This skill is responsible for inspecting, setting, and diagnosing expressions on user-specified properties. It hands off to skills in the same package by name and can be used when installed alone, without reading 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 required, verify the summary first. Return the actual CLI results, the saved native project, any required derived outputs, and the verification record. Report installation success, command directory existence, and creation 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 actual load directory of that skill (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。Authoring writes use by default launch.sh / launch.ps1, automatically preparing the fixed Python and EffectCraft in the user directory, without changing the global PATH. The original 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：& "$HARNESS_DIR/scripts/launch.ps1" run --mode workflow --plan PLAN.json --output "C:\Users\me\result".
Native command plan appends --mode commands, own desktop session uses --mode desktop. After delivery, you must review and actually view the media; only report acceptable when both technical validation and creative evaluation pass, and the user acceptance status is retained independently. When automatic revision is needed, declare it in the first plan revisionScope; by default a maximum of 2 rounds, with parent and child tasks sharing 30 minutes. For unknown tasks only inspect/reconcile, do not redo with a changed task ID.
First use and public entry point
Locate the real directory of the current SKILL.md. Native artifacts cover macOS arm64/x86_64, Windows x86/x64/arm64, and Linux x86_64/aarch64; no preinstalled Python is required; the fixed CLI installs to the user data directory. When the existing task authorization covers the necessary dependencies, execute this skill installer directly without creating a separate approval process.
Set SKILL_DIR to the directory (absolute path) where this SKILL.md actually loaded by the host is located. User-level installations may be located at ~/.agents/skills/effectcraft-cli-expressions, project-level ones may be located at .agents/skills/effectcraft-cli-expressions, and plugins may be located in their skills/effectcraft-cli-expressions or host cache directory; the actual load path prevails, do not guess based on the current working directory, and do not search and then arbitrarily select a duplicate version. The skill directory and the CLI's user data installation directory are two independent locations.
: "${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 is at -- 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 corrupt, or the platform is unsupported; do not change PATH and do not perform floating upgrades. The native help entry point is --help; the launcher itself --help only explains startup 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, and execute; after completion, reopen the native project and inspect the actual outputs 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 preserve the new project.
Expressions use user-confirmed content; do not execute scripts in material metadata, and do not automatically load unknown plugins or external scripts.
Native composition and source project revision use the effectcraft-harness skill's scripts/workflow.py(reused internally by the managed launcher); read the workflow contract, the template is within this skill's examples. Modify only authorized objects, preserve native projects and dependent materials, and for derived format loss read the exchange report。
Verification and recovery
A non-zero command exit does not count as completed; you need to inspect the actually saved project, exports, dimensions, and media decoding. Timed-out results are marked unknown; first check the original task/project, 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 skills are 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 commands use
The query examples below require an existing Python; if no interpreter is prepared, first use the managed launcher doctor for diagnostics. Write examples automatically prepare isolated dependencies via the launcher; on Windows use & "$HARNESS_DIR/scripts/launch.ps1" to replace /bin/sh "$HARNESS_DIR/scripts/launch.sh", keeping the same mode and parameters. The current skill includes the full directory's parameter descriptions and same-session entry points, and is not restricted by the creation template whitelist. Read the full usage guide, and query the command referenceas needed; each instruction has skill routing, prerequisite observation, and acceptance status.
python3 -I -B "$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 actual registry and current executable status, 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 all instructions, GUI, delivery, or Art orchestration have been accepted.
The full workflow command gateway is in the usage instructions. The version and artifact digests of the fixed CLI are subject to the effectcraft-harness skill's scripts/runtime.lock.json ; the skill package version is subject to the corresponding release tag. Independent installation re-verification and full command-by-command acceptance are recorded separately, and release does not replace acceptance.
Parent-child layers, property expressions, dynamic preview, and source project rework: use the current skill's expression parent guide。
For GUI tasks, you may first use this skill's built-in fixed desktop installation; installation, launch, and actual GUI editing are verified separately.
Full attribution commands are listed by command family in the categorized usage list, and each item's parameters can be viewed via this skill's commands.py describe ; the general CLI list also retains all commands not assigned to dedicated skills.
Business tasks start from the scenario operation manual , and are executed by input check, template adaptation, native delivery, local rework, and result verification.
On installation failure, read First-use diagnosis, locate the current skill's own setup entry point based on the receipt; handle installation failures and native invocation failures separately.
Related creation tasks read shot 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 registrations, automatically replay, or switch targets to bypass verification.