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firebase-ai-logic-basics

Integrates Firebase AI Logic (formerly Vertex AI for Firebase) into mobile and web apps (Android, iOS, Web, and Flutter) to call Gemini models from client code. Use when provisioning or initializing Firebase AI Logic, or adding Gemini features to an app: text generation, multimodal input, chat, streaming, structured output, hybrid on-device inference, App Check protection, or switching models with Remote Config.

1.16x
Quality

78%

Does it follow best practices?

Impact

98%

1.16x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/firebase-ai-logic-basics/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

60%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 provisioning and App Check workflows are strong — exact CLI commands, clear sequencing, and thoughtful failure-mode warnings — and the bundle structure correctly pushes platform code into real, well-signaled reference files. Weaknesses are concentrated in duplicated content (repeated model-name warning, repeated reference lists, inlined TTS detail) and several capability sections that are empty or pointer-less one-liners.

Suggestions

Fill or remove the empty capability sections ('Text-Only Generation', 'Search Grounding with the built in googleSearch tool') — either give each a one-line concrete directive plus a pointer to the relevant platform reference, or delete the headings.

De-duplicate: keep the 'Use current model names' warning in one place, and replace the bottom 'References' section with the 'Initialization Code References' list only, since both list the same five files.

Trim the TTS capabilities bullet list to 2-3 lines and defer the model names, formats, and audio-directive details to references/tts_generation.md, which already covers them.

Add an explicit validation checkpoint after 'npx -y firebase-tools@latest init ailogic' (e.g., re-run 'projects:list' or check the Gemini API enablement in the console) so the setup workflow closes the loop.

DimensionReasoningScore

Conciseness

The setup sections are command-driven and tight, but there is avoidable padding: the 'CRITICAL: Use current model names' warning appears verbatim twice, the bottom 'References' section repeats the same five links already listed under 'Initialization Code References', the Overview re-explains product positioning Claude can infer, and the ~20-line TTS capability bullet list duplicates content that the linked references/tts_generation.md already carries. This fits 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than the level-4 anchor, whose over-explanation would be only minor.

3 / 5

Actionability

Provisioning is fully executable ('npx -y firebase-tools@latest init ailogic', 'projects:list', 'apps:list') but several capability sections are empty or directive-only with no code or pointer: 'Text-Only Generation' and 'Search Grounding with the built in googleSearch tool' are bare headings, 'Enforce a specific JSON schema for the response' and 'Maintain history automatically using startChat' are one-line descriptions with no example or reference link. This is concrete guidance that is incomplete with missing key details — the level-4 anchor requires mostly executable guidance with only minor gaps.

3 / 5

Workflow Clarity

Setup is clearly sequenced (prerequisites → verify active project via the 'current' marker → confirm a platform app exists → init ailogic) with anticipated failure modes (PERMISSION_DENIED warning, fallback when the CLI can't run, debug-token churn tip) and a well-sequenced App Check debug-token workflow for local dev and CI. It misses the level-5 anchor because there is no explicit post-'init ailogic' validation step to confirm the service was provisioned — checkpoints are present but not closed-loop.

4 / 5

Progressive Disclosure

The body is an overview with one-level-deep references to real files (usage_patterns_android.md, ios_setup.md, usage_patterns_web.md, flutter_setup.md, tts_generation.md), organized per platform under 'Initialization Code References' and per capability for TTS. It falls short of the level-5 anchor due to minor organization gaps: the duplicated bottom 'References' list repeats the same links, and TTS capability detail is inlined in the body alongside its reference instead of being fully split out.

4 / 5

Total

14

/

20

Passed

Description

88%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: third-person voice, explicit what-and-when structure, and a concrete capability list with a useful former-name synonym. The only weakness is keyword coverage that omits a few natural user phrasings (notably image generation and generative-AI terminology) supported by the skill body.

DimensionReasoningScore

Specificity

The description names concrete capabilities in third person: 'Integrates Firebase AI Logic... to call Gemini models from client code' followed by a specific list — 'text generation, multimodal input, chat, streaming, structured output, hybrid on-device inference, App Check protection, or switching models with Remote Config'. This matches the comprehensive multi-action anchor; it is not the level-4 anchor because coverage of the skill's feature set is broad rather than having 'minor gaps'.

5 / 5

Completeness

Both questions are explicitly answered: the first sentence states what the skill does ('Integrates Firebase AI Logic... to call Gemini models from client code') and an explicit 'Use when' clause gives concrete triggers ('Use when provisioning or initializing Firebase AI Logic, or adding Gemini features to an app:...'). This matches the top anchor exactly; the level-4 anchor's 'when could be more explicit' does not apply.

5 / 5

Trigger Term Quality

Good keyword coverage with natural product terms ('Gemini', 'chat', 'streaming', 'structured output', 'App Check', 'Remote Config') plus the former-name synonym '(formerly Vertex AI for Firebase)'. It falls short of the level-5 anchor because common user phrasings like 'generative AI', 'image generation', or 'prompts' are absent — notably the body supports image generation (Nano Banana) and TTS, which have no trigger terms here.

4 / 5

Distinctiveness Conflict Risk

The niche is clear (Firebase AI Logic client SDKs for Gemini) with product-specific triggers unlikely to fire for unrelated skills. Minor overlap risk remains with sibling skills: a broad Firebase setup skill (the body itself references a 'firebase-basics' skill) and generic 'Remote Config' requests could partially match, so it fits 'mostly distinct; minor overlap risk' rather than the minimal-conflict level-5 anchor.

4 / 5

Total

18

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository

firebase/agent-skills

Path

skills/firebase-ai-logic-basics/SKILL.md

Reviewed

Table of Contents

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