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create-litert-lm-android-demo-app

Guides the agent to orchestrate and implement a standalone LiteRT-LM Android demo app with backend selection and multi-modality support using Bazel 9. Use when asked to create a demo app for LiteRT-LM on Android. Don't use for general Android app development without LiteRT-LM, or for building the core library alone without an app.

Quality

86%

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SecuritybySnyk

Critical

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SKILL.md
Quality
Evals
Security

Quality

Content

77%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is exceptionally well-structured as a sequenced, validated workflow with excellent progressive disclosure into a complete reference bundle. Its main cost is token efficiency: redundant verbatim-copy and full-reload mandates, plus emphatic repetition of constraint language, inflate the file without adding guidance value.

Suggestions

Conciseness: drop the requirement to copy the Core Constraints verbatim into task.md OR the requirement to reload the entire SKILL.md before every step — keeping one of the two mechanisms preserves the context-refresh safety goal at roughly half the token cost in task.md and in the skill body.

Conciseness: replace the repeated ALL-CAPS enforcement phrasing ("STRICTLY FORBIDDEN", "SOLE, absolute plan", repeated "MUST") with a single terse constraint list; one statement of each rule carries the same behavioral weight.

Actionability: inline the few concrete commands that gate the workflow (sdkmanager install line for NDK 27.3.13750724, the model download command, and the Bazel build invocation) or at minimum state how the NDK version is verified, so the body's verification steps are executable without opening a reference file first.

DimensionReasoningScore

Conciseness

There is no educational padding (it never explains concepts Claude already knows), but the body is noticeably repetitive in its enforcement language: the Core Constraints are mandated to be copied verbatim into task.md (1.2) while step 1.3 additionally requires reloading the entire SKILL.md before every step, and ALL-CAPS emphatic restatements ("STRICTLY FORBIDDEN", "SOLE, absolute plan") recur across the constraints section. This fits the 3-anchor (mostly efficient but could be tightened) and stays above the 2-anchor because the excess is directive duplication, not unnecessary explanation.

3 / 5

Actionability

The body gives concrete, specific guidance — exact reference filenames to load per step, NDK version 27.3.13750724, a default model URL, a default device (Pixel 10), and file outputs to create (compliance_review_*.md) — matching the 4-anchor of mostly executable guidance with minor gaps. It does not reach the 5-anchor because executable commands (SDK download, model download, Bazel build invocation, how to verify the NDK version) are absent from the body and delegated to reference files.

4 / 5

Workflow Clarity

Seven strictly numbered steps with sub-steps are sequenced in exact order, each phase has an explicit validation checkpoint (compliance checklists initialized to 'Pending', audited with line-numbered evidence, then re-verified in 6.1), and completion is gated on no Pending/Fail items — matching the 5-anchor of clear sequence with explicit validation, feedback loops, and checklists.

5 / 5

Progressive Disclosure

The SKILL.md is a well-organized overview and every referenced file (all 7 in references/) exists, is one level deep, and is loaded just-in-time at the exact step that needs it — matching the 5-anchor of a clear overview with well-signaled one-level-deep references. The JIT reading rule explicitly prevents loading references early, keeping the overview lean.

5 / 5

Total

17

/

20

Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete scope, explicit third-person 'what', an explicit 'Use when' trigger, and negative boundary clauses that minimize conflict risk. The only weakness is that the capability list stays at a coarse grain and lacks synonym-level trigger variations.

DimensionReasoningScore

Specificity

The description names the domain and several concrete facets — "orchestrate and implement a standalone LiteRT-LM Android demo app with backend selection and multi-modality support using Bazel 9" — which matches the 4-anchor (several specific actions, minor gaps). It falls short of the 5-anchor because the actions are coarser-grained (orchestrate/implement) rather than a comprehensive list of distinct concrete operations, and it is above the 3-anchor because more than 1-2 specific actions are enumerated.

4 / 5

Completeness

It explicitly answers both questions: the "what" ("orchestrate and implement a standalone LiteRT-LM Android demo app with backend selection and multi-modality support using Bazel 9") and the "when" ("Use when asked to create a demo app for LiteRT-LM on Android"), plus an explicit when-not clause — matching the 5-anchor's clear what AND when with concrete trigger phrases. The 4-anchor is surpassed precisely because the 'when' is fully explicit rather than improvable.

5 / 5

Trigger Term Quality

Natural phrases a user would say are present ("create a demo app for LiteRT-LM on Android", "demo app", "Android") alongside boundary phrases ("general Android app development", "building the core library"), matching the 4-anchor of good coverage with a few natural terms missing. It does not reach the 5-anchor because common synonyms and variations (e.g., sample app, example app, APK demo) are absent, and it clears the 3-anchor because the key user-facing terms are all covered.

4 / 5

Distinctiveness Conflict Risk

The niche is unambiguous (LiteRT-LM demo apps) and the negative triggers ("Don't use for general Android app development without LiteRT-LM, or for building the core library alone") actively reduce overlap with adjacent skills, matching the 5-anchor of a clear niche with minimal conflict risk. The 4-anchor's 'minor overlap risk' does not apply given the explicit exclusions.

5 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

google-ai-edge/LiteRT-LM

Path

agents/skills/create-litert-lm-android-demo-app/SKILL.md

Reviewed

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