discover-codebase-enhancements
Use when the user asks for a deep codebase analysis to identify and rank improvements, optimizations, architectural enhancements, or potential bugs aligned to developer, end-user, and agent jobs-to-be-done.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install kasperjunge-agent-resources-discover-codebase-enhancements
Repository
Skill path: .opencode/skill/discover-codebase-enhancements
Use when the user asks for a deep codebase analysis to identify and rank improvements, optimizations, architectural enhancements, or potential bugs aligned to developer, end-user, and agent jobs-to-be-done.
Open repositoryBest for
Primary workflow: Ship Full Stack.
Technical facets: Full Stack.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: kasperjunge.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install discover-codebase-enhancements into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/kasperjunge/agent-resources before adding discover-codebase-enhancements to shared team environments
- Use discover-codebase-enhancements for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
--- name: discover-codebase-enhancements description: Use when the user asks for a deep codebase analysis to identify and rank improvements, optimizations, architectural enhancements, or potential bugs aligned to developer, end-user, and agent jobs-to-be-done. --- # Discover Codebase Enhancements ## Overview Spend significant time crawling and analyzing the codebase to surface high-impact improvements. Center findings on the jobs-to-be-done of the codebase, developers, end users, and AI agents working in the repo. ## Inputs (ask if missing, max 5) - Target area or scope (whole repo or specific modules) - Primary user jobs-to-be-done and business goals - Known pain points or incidents - Constraints (time, risk tolerance, release window) - Evidence sources allowed (tests, metrics, logs) ## Jobs-to-Be-Done Lens - **Codebase**: reliability, simplicity, maintainability - **Developers**: speed, clarity, safe changes - **End users**: correctness, performance, usability - **AI agents**: discoverability, consistency, explicit patterns ## Workflow 1. **Deep crawl** - Read architecture docs, READMEs, key modules, and tests. - Search for hotspots (TODO/FIXME, large files, duplication, complex flows). 2. **Evidence gathering** - Note error-prone areas, missing tests, performance risks, and coupling. - Capture references to files/functions and concrete symptoms. 3. **Opportunity synthesis** - Group findings by theme: correctness, performance, DX, architecture, tests, tooling. 4. **Impact scoring** - Rate impact, effort, risk, and evidence strength. 5. **Ranked recommendations** - Present top enhancements with rationale and expected outcomes. ## Output Format ``` ## Codebase Enhancement Discovery ### Context Summary [1-3 sentences] ### JTBD Summary - Codebase: ... - Developers: ... - End users: ... - AI agents: ... ### Evidence Sources - Files/modules reviewed: ... - Patterns searched: ... - Tests or metrics considered: ... ### Ranked Enhancements 1) [Enhancement] - Category: ... - Impact: high | Effort: medium | Risk: low | Evidence: moderate - Rationale: ... - Affected areas: ... ### Quick Wins - ... ### Open Questions - ... ``` ## Quick Reference - Spend more time exploring than feels necessary. - Prefer evidence-backed findings over speculation. - Center recommendations on user and developer outcomes. ## Common Mistakes - Skimming without enough code context - Listing fixes without evidence or impact scoring - Ignoring AI agent or developer workflows - Recommending changes that fight existing architecture