This skill handles previewing keyframes, rendering sequences or videos, and transparent handoff clips. It hands off by name with skills in the same package, 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 changes; when an existing project is required, 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 is consolidated 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 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 uses 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 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：& "$HARNESS_DIR/scripts/launch.ps1" run --mode workflow --plan PLAN.json --output "C:\Users\me\result".
The native command plan appends --mode commands, own desktop sessions use --mode desktop. After delivery you must review and actually view the media; only report acceptance-ready when both technical verification and creative evaluation pass; user acceptance status is kept separately. When automatic revision is needed, declare it in the initial plan revisionScope; by default at most 2 rounds, parent and child tasks share 30 minutes. For unknown tasks only inspect/reconcile, do not redo with a different task ID.
First Use and Public Entry Point
Locate the real directory of the current SKILL.md. Native artifacts cover macOS arm64/x86_64, Windows x86/x64/arm64, Linux x86_64/aarch64; no preinstalled Python is required; the fixed CLI installs into the user data directory. When existing task authorization covers the necessary dependencies, directly execute this skill's installer, and do not create a separate approval process.
Set SKILL_DIR to the directory containing this SKILL.md actually loaded by the host (absolute path). A user-level installation may be located at ~/.agents/skills/effectcraft-cli-export, a project-level one may be located at .agents/skills/effectcraft-cli-export, and a plugin may be located in its skills/effectcraft-cli-export or the host cache directory; rely on the actual load path, do not guess based on the current working directory, and do not search and then arbitrarily choose a duplicate version. The skill directory and the CLI user data installation directory are two independent locations.
: "${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
The CLI argv follows -- , and 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 startup parameters.
Scene Operations
Verify this scene's inputs, native project, target objects, versions, and output boundaries. According to the scene 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; use props/get/set for property reads and writes, and use exec <id> --params <JSON> or paired run <id> <JSON> ...; use --save-as to preserve a new project.
Verify transparency handoff against rgba and format support; MP4 does not replace alpha-preserving assets, save the ecproj and render parameters.
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 within this skill's examples. Modify only authorized objects, preserve native projects and dependent assets, read 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. Timed-out results are marked unknown; check the original task/project first, 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 reference and handoff
- actual command evidence: fixed-version observations, used only as routing and parameter reference; live results take precedence, 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, 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 fictional login interface; local headless does not require a cloud account. Full GUI, cross-editor fidelity and creative quality are stated according to actual evidence.
For the first transparent delivery, first read this skill's scenario operation instructions: in 0.2.0, render-frame is an RGB preview; use render's composition name and RGBA PNG sequence paths to preserve the alpha channel.
For workspace segmented production candidates see segmented rendering. The public png-segmented workflow and bounded Film/Art candidate consumption are already connected; they have not yet entered the current fixed release, HD long sequences and fixed-installation acceptance are open, and incomplete checkpoints must not be used as a substitute for complete asset delivery.
Full native command usage
The query examples below require an existing Python; if no interpreter is prepared, first use the managed launcher doctor to diagnose. Write examples automatically prepare isolated dependencies via the launcher; on Windows use & "$HARNESS_DIR/scripts/launch.ps1" to replace /bin/sh "$HARNESS_DIR/scripts/launch.sh", keeping the same mode and parameters. The current skill includes complete catalogued parameter documentation and same-session entry points, not subject to the creative template allowlist. Read the complete usage guide, query the command referenceas needed; each instruction has a skill route, prior observation and acceptance status.
python3 -I -B "$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 new entry points, check the real registry and current executable status, preserve return value references and step-by-step receipts; semantic errors or timeouts are not passed off as success. Catalog coverage and direct native usage do not mean all instructions, GUI, delivery or Art orchestration have been accepted.
For the full workflow command gateway see usage instructions. For the fixed CLI's version and artifact digest, refer to the effectcraft-harness skill's scripts/runtime.lock.json ; the skill package version refers to the corresponding release tag. Independent installation re-verification and full per-command acceptance are recorded separately; releases are not used as a substitute for 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 classification usage list, and each item can be viewed through this skill's commands.py describe to view 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 in the order of input checking, template adaptation, native delivery, local rework, and result verification.
When installation fails, read the first-use diagnostics, 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 the 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 competition or unknown status, do not delete the registration, automatically replay, or switch targets to bypass verification.