This skill is responsible for querying actual command parameters and capabilities, invoking the public CLI, MCP, and diagnostics. 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
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, required derived outputs, and verification records. Report installation success, command directory existence, and creation task completion separately.
Default Managed Entry Point
Execution implementation converges 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 skill's actual load directory (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 default to using launch.sh / launch.ps1, automatically preparing the 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 must not bypass the task recovery ledger. For the complete operational 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：& "$HARNESS_DIR/scripts/launch.ps1" run --mode workflow --plan PLAN.json --output "C:\Users\me\result".
Native command plan appends --mode commands, own desktop session uses --mode desktop. After delivery, must review and actually view the media; report as acceptable only when both technical verification and creative evaluation pass; user acceptance status is kept independently. When automatic revision is needed, declare it in the first plan revisionScope; default maximum 2 rounds, parent and child tasks share 30 minutes. Unknown tasks are only inspected/reconciled, do not redo with a changed task ID.
First Use and Public Entry Points
Locate the real directory of the current SKILL.md. Native artifacts cover macOS arm64/x86_64, Windows x86/x64/arm64, Linux x86_64/aarch64; no preinstalled Python is required; the fixed CLI is installed to the user data directory. When existing task authorization covers the necessary dependencies, directly execute this skill's installer without creating a separate approval process.
Set SKILL_DIR to the directory containing this SKILL.md actually loaded by the host (absolute path). A user-level installation may be located at ~/.agents/skills/effectcraft-cli-puppet, a project-level one may be located at .agents/skills/effectcraft-cli-puppet, and a plugin may be located in its skills/effectcraft-cli-puppet or host cache directory; use the actual load path as the standard, do not guess based on the current working directory, and do not search and arbitrarily choose among duplicate versions. The skill directory and the CLI's user data installation directory are two independent locations.
: "${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 comes after -- , and the native subcommand must be 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 itself --help only explains startup parameters.
Scene Operations
First read Puppet Scene Guide, and verify layer space coordinates, active composition, pin IDs, mesh, and time units.
First query the current version and command catalog; record read-only and edit invocations separately. After reading the current project state and command enabled/params, construct argv; consecutive steps that need to create objects must be executed in the same session, and the returned ID must not be guessed.
commands --filter <关键词> --json Verify the parameters; use props/get/set for property reads and writes, and for commands use exec <id> --params <JSON> or the paired run <id> <JSON> ...; use --save-as to preserve the new project.
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, and the templates are in this skill's examples. Modify only authorized objects; native projects and dependent assets are preserved, and for derived-format loss read the exchange report。
Verification and recovery
A nonzero command exit does not count as completion; the actually saved project, export, dimensions, and media decoding must be inspected. Timed-out results are marked unknown; check the original task/project first, and do not automatically replay edits. Existing native workflows support save-as revision; when the corresponding project summary does not match, refuse to overwrite.
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 save, dependency collection, and revision.
- Hand off to effectcraft-cli-setupwhen installation/diagnostics are needed, and hand off complete 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.
Complete native command usage
The query examples below require an existing Python; when no interpreter is prepared, first use the managed launcher doctor for diagnostics. The write examples automatically prepare isolated dependencies through the launcher; on Windows use & "$HARNESS_DIR/scripts/launch.ps1" instead of /bin/sh "$HARNESS_DIR/scripts/launch.sh", keeping the same mode and parameters. This skill currently includes parameter descriptions for the complete catalog and same-session entry points, and is not limited by the creative template allowlist. Read the complete usage guide, and query on demand the command reference; each instruction has a skill route, prior observations, and acceptance status.
python3 -I -B "$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 a new entry point, check the real registry and current executable status, and keep return value references and step-by-step receipts; semantic errors or timeouts must not be passed off as success. Catalog 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 usage instructions. For the fixed CLI version and artifact summaries, the effectcraft-harness skill's scripts/runtime.lock.json prevails; the skill package version is determined by the corresponding release tag. Independent installation re-verification and full command-by-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。
For GUI tasks, you can first use this skill's bundled 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 can be viewed through this skill's commands.py describe for parameters; the general CLI list also retains all commands not assigned to specialized skills.
Business tasks start from Scenario Operation Manual Begin, and execute according to input checks, template adaptation, native delivery, local rework, and result verification.
When installation fails, read First-Use Diagnosis, and use the receipt to locate the setup entry point of the current skill itself; handle installation failures and native invocation failures separately.
For related creation tasks, read Camera Tracking Scenario, and verify native parameters, object context, local rework, and delivery boundaries.
Same-Target Execution Protection
Before invoking a public workflow, read this skill's Execution Registration and Interruption Handling. In cases of contention or unknown status, do not delete the registration, automatically replay, or switch targets to bypass verification.