Verified:GitHub

stack-discovery

Scan project manifests and CI files to produce an evidence-backed stack fingerprint.

Updated Apr 27, 2026

Search Tags:#requirements-spec #evidence gathering #stack discovery #project intelligence #frontend detection #backend detection #assistant planning

Ready to run

npx skills add https://github.com/vTRKA/supervibe --skill stack-discovery

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: stack-discovery
Source: https://github.com/vTRKA/supervibe/tree/main/skills/stack-discovery
Command: npx skills add https://github.com/vTRKA/supervibe --skill stack-discovery

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Stack Discovery prevents confusion and wrong assumptions at the start of an AI coding session by determining what technology stack your project actually uses before plans or changes are made.

Core Features & Use Cases

Evidence-based stack fingerprinting: Reads manifests, CI/IaC, and runtime-relevant signals to infer frontend, backend, database, and infra with cited evidence.
Confirmation-driven unknown handling: Stops and requests one question at a time when evidence is missing or conflicting, avoiding incorrect auto-inference.
Safe handoff to next workflow: Produces a requirements-spec-ready output for supervibe:genesis or supervibe:requirements-intake, including confidence, blockers, and nextAction.
Chrome extension specialization: Detects Chrome extension runtime and build tooling (e.g., MV2 vs MV3 and common bundlers) using manifest_version and corroborating build evidence.

Quick Start

Tell your AI to run stack discovery for an unfamiliar repo by requesting stack discovery and confirming any single missing details it asks for.

Frequently Asked Questions about stack-discovery

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect my project's technology stack from manifests before making code changes?▼

Stack discovery determines your project's technology stack by scanning manifests, CI/IaC files, and existing instructions to produce an evidence-backed stack fingerprint. It prevents wrong assumptions at the start of an AI coding session by confirming unknown details.

What is evidence-based stack fingerprinting for unfamiliar repositories?▼

Evidence-based stack fingerprinting reads manifests, CI/IaC, and runtime signals to infer frontend, backend, database, and infrastructure components. It cites specific evidence for each detected component to avoid incorrect auto-inference in unfamiliar repositories.

How do I handle missing or conflicting technology evidence during stack discovery?▼

Stack discovery uses confirmation-driven unknown handling, stopping to request one question at a time when evidence is missing or conflicting. This prevents the AI from making incorrect auto-inferences about your project's technology stack.

Can I detect Chrome extension build tooling and manifest versions using stack discovery?▼

Yes, stack discovery specializes in Chrome extension runtime detection by identifying MV2 versus MV3 using manifest_version and corroborating build evidence. It detects common bundlers and build tooling specific to Chrome extension development.

How do I start requirements intake after detecting a project's stack?▼

Stack discovery generates a requirements-spec-ready output containing status, evidence, confidence, nextAction, and blockers for safe handoff to requirements intake or genesis workflows. You simply request stack discovery and confirm any missing details it asks for.

What are the limitations of automatic project intelligence for new codebases?▼

Automatic project intelligence requires manifest-based component detection and cannot infer stacks without explicit evidence. It stops and asks for explicit user confirmation for unknowns, meaning it will not guess when encountering missing or conflicting technology signals.