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Agent skill

Gap Analysis

by TanStack in TanStack/ai

Audit TanStack AI provider adapters for feature parity gaps and outdated model lists.

MITAuto-check passed

Install Gap Analysis

skills CLI
$ npx skills add TanStack/ai --skill gap-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install TanStack/ai gap-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/TanStack/ai.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.grok/skills/gap-analysis .claude/skills/gap-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gap-analysis
GitHub stars
3.2k
Token cost
~1.9k tokens
SKILL.md length
748 words
Files
4 (incl. references)
Skills in repo
24
Repo updated
First seen
Licence
MIT

At a glance

Audit TanStack AI provider adapters for feature parity gaps and outdated model lists.

  • Works in 7 steps: Parse scope. If missing, AskUserQuestion… → Load the truth files, then read the… → Research upstream. Use WebFetch against… → …
  • SKILL.md covers Invocation, Workflow, Critical rules and Known providers, plus 3 more sections
  • Calls git; needs ARK_API_KEY and BYTEPLUS_VOICE_API_KEY

What it does

Gap Analysis is an agent skill from TanStack/ai. Audit TanStack AI provider adapters for feature parity gaps and outdated model lists.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.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/audit-checklist.md`, `references/provider-doc-urls.md` and `references/report-template.md`).

It works with TanStack and Model Context Protocol. The repository describes itself as: 🤖 Type-safe, provider-agnostic TypeScript AI SDK for streaming chat, tool calling, agents, and multimodal apps across OpenAI, Anthropic, Gemini, React, Vue, Svelte, and Solid. The licence is MIT.

Example prompts

  • “/gap-analysis”

Requirements

  • A credential in ARK_API_KEY
  • A credential in BYTEPLUS_VOICE_API_KEY

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Parse scope. If missing, AskUserQuestion with the four options above.
  2. Load the truth files, then read the per-scope inputs you need
  3. Research upstream. Use WebFetch against the curated URLs in
  4. Walk the audit dimensions in references/audit-checklist.md
  5. Fan out for --all: launch one Explore subagent per provider, max 3
  6. Write the report to .agent/gap-analysis/YYYY-MM-DD-.md using
  7. Print the report path and a 5-line summary to the user.

What it can do on your machine

Read from SKILL.md and the folder at commit 5a41239. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • ARK_API_KEY
    • BYTEPLUS_VOICE_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gap Analysis loads about 1.9k tokens when it runs, and up to ~8.4k if it reads all its reference files. Until then it costs about 76 tokens; SKILL.md has 748 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~76
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from TanStack/ai at commit 5a41239, republished under its MIT licence (© TanStack). 748 words, ~1,943 tokens.

Download SKILL.mdSave it as .claude/skills/gap-analysis/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
gap-analysis
description
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.

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

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

Workflow

  1. Parse scope. If missing, AskUserQuestion with the four options above.
  2. 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)
  3. 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).
  4. 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.
  5. 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.
  6. 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.
  7. Print the report path and a 5-line summary to the user.

Critical rules

  1. Never edit feature-support.ts or any model-meta.ts. The report is read-only — the maintainer applies changes.
  2. Always reference line numbers when citing exclusions (e.g., feature-support.ts:57) so the maintainer can jump to them.
  3. 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.
  4. Cite sources. Every claim "upstream supports X" must link the upstream doc page you read. No claims from training data.
  5. Use today's date from the system context (currentDate). Never invent.
  6. Quote the relevant snippet from feature-support.ts when flagging a parity gap, so the report is self-contained.
Show full SKILL.md (262 more words)Show less

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 fileActivity kind
text.ts / text-chat-completions.ts / responses-text.tstext
summarize.tssummarize
image.tsimage
audio.tsaudio
video.tsvideo
speech.ts / tts.tstts
transcription.tstranscription

(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.

© TanStack, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in .grok/skills/gap-analysis of TanStack/ai.

  • SKILL.md
  • references/audit-checklist.md
  • references/provider-doc-urls.md
  • references/report-template.md

Open the folder on GitHubat commit 5a41239

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Works with

Questions about Gap Analysis

What does Gap Analysis do?

Audit TanStack AI provider adapters for feature parity gaps and outdated model lists. Gap Analysis is an agent skill from TanStack/ai. Audit TanStack AI provider adapters for feature parity gaps and outdated model lists.

How do I install Gap Analysis in Claude Code?

Run `npx skills add TanStack/ai --skill gap-analysis -a claude-code`. Or copy the skill folder (.grok/skills/gap-analysis in TanStack/ai) into .claude/skills/gap-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Gap Analysis in Codex?

Run `npx skills add TanStack/ai --skill gap-analysis -a codex`. Or copy the skill folder (.grok/skills/gap-analysis in TanStack/ai) into .agents/skills/gap-analysis in your project. Codex loads it when a task matches its description.

Can I use Gap Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add TanStack/ai --skill gap-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gap-analysis, .gemini/skills/gap-analysis, .github/skills/gap-analysis and .opencode/skills/gap-analysis in your project.

What does Gap Analysis need to run?

Going by SKILL.md and its folder, Gap Analysis needs the command-line tools its instructions call (git) and credentials named ARK_API_KEY and BYTEPLUS_VOICE_API_KEY. Our summary lists: A credential in ARK_API_KEY; A credential in BYTEPLUS_VOICE_API_KEY.

Does Gap Analysis access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Gap Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Gap Analysis use?

Gap Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gap Analysis use?

About 1.9k tokens (SKILL.md is roughly 7.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.5k tokens, read only when the agent opens those files.

What are the alternatives to Gap Analysis?

Skills that share tags, products or a category with Gap Analysis: Bm Md (miantiao-me/bm.md, 616 stars), Svgrid (sv-grid/sv-grid, 179 stars), holaOS App Builder SDK (holaboss-ai/holaOS, 11k stars) and MCP Server Builder (anthropics/skills, 180k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gap Analysis?

TanStack (a GitHub organization) maintains it in TanStack/ai, which has 3,169 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on October 7, 2026.

Source: TanStack/ai on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.