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 and can be used when installed alone; it does not read sibling directories. It invokes the fixed official effectcraft-cli and preserves native editing projects.
Inputs and deliverables
The inputs are the user-confirmed task, assets, 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 verification records. Report installation success, command directory existence, and creative 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 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 defaults 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 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：& "$HARNESS_DIR/scripts/launch.ps1" run --mode workflow --plan PLAN.json --output "C:\Users\me\result".
Native command plan appends --mode commands, and a dedicated desktop session uses --mode desktop. After delivery, you must review and actually view the media; report it as acceptable only when both technical verification and creative evaluation pass. The user's acceptance status is retained independently. When automatic revision is needed, declare it in the initial plan revisionScope; by default, a maximum of 2 rounds, with parent and child tasks sharing 30 minutes. For unknown tasks, only inspect/reconcile; do not redo with a changed task ID.
First-time use and public entry point
Locate the real directory of the current SKILL.md. Native artifacts cover macOS arm64/x86_64, Windows x86/x64/arm64, and Linux x86_64/aarch64; no preinstalled Python is required; the fixed CLI is installed to the user data directory. When an existing task authorization covers the necessary dependencies, execute this skill's installer directly; do not create a separate approval process.
Set SKILL_DIR to the directory containing this SKILL.md actually loaded by the host (absolute path). User-level installations may be located at ~/.agents/skills/effectcraft-cli, project-level installations may be located at .agents/skills/effectcraft-cli, and plugins may be located in their skills/effectcraft-cli or host cache directory; use the actual load path as authoritative, do not guess based on the current working directory, and do not search and arbitrarily choose among duplicate versions. The skill directory and the CLI user data installation directory are two independent locations.
: "${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 after -- ; the native subcommand must be first. Installation parameters go before the separator;--runtime-home can isolate the cache. Stop if 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 startup parameters.
Scene operations
First query the current version and command directory; record read-only and editing calls 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 returned IDs must not be guessed.
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> ...for commands; use --save-as to preserve a new project.
Native composition and source project revisions use the effectcraft-harness skill's scripts/workflow.py(reused internally by the managed launcher); read workflow contract, templates are within this skill's examples. Modify only authorized objects; native projects and dependency assets are preserved; for derived format loss read exchange report。
Verification and recovery
A non-zero command exit does not count as completion; the actual saved project, exports, dimensions, and media decoding need to be inspected. Timed-out results are marked unknown; check the original task/project first, and 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 references; live 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 to effectcraft-usefor full task routing; 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.
Full native command usage
The query examples below require an existing Python; if no interpreter is prepared, first diagnose with the managed launcher doctor . The write examples automatically prepare isolated dependencies via the launcher; on Windows use & "$HARNESS_DIR/scripts/launch.ps1" to replace /bin/sh "$HARNESS_DIR/scripts/launch.sh", keeping the same mode and parameters. The current skill includes a full directory of parameter descriptions and same-session entry points, not limited by the creative template allowlist. Read full usage guide, and query on demand command reference; each instruction has a skill route, prerequisite observations, and acceptance status.
python3 -I -B "$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 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 usage do not mean all instructions, GUI, delivery, or Art orchestration have been accepted.
For the full workflow command gateway see usage instructions. The version and artifact digest of the fixed CLI are governed by the effectcraft-harness skill's scripts/runtime.lock.json ; the skill package version is governed by the corresponding release tag. Independent installation re-verification and full per-command acceptance are recorded separately; release does not 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, first use this skill's bundled fixed desktop installation; installation, launch, 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 via this skill's commands.py describe for parameters; the general CLI list also retains all commands not assigned to specialized skills.
For business tasks, start from scenario operation manual and proceed by input check, template adaptation, native delivery, local rework, and result verification.
If installation fails, read first-use diagnostics, and locate the current skill's own setup entry point based on the receipt; handle installation failures and native invocation failures separately.
For related creative tasks read Shot tracking scene, verify native parameters, object context, local rework, and delivery boundaries.
Read this skill before shot trackingFirst use of tracking and decoding verification: verify source pixels and actual keyframes, and distinguish planned frame counts from completed results.
Execution protection for the same target
Before calling a public workflow, read this skill's execution registration and interruption handling. In case of contention or unknown status, do not delete registrations, automatically replay, or switch targets to bypass verification.