This skill is responsible for creating, saving, and reopening ecproj projects and organizing project items. It hands off with same-package skills by name and can be used when installed alone, without reading sibling directories. It invokes the fixed official effectcraft-cli and preserves native editing projects.
Inputs and Deliverables
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 creative task completion separately.
Default Managed Entry Point
Execution implementation converges into 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 skill's actual load directory (absolute path); this skill's SKILL_DIR is used only to read its own examples/ and references/。
Set from the skill directory actually loaded by the host SKILL_DIR。Creative writing uses launch.sh / launch.ps1by default, automatically preparing a fixed Python and EffectCraft within the user directory without changing the global PATH. The raw 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：& "$HARNESS_DIR/scripts/launch.ps1" run --mode workflow --plan PLAN.json --output "C:\Users\me\result".
Append to the native command plan --mode commands; a native desktop session uses --mode desktop. After delivery, you must review and actually view the media; report acceptance only when both technical verification and creative evaluation pass, while user acceptance status is kept separately. When automatic revision is needed, declare it in the first plan revisionScope; by default at most 2 rounds and parent/child tasks share 30 minutes. For unknown tasks, only inspect/reconcile; do not redo with a different task ID.
First Use and Public Entry Point
Locate the real directory of the current SKILL.md. Native artifacts cover macOS arm64/x86_64, Windows x86/x64/arm64, and Linux x86_64/aarch64; no preinstalled Python is required; the fixed CLI installs to the user data directory. When an existing task authorization covers the necessary dependencies, run this skill's installer directly without creating a separate approval process.
Set SKILL_DIR to the directory (absolute path) of this SKILL.md actually loaded by the host. A user-level installation may be located at ~/.agents/skills/effectcraft-cli-project, a project-level one may be located at .agents/skills/effectcraft-cli-project, and a plugin may be located in its skills/effectcraft-cli-project or host cache directory; rely on the actual load path, do not guess based on the current working directory, and do not search and arbitrarily choose a duplicate version. The skill directory and the CLI's user data installation directory are two independent locations.
: "${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
In CLI argv, after -- , 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 is --help; the launcher's own --help only explains launch parameters.
Scenario Operations
Verify this scenario's inputs, native project, target object, version, and output boundaries. Follow scenario guide to 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 the parameters; for property read/write use props/get/set, for commands use exec <id> --params <JSON> or paired run <id> <JSON> ...; use --save-as to keep the new project.
Sessions are not shared between CLI invocations; changes must be saved with save-as or saved in the same batch, and native checkpoints must be saved separately.
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, with templates inside this skill's examples. Modify only authorized objects; native projects and dependency assets are preserved; for derived format loss, read the exchange report。
Verification and recovery
A non-zero command exit does not count as completion; the actual saved project, exports, dimensions, and media decoding must be inspected. Timed-out results are marked unknown; first check the original task/project, and editing must not be replayed automatically. Existing native workflows support save-as revisions; overwriting is refused when the corresponding project summary does not match.
On-demand reference and handoff
- Actual command evidence: fixed-version observations, used only as routing and parameter reference; real-time results take precedence, and disabled items are not executed.
- Workflow contract: composition operations, native saving, dependency collection, and revisions.
- Hand off to effectcraft-cli-setupwhen installation/diagnostics are needed, and hand off full task routing to effectcraft-use; when the 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 through the launcher; on Windows, replace & "$HARNESS_DIR/scripts/launch.ps1" with /bin/sh "$HARNESS_DIR/scripts/launch.sh", keeping the same mode and parameters. This skill currently includes complete directory parameter descriptions and same-session entry points, and is not subject to the creative template allowlist. Read the full usage guide, query the command referenceas needed; each instruction has a skill route, prerequisite observation, and acceptance status.
python3 -I -B "$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 real registry and current executable status, and retain return value references and step-by-step receipts; semantic errors or timeouts must not be passed off as success. Directory coverage and direct native use do not mean that all instructions, GUI, delivery, or Art orchestration have been accepted.
For the complete workflow command gateway, see the usage instructions. The fixed CLI's version and artifact digest are governed by 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 command-by-command acceptance are recorded separately; release does not replace 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 fixed desktop installation; installation, launch, and actual GUI editing are verified separately.
Complete attribution commands are listed by command family in the categorized usage list, and each item's parameters can be viewed through this skill's commands.py describe ; the general CLI list also retains all commands not assigned to specialized skills.
Business tasks start from the scenario operation manual , and are executed according to input checks, template adaptation, native delivery, local rework, and result verification.
When installation fails, read First-time use diagnosis. 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 camera tracking scenes, verifying native parameters, object context, local rework, and delivery boundaries.
Same-target execution protection
Before calling public workflows, read this skill's execution registration and interruption handling. In the event of contention or an unknown state, do not delete the registration, automatically replay, or switch targets to bypass verification.