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CD video-editor-development

Architecture guidance for the LTX video editor — editor store, selectors, actions, undo/redo, persistence, keyboard behavior, timeline semantics, and hot-path performance.

The skillemall take

The skill promises architectural guidance for the LTX video editor: store, selectors, actions, undo/redo, persistence, keyboard behavior, timeline semantics, and hot-path performance. In practice, it's a single file with 12 tokens—essentially a title with no substance.

Tests found no critical or medium issues, but quality scores 46 percent, process 38 percent. No model runs, no sandbox testing. Compatible with all major platforms, but that doesn't fix the emptiness.

Don't install it. Even as a cheat sheet on editor architecture, the skill fails—there's nothing to read.

Lightricks/LTX-Desktop Agent Skills author: Lightricks 1 file body ≈ 12 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Architecture guidance for the LTX video editor — editor store, selectors, actions, undo/redo, persistence, keyboard behavior, timeline semantics, and hot-path…

As a process D 38/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

ReferenceMedia and videotype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
78/100
safety, quality, tests
Safety 60%
100
Quality 40%
46
Run on models
none yet
Process rating
D
38/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: LTX-Desktop

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 38/100

  • 0Steps. Prose only: no discrete steps
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 12 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +4Structure: 0 headings, hard to scan
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -15SKILL.md body under 300 characters: nearly empty
  • +1No license
  • +2Single-language instructions
  • +3Description length 171: enough signal without eating the budget

Quality base 70; lint remarks subtract, signals add up to 100. Result: 46.