Gap Analysis

OrganizationPopular

TanStack

gap-analysis

Audit TanStack AI provider adapters for feature parity gaps and outdated model lists. Triggered as /gap-analysis <provider|feature <name>|models|--all>. Produces a dated markdown report under .agent/gap-analysis/. Maintainer tool — does not edit feature-support.ts or model-meta.ts directly.

Overview

Publisher	TanStack
Repository	`ai`
Skill name	`gap-analysis`
Stars	3.2K
Forks	361
Bundled files	3
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by TanStack on GitHub. Read the source before you install it.

Installation

Install the Gap Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/TanStack/ai/tree/main/.agents/skills/gap-analysis
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/TanStack/ai.git /tmp/ai
mkdir -p .claude/skills
cp -r /tmp/ai/.agents/skills/gap-analysis .claude/skills/gap-analysis

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gap Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gap Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gap Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Gap Analysis — TanStack AI adapter audit

You are auditing TanStack AI's provider adapters against each provider's upstream documentation. This is a maintainer tool. Your only output is a markdown report under .agent/gap-analysis/. Do not edit source files.

Invocation

Args	Scope
`<provider>` (e.g. `openai`)	One provider — all audit dimensions.
`feature <feature>` (e.g. `tts`)	One feature row of the matrix across all providers.
`models`	New-model diff for every provider.
`activities`	Activity-coverage diff: which of the 7 core activity
	kinds each provider ships an adapter for, vs. what
	upstream supports. (Dimension 6 only, all providers.)
`--all`	Full sweep (fan out subagents, one per provider).
(none)	Ask the user which scope via AskUserQuestion.

Workflow

Parse scope. If missing, AskUserQuestion with the four options above.
Load the truth files, then read the per-scope inputs you need:
- Matrix: testing/e2e/src/lib/feature-support.ts
- Types: testing/e2e/src/lib/types.ts (Provider + Feature unions, ALL_PROVIDERS, ALL_FEATURES)
- Adapter index: packages/ai-<provider>/src/index.ts
- Model meta: packages/ai-<provider>/src/model-meta.ts
- Core types: packages/ai/src/types.ts (Modality, ContentPart, ToolCall)
Research upstream. Use WebFetch against the curated URLs in references/provider-doc-urls.md. When a doc page has moved, fall back to WebSearch. For SDK API surface details use the context7 MCP server (mcp__plugin_context7_context7__resolve-library-id then mcp__plugin_context7_context7__query-docs).
Walk the audit dimensions in references/audit-checklist.md:
1. New models
2. Cross-adapter feature parity
3. Untracked features
4. Capability-flag drift
5. Telemetry / observability parity (usage tokens, cache/reasoning counts, request ids, logging asymmetry)
6. Activity coverage (which of the 7 core activity kinds each provider ships an adapter for vs. what upstream supports) — this is the only dimension for the activities scope; it's also rolled into --all.
Fan out for --all: launch one Explore subagent per provider, max 3 in parallel. Each subagent returns the multi-dimension findings for its provider; you synthesise into the combined report. The activities scope does not fan out — derive the provider×activity matrix centrally from the adapter files (see dimension 6), since it's a fast mechanical diff.
Write the report to .agent/gap-analysis/YYYY-MM-DD-<scope>.md using references/report-template.md. Date is today's ISO date. <scope> is openai / feature-tts / models / activities / all.
Print the report path and a 5-line summary to the user.

Critical rules

Never edit feature-support.ts or any model-meta.ts. The report is read-only — the maintainer applies changes.
Always reference line numbers when citing exclusions (e.g., feature-support.ts:57) so the maintainer can jump to them.
Distinguish three gap classes in the report:
- Real gap — upstream supports it, TanStack AI doesn't, no exclusion comment.
- Tested gap — TanStack AI doesn't list it but there's an exclusion comment in feature-support.ts (e.g., aimock format limitation). Not actionable code-wise; surface in "Out-of-scope" section.
- Stale capability flag — model-meta.ts declares a capability the model no longer has, or omits one it now has.
Cite sources. Every claim "upstream supports X" must link the upstream doc page you read. No claims from training data.
Use today's date from the system context (currentDate). Never invent.
Quote the relevant snippet from feature-support.ts when flagging a parity gap, so the report is self-contained.

Known providers

openai, anthropic, gemini, ollama, grok, groq, openrouter, bedrock (@tanstack/ai-bedrock; three-API surface — Converse default (adapter name bedrock-converse), Chat Completions opt-in (api: 'chat', adapter name bedrock), Responses opt-in (api: 'responses', adapter name bedrock-responses)), byteplus (@tanstack/ai-byteplus; text, video, tts, transcription, image — two products/keys: ModelArk ARK_API_KEY for text/video/image, Seed Speech BYTEPLUS_VOICE_API_KEY for tts/transcription), fal (media-only), elevenlabs (TTS-only). The feature matrix tracks openai, anthropic, gemini, ollama, grok, groq, openrouter, bedrock, bedrock-converse, bedrock-responses, and byteplus; fal and elevenlabs only appear in model/media audits.

Known features (19)

Canonical list is ALL_FEATURES in testing/e2e/src/lib/types.ts — always re-read it; this list is a snapshot:

chat, one-shot-text, reasoning, multi-turn, tool-calling, parallel-tool-calls, tool-approval, text-tool-text, structured-output, structured-output-stream, agentic-structured, multimodal-image, multimodal-structured, summarize, summarize-stream, image-gen, tts, transcription, video-gen.

Known activities (7)

Features (above) are matrix rows about behaviours within an activity. Activities are the coarser-grained core capability kinds in @tanstack/ai — each has a Base<Kind>Adapter and a provider "supports" one only if its package ships an adapter of that kind. Canonical list is the AdapterKind union in packages/ai/src/activities/index.ts — always re-read it:

text, summarize, image, audio, video, tts, transcription.

A provider's activity surface is derived mechanically from its adapter files: packages/ai-<provider>/src/adapters/. Filename → activity-kind map:

Adapter file	Activity kind
`text.ts` / `text-chat-completions.ts` / `responses-text.ts`	`text`
`summarize.ts`	`summarize`
`image.ts`	`image`
`audio.ts`	`audio`
`video.ts`	`video`
`speech.ts` / `tts.ts`	`tts`
`transcription.ts`	`transcription`

(cost.ts is a helper, not an activity adapter.)

Verification before finishing

Before printing the summary:

Report file exists and is non-empty.
git status shows only new files under .agent/gap-analysis/ — nothing under packages/ or testing/ should have been modified. Run git status and confirm.
Every "real gap" entry has an upstream doc URL.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gap Analysis AI skill do?

Why use Gap Analysis on TypingMind?

Because you install it once and use it with any model. Gap Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gap Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TanStack/ai/tree/main/.agents/skills/gap-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gap Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gap Analysis?

As many as you like. As long as a model supports skills, you can use Gap Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gap Analysis AI skill free?

Yes. It is published on GitHub by TanStack under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Gap Analysis — TanStack AI adapter audit

Invocation

Workflow

Critical rules

Known providers

Known features (19)

Known activities (7)

Verification before finishing

Bundled files

Frequently asked questions

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