; the general CLI list also retains all commands not assigned to specialized skills.
For business tasks start from the scenario operation manual and proceed through input checks, template adaptation, native delivery, local rework and result verification.
On installation failure read the first-use diagnostics, locate the setup entry point of the current skill itself based on the receipt; handle installation failures and native invocation failures separately.
Related creative tasks read shot tracking scenarios, and verify native parameters, object context, local rework, and delivery boundaries.
Same-target execution protection
Before invoking the public workflow, read this skill's execution registration and interruption handling. In cases of contention or unknown status, you must not delete the registration, automatically replay, or switch targets to bypass verification.