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Build React chat interfaces with Vercel AI SDK v6. Covers useChat/useCompletion/useObject hooks, message parts structure, tool approval workflows, and 18 UI error solutions. Prevents documented issues with React Strict Mode, concurrent requests, stale closures, and tool approval edge cases. Use when: implementing AI chat UIs, migrating v5→v6, troubleshooting "useChat failed to parse stream", "stale body values", "React maximum update depth", "Cannot read properties of undefined (reading 'state')", or tool approval workflow errors.

modbender/skill-library-mcp Agent Skills author: modbender MIT 21 files · 1 script body ≈ 3 958 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Build React chat interfaces with Vercel AI SDK v6.

As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationGitHubSoftware developmentInfrastructureWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 21. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 47/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 11 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 64 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3958 tokens
    • low 11 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +3Output format is not stated: the model decides each time
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 537: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 64 items
    • +4Has examples (22 code blocks)
    • +4Reference files are cited in the instructions (5 of 6)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.