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AD nestjs-trpc

Build end-to-end type-safe tRPC APIs inside NestJS using the nestjs-trpc adapter — @Router/@Query/@Mutation/@Subscription decorators, TRPCModule.forRoot, injectable middlewares and context, Zod input/output schemas, and the Rust CLI that generates the AppRouter type for clients. Use this skill whenever the user mentions nestjs-trpc, tRPC in a NestJS app, @Router or @Query decorators, TRPCModule, AppRouterHost, "typed API between my Nest backend and frontend", generating AppRouter types, or is migrating a NestJS GraphQL/REST API to tRPC — even if they don't name the library explicitly.

trycompai/crm Agent Skills author: trycompai MIT 6 files body ≈ 2 645 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Build end-to-end type-safe tRPC APIs inside NestJS using the nestjs-trpc adapter — @Router/@Query/@Mutation/@Subscription decorators, TRPCModule.forRoot…

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
D
49/100
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 239): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 49/100

    • 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. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2645 tokens
    • low The response is described with custom markup (3 tags): a typed call is more reliable

    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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 591: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.