agentmods.dev

gap-analysis

A maintainer report that checks TanStack AI provider adapters against each provider’s documentation for missing features and outdated model lists.

In plain words
What is it for?
Use it to audit one provider, one feature, model updates, activity coverage, or all providers, then save a dated Markdown report under `.agent/gap-analysis/`.
Why use it?
It reveals differences between the project’s support matrix and what providers actually offer, without changing source files.

Skill for Claude CodeCodex

3.0k 2d ago A scan 65 tokens original MIT
View the source file TanStack/ai
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/tanstack/ai/gap-analysis
Any agent
npx skills add TanStack/ai --skill gap-analysis
Clone the repo
git clone --depth 1 https://github.com/TanStack/ai

Made for: Claude Code, Codex.

Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,883 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00065 $0.01883
Opus 5 $0.00032 $0.00941
Sonnet 5 $0.00013 $0.00377
Haiku 4.5 $0.00006 $0.00188

Measured 2d ago against content hash c3f67ecfff47, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

gap-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/gap-analysis/SKILL.md · 142 lines

How it starts

The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

  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.

Read the full file on GitHub · 142 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 142 lines · 65 tokens per session scan A c3f67ecfff47

Subscribe to this mod's changes

gap-analysis is a skill published in the GitHub repository TanStack/ai (3,045 stars, last pushed 2d ago), licensed MIT. It adds 65 tokens to every session and 1,883 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Data and AITestingWriting and docsaiai-agentsai-sdkanthropicchatbotfunction-calling
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