This skill handles shot tracking along with its native delivery and local rework. It hands off to skills in the same package by name and can be installed standalone; it does not read sibling directories. It invokes the pinned official effectcraft-cli and preserves the native editing project.
Inputs and delivery
The inputs are the user-confirmed task, media, 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 is implemented 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 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。Creative writing defaults to using launch.sh / launch.ps1, automatically preparing the pinned Python and EffectCraft within the user directory without modifying 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 full 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：& "$HARNESS_DIR/scripts/launch.ps1" run --mode workflow --plan PLAN.json --output "C:\Users\me\result".\nThe native command plan appends --mode commands, and own desktop sessions use --mode desktop. After delivery you must review and actually view the media; only report acceptance-ready when both technical validation and creative evaluation pass, and the user acceptance state is kept independent. When automatic revision is needed, declare revisionScopein the first plan; by default at most 2 rounds, with parent and child tasks sharing 30 minutes. For unknown tasks, only inspect/reconcile; do not redo with a changed task ID.
First use and public entry point
Locate the real directory of the current SKILL.md. Native artifacts cover macOS arm64/x86_64, Windows x86/x64/arm64, and Linux x86_64/aarch64; no preinstalled Python is required; the pinned CLI is installed to the user data directory. When existing task authorization covers the necessary dependencies, run this skill's installer directly without creating a separate approval process.
Set SKILL_DIR to the directory containing this SKILL.md actually loaded by the host (absolute path). User-level installations may be located at ~/.agents/skills/effectcraft-cli-tracking, project-level ones may be located at .agents/skills/effectcraft-cli-tracking, and plugins may be located in their skills/effectcraft-cli-tracking or the host cache directory; use the actual load path as the standard, do not guess based on the current working directory, and do not search and arbitrarily choose among duplicate versions. The skill directory and the CLI's user data installation directory are two separate locations.
: "${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
CLI argv in -- after that, 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 describes launch parameters.
Scene operations
According to shot tracking scene , verify the real object, parameters, and task boundaries.
First query the current version and command directory; record read-only and editing calls separately. Read the current project state and command enabled/params before constructing 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; 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.
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 in this skill's examples. Modify only authorized objects; preserve the native project and dependency assets; for derived-format loss, read exchange report。
Verification and recovery
A non-zero command exit does not count as completion; you need to inspect the actual saved project, export, dimensions, and media decoding. Timeout results are marked unknown; check the original task/project first, and do not automatically replay edits. The existing native workflow supports save-as revision; refuse to overwrite when the corresponding project summary does not match.
On-demand reference and handoff
- actual command evidence: pinned-version observations, used only as routing and parameter reference; live results take precedence, and disabled items are not executed.
- workflow contract: composition operations, native saving, dependency collection, and revision.
- When installation/diagnostics are needed, hand off to effectcraft-cli-setup; for complete 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 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 example below requires Python to already be available; if no interpreter is prepared, first use the managed launcher doctor to diagnose. The write example automatically prepares isolated dependencies through the launcher; on Windows use & "$HARNESS_DIR/scripts/launch.ps1" to replace /bin/sh "$HARNESS_DIR/scripts/launch.sh", keeping the same mode and parameters. The current skill includes full catalog parameter descriptions and same-session entry points, and is not limited by the creative template allowlist. Read full usage guide, and query on demand command reference; each instruction has skill routing, prerequisite observations, and acceptance status.
python3 -I -B "$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, and retain return value references and step-by-step receipts; semantic errors or timeouts must not be passed off as success. Catalog coverage and direct native use do not mean all instructions, GUI, delivery, or Art orchestration have been accepted.
For the full workflow command gateway, see usage instructions. For the pinned CLI's version and artifact digest, defer 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 per-command acceptance are recorded separately, and 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 pinned desktop installation; installation, launch, and actual GUI editing are verified separately.
Full ownership commands are listed by command family in categorized usage list; each item can be viewed through this skill's commands.py describe for parameters; the general CLI list also retains all commands not assigned to specialized skills.
Business tasks start from scenario operation manual , and are executed by input checking, template adaptation, native delivery, local rework, and result verification.
If installation fails, read first-use diagnostics, locate the setup entry point of the current skill itself according to the receipt; handle installation failures and native invocation failures separately.
The related creation task reads Shot Tracking Scenario, to verify native parameters, object context, local rework, and delivery boundaries.
Before shot tracking, first read this skill'sTracking First Use and Decode Verification: verify source pixels and actual keyframes, and distinguish planned frame counts from completed results.
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.