This skill is responsible for setting properties, keyframes, easing, and text graphic animation. 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, materials, 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, any required derived outputs, and verification records. Installation success, command directory existence, and creation task completion are reported 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 SKILL_DIR。from the skill directory actually loaded by the host. Creation writes use launch.sh / launch.ps1by default, 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. See the complete operation 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：& "$HARNESS_DIR/scripts/launch.ps1" run --mode workflow --plan PLAN.json --output "C:\Users\me\result".
The native command plan appends --mode commands, and a dedicated desktop session uses --mode desktop. After delivery, you must review and actually view the media; report as acceptable only when both technical verification and creative evaluation pass, with user acceptance status kept separately. When automatic revision is needed, declare revisionScopein the first plan; by default a maximum of 2 rounds, with parent and child tasks sharing 30 minutes. For unknown tasks, only inspect/reconcile; do not redo with a changed task ID.
First Use and Public Entry Point
Locate the real directory of the current SKILL.md. Native artifacts cover macOS arm64/x86_64, Windows x86/x64/arm64, and Linux x86_64/aarch64; no preinstalled Python is required; the fixed CLI installs to the user data directory. When existing task authorization covers the necessary dependencies, run this skill's installer directly without creating a separate approval process.
Set SKILL_DIR to the directory (absolute path) containing this SKILL.md actually loaded by the host. User-level installations may be located in ~/.agents/skills/effectcraft-cli-animation, project-level ones may be located in .agents/skills/effectcraft-cli-animation, and plugins may be located in their skills/effectcraft-cli-animation or host cache directory; rely on the actual load path, do not guess based on the current working directory, and do not search and then arbitrarily select a duplicate version. The skill directory and the CLI's user data installation directory are two separate locations.
: "${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 point is --help; the launcher's own --help only explains launch parameters.
Scene Operations
Verify this scene's inputs, native project, target object, version, and output boundaries. Choose the current command according to the scene 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 parameters; for property reads and writes use props/get/set, for commands use exec <id> --params <JSON> or the paired run <id> <JSON> ...; use --save-as to preserve the new project.
props/get to verify paths and times; update specified keyframes rather than rebuilding the entire layer. Render comparisons at key moments.
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 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, export, dimensions, and media decoding need to be inspected. Timeout results are marked unknown; first check the original task/project, and do not automatically replay edits. The existing native workflow supports save-as revisions; 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 saving, dependency collection, and revisions.
- When installation/diagnostics are needed, hand off to effectcraft-cli-setup; for full task routing hand off 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 fictional login interface; local headless does not require a cloud account. Full GUI, cross-editor fidelity, and creative quality are stated based on actual evidence.
Full native command usage
The query example below requires an existing Python; if no interpreter is prepared, first use the managed launcher doctor for diagnostics. The write example automatically prepares isolated dependencies via the launcher; on Windows use & "$HARNESS_DIR/scripts/launch.ps1" to replace /bin/sh "$HARNESS_DIR/scripts/launch.sh", keeping the same mode and parameters. The current skill includes parameter descriptions for the full directory and same-session entry, not restricted by the creative template allowlist. Read the full usage guide, and query the command referenceas needed; each instruction has a skill route, prerequisite observation, and acceptance status.
python3 -I -B "$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, check the real registry and current executable status, keep 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.
The full workflow command gateway is in usage instructions. For the fixed CLI version and artifact summary, 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 command-by-command acceptance are recorded separately; release does not replace acceptance.
Parent-child layers, property expressions, dynamic previews, and source project rework: use the current skill's expression parent guide。
For GUI tasks, you can first use this skill's built-in fixed desktop installation; installation, startup, 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 via this skill's commands.py describe to see parameters; 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-use diagnostics, locate the setup entry point of the current skill itself based on the receipt; handle installation failures and native call failures separately.
The relevant creation task reads shot tracking scene, verify native parameters, object context, local rework, and delivery boundaries.
Same-target execution protection
Before calling the 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.