This skill is responsible for querying and applying the effects supported by the current version, and adjusting effect order and parameters. It hands off to sibling skills 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, assets, project or object, output directory, and modification scope; when an existing project is needed, verify the summary first. It returns the actual CLI results, the saved native project, any required derived outputs, and verification records. Successful installation, existence of the command directory, and completion of the creative task are reported separately.
Default Managed Entry Point
Execution implementation is consolidated within 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 SKILL_DIR。from the skill directory actually loaded by the host. Creative writing defaults to using launch.sh / launch.ps1, automatically preparing the fixed Python and EffectCraft within the user directory, without modifying the global PATH. The raw Python/CLI examples below are retained for compatibility and diagnostic purposes and must not bypass the task recovery ledger. The complete operational contract is 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：& "$HARNESS_DIR/scripts/launch.ps1" run --mode workflow --plan PLAN.json --output "C:\Users\me\result".
The native command plan appends --mode commands, and own desktop sessions use --mode desktop. After delivery, you must review and actually view the media; report as acceptable only when both technical verification and creative evaluation pass, and the user's acceptance status is kept separately. When automatic revision is needed, declare revisionScopein the initial plan; by default a maximum of 2 rounds, with parent and child tasks sharing 30 minutes. For unknown tasks, only inspect/reconcile; do not redo with a different task ID.
First Use and Public Entry Point
Locate the real directory of the current SKILL.md. Native artifacts cover macOS arm64/x86_64, Windows x86/x64/arm64, and Linux x86_64/aarch64; no pre-installed Python is required; the fixed CLI is installed into the user data directory. When existing task authorization covers the necessary dependencies, execute this skill's installer directly without creating a separate approval process.
Set SKILL_DIR to the directory (absolute path) of this SKILL.md that is actually loaded by the host. User-level installations may be located at ~/.agents/skills/effectcraft-cli-effects, project-level ones may be located at .agents/skills/effectcraft-cli-effects, and plugins may be located in their skills/effectcraft-cli-effects or the host cache directory; rely on the actual loaded path, do not guess based on the current working directory, and do not randomly select a duplicate version after searching. The skill directory and the CLI's user data installation directory are two independent locations.
: "${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
After -- in the CLI argv, 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 modify PATH and do not perform floating upgrades. The native help entry point is --help; the launcher's own --help only explains startup parameters.
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, and 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 reading and writing 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.
The current effects directory being visible does not mean all effects have passed real rendering; effects with disabled or parameterless contracts are stopped and gaps are recorded.
Native composition and source project revision use the effectcraft-harness skill's scripts/workflow.py(reused internally by the managed launcher); read workflow contract, templates are inside this skill's examples. Only modify authorized objects, native projects and dependent assets are preserved, derived format loss read 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. Timeout results are marked unknown; check the original task/project first, and do not automatically replay edits. Existing native workflows support save-as revision; 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; real-time results take precedence, 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, 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 through 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; when no interpreter is prepared, first diagnose with the managed launcher doctor . Write examples automatically prepare isolated dependencies through the launcher; on Windows use & "$HARNESS_DIR/scripts/launch.ps1" to replace /bin/sh "$HARNESS_DIR/scripts/launch.sh", keeping the same mode and parameters. The current skill includes the full directory's parameter descriptions and same-session entry points, and is not restricted by the creative template whitelist. Read full usage guide, query on demand command reference; each instruction has a skill route, prior observation, and acceptance status.
python3 -I -B "$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 a new entry point, check the real registry and current executable state, preserve 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 equal acceptance of all instructions, GUI, delivery, or Art orchestration.
For the full workflow command gateway, see usage instructions. The fixed CLI's version and artifact digest are based on 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 per-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 parenting guide。
GUI tasks can first use this skill's built-in fixed desktop installation; installation, startup, and actual GUI editing are verified separately.
Full attribution commands are listed by command family in categorized usage list, and each item can be viewed through this skill's commands.py describe parameters; the general CLI list also retains all commands not assigned to specialized skills.
Business tasks start from scenario operations manual , executed according to input checks, template adaptation, native delivery, local rework, and result verification.
On installation failure, read 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 creative tasks, read Camera Tracking Scenarios, 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.