Writing specs manually is slow. Skipping specs causes rework. Using ChatGPT to generate them produces output that doesn't know your codebase, your stack, or your constraints — and your coding agents flail against it just as much as against a vague paragraph.
Tekk.coach automates spec generation differently: the agent reads your actual codebase before writing a single word. Every question, every architectural option, every subtask in the output references your real files, patterns, and dependencies. The spec you get is grounded, complete, and executable — not a formatted template with your feature name swapped in.
How Tekk.coach Automates Spec Generation
Most "automated" spec tools are one-shot: you type, AI formats. Tekk is a structured multi-turn workflow that produces categorically better output.
The agent starts by reading your repository. Semantic search via embeddings for intelligent code discovery. File search and regex lookup for precise pattern matching. Directory browsing for structural understanding. Repository profiling: languages, frameworks, services, packages, dependencies. This happens before any question is asked. The agent knows your codebase, not a generic version of your problem type.
Then it asks 3-6 questions grounded in what it found. Not "What is the expected behavior?" — instead: "Your auth layer uses JWT tokens stored in httpOnly cookies, but this feature touches the session management service. Which token validation pattern should govern this flow?" The questions are specific because the agent read your code.
When there are genuinely different architectural approaches, the agent presents 2-3 options with honest tradeoffs before locking in a direction. You decide, not the AI.
The complete spec streams into your task editor (BlockNote) in real-time as an editable working document — not a chat message you copy-paste somewhere. It includes: TL;DR, explicit Building/Not Building scope boundaries, subtasks with acceptance criteria and file references, dependency ordering, risk-flagged assumptions, and validation scenarios. That's your coding agent's input. That's what makes the difference between execution that ships and execution that requires three rounds of rework.
Key Benefits
Grounded in your codebase, not a generic template. Tekk reads your repo before generating anything. The spec that comes out references actual files, patterns, and constraints. Your coding agents execute correctly because they receive precise, specific instructions — not a templated document with your feature name in the title.
Scope is enforced by default. Every spec includes an explicit "Not Building" section. This is not optional and it's not a text field you fill in manually — it's part of the automated output. Scope creep starts where the spec ends. Tekk closes that gap structurally.
Multi-turn, not one-shot. The best specs come from informed questions, not a single prompt. Tekk's Search → Questions → Options → Plan workflow ensures the spec reflects your actual system constraints and your actual decisions — not the AI's best guess at both.
An editable document, not a chat message. The spec lives in your task editor, not in a chat thread. It's persistent, editable by you and your team, and linked to your kanban card. Close the chat, reopen the task — your spec is still there.
Your coding agents can execute it. The spec format (behavioral subtasks with acceptance criteria and file references) is specifically designed for AI coding agents. Better inputs → better outputs. The spec is the moat.
How It Works
Step 1: Connect your repository. GitHub, GitLab, or Bitbucket. Tekk's agent indexes it with semantic search so every spec is grounded in your actual code, not generic patterns.
Step 2: Describe what you're building. Plain language. No special format required. "Add magic link auth," "Build a CSV export endpoint," "Refactor the payment service to support multi-currency." The agent handles the rest.
Step 3: Answer 3-6 questions. Questions are specific to what the agent found in your code. Not templates. You're answering questions about your actual architectural constraints — and you get a spec that reflects your actual decisions.
Step 4: Review architectural options (when applicable). When there are genuinely different approaches, the agent presents 2-3 options with honest tradeoffs. You choose the direction. When there's an obvious path, this step is skipped.
Step 5: Review your spec. The complete spec streams into the BlockNote editor in real-time. TL;DR, Building/Not Building scope, subtasks with acceptance criteria and file references, dependency ordering, assumptions with risk levels, validation scenarios. Edit anything. Then execute.
Who This Is For
Founders and solo builders shipping with AI coding agents. You don't have time to write 20-page PRDs and you shouldn't need to. Tekk generates a precise, executable spec from a plain language description in under 10 minutes.
Product managers and technical PMs who need specs that are grounded in system reality, not organizational narrative. If your specs keep getting revised during implementation because the engineer found architectural conflicts the spec didn't account for — Tekk's codebase-first approach fixes this.
Developers using Cursor, Codex, or Claude Code who've learned that the agent is only as good as what you give it. You've been burned by vague prompts producing wrong architectures. Tekk is the tool that produces the inputs your agents need.
Not for teams that want spec templates to fill in manually. Not for enterprises that need spec governance workflows. Tekk automates spec generation — it doesn't provide a spec management system.
What Is Automated Spec Generation?
Automated spec generation refers to AI systems that produce structured software specifications from natural language input, reducing or eliminating manual spec-writing effort. The output of a spec generator is not a narrative document — it's a structured artifact with acceptance criteria, scope boundaries, subtask sequencing, and file-level guidance that a coding agent can execute against.
The term covers a wide capability range. At the low end: AI reformats your description as bullet points. At the high end: AI reads your codebase, conducts an informed planning dialogue, presents architectural options, and produces a complete spec with traceable acceptance criteria and explicit scope enforcement. The difference in output quality — and downstream coding agent performance — is significant.
The demand for automated spec generation has risen alongside AI coding agents. As developers hand more implementation work to autonomous agents, the spec quality ceiling has become the performance ceiling. Agents executing against vague prompts produce vague code. The discipline of spec-driven development — write a precise spec, then execute — is being automated by tools like Tekk.coach because it's both high-value and previously high-effort.
Ready to Try Tekk.coach?
Your coding agent needs a spec, not a paragraph. Connect your repo and get a codebase-grounded specification in minutes — complete with scope boundaries, acceptance criteria, and everything your agents need to execute correctly.