Skill · AI & Development

Technical Spec Writer

Generate rigorous technical specifications that focus on failure modes, data models, and edge cases to catch flaws early. Install in 30 seconds.

Category: AI & Development
Deliverable: 1 .skill bundle
Outputs: —
Last updated: 13 Jun 2026

$9.99 One-time · lifetime updates

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Overview

What Technical Spec Writer does.

Technical Spec Writer asks four focused questions about your stack, requirements, team, and audience, then produces a spec structured around the sections reviewers actually use to find problems: data model with rationale, state machines for multi-state flows, an explicit failure modes table, security surface, performance assumptions, rollout and rollback plan, and a clean separation of open questions from settled decisions. Rejected alternatives are documented with their real losing reasons, not vague dismissals, which cuts re-litigation in review meetings.

Give it something like: 'We're adding a per-user rate limiter to a Node/Postgres API that currently has no rate limiting. Traffic is 2k req/s at peak, multi-region. Need to ship in two sprints. Audience is two senior engineers and a security lead doing design review.' That context is enough for the skill to calibrate depth, surface the right failure modes, and flag the distributed-counter consistency question before your reviewers do.

The output excerpt below shows the kind of section that separates a reviewable spec from an approvable one: Failure Modes: – Redis unavailable: fail open with degraded limiting (log + alert); document SLA impact – Counter drift under split-brain: bounded over-admission for up to TTL window; acceptable vs. strict quota use cases differ — OPEN – Migration: existing sessions have no counter row; treat as zero-count on first request, not as bypass Open Questions vs. Decisions: – DECIDED: token-bucket over sliding window (simpler rollback path) – OPEN: whether per-user limits apply to internal service tokens — needs product input before implementation

Who it's for

Engineers and tech leads who need a design doc their reviewers can actually attack, not a narrative that describes the happy path and defers the hard questions to implementation. Particularly useful when the system touches shared state, multi-step migrations, or security-sensitive surfaces where an undocumented assumption becomes a production incident.

How it works

Three steps. About two minutes.

Install

Add the .skill file to your Claude app. ~10 seconds.

Run it on your work

Invoke the skill and paste in your material.

Apply the output

Review, keep what works, and use it.

In depth

Why a Claude skill beats a prompt template.

A copy-paste prompt runs one static pass and stops. A skill is a bundled program — instructions, examples, and a workflow Claude runs as a unit: it asks for the right input, applies the same pattern every time, and returns the structured outputs above.

FAQ

Common questions.

What input does the skill need to produce a useful spec?

At minimum: what the feature does, the relevant parts of your existing stack, who will review the spec, and any hard constraints on timeline or approach. The skill will ask if anything is missing before drafting.

Does it follow a specific spec format like RFC or PRD?

It follows a structure oriented around design review — problem restatement, proposed design, data model, API changes, failure modes, rollout/rollback, alternatives considered, security and performance, and open questions. If your team uses a named template, describe it in context and the skill will adapt.

How does it handle the 'alternatives considered' section?

It documents rejected options with the specific reason each lost — performance trade-off, complexity cost, operational burden — not just 'we chose X'. This section is written to prevent reviewers from re-proposing alternatives that were already evaluated.

Can it write specs for features that span multiple services or teams?

Yes. Provide the relevant cross-service context — contracts, ownership boundaries, shared data stores — and the spec will surface the integration failure modes and the questions that need cross-team answers before implementation starts.

What if I only have rough requirements, not a finished design?

That is the intended starting point. The skill will restate the problem and requirements first so you can confirm the framing, then propose a design. If key information is missing, it flags assumptions explicitly so you can correct them before the spec circulates.