software

Runs an eight-persona software engineering review panel over codebases and synthesizes consensus findings.

4|Updated Jun 9, 2026
One-click install
npx skills add https://github.com/laicluse/agent-fieldkit --skill software-laicluse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: software
Source: https://github.com/laicluse/agent-fieldkit/tree/main/.agents/plugins/generated/gurus/skills/software
Command: npx skills add https://github.com/laicluse/agent-fieldkit --skill software-laicluse

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Single-perspective code reviews often miss issues or reflect one reviewer's bias. This Skill convenes eight opinionated engineering personas (Beck, Fowler, Uncle Bob, DHH, Metz, Evans, Hickey, Ousterhout) to review the same code independently, then surfaces only the improvements that reach consensus across fundamentally different philosophies. ## Core Features & Use Cases - Parallel multi-persona review: Dispatches eight reviewer agents (via native subagents when available, or sequential single-session passes as fallback) that read the full codebase and return structured critiques. - Consensus synthesis: Groups semantically similar findings, counts agreement, and splits results at a 6/8 threshold into an action plan versus discussion points, while preserving singleton observations. - Scoped or full-codebase review: Defaults to reviewing all source files rather than just a diff, unless the user explicitly narrows the scope to a file, directory, or commit range. - Use Case: Before merging a large feature branch, run the panel over the repository to get a prioritized, consensus-backed list of refactoring and design improvements, then type "doe het" to execute the agreed fixes one commit at a time. ## Quick Start Ask the agent to run the software guru panel over the current repository and present the consensus action plan.

Frequently Asked Questions about software

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

FAQPage Schema
How do I run a multi-perspective code review with AI agents?▼

Invoke the skill against your repository and it dispatches eight reviewer personas, each reading the full file list and returning structured critiques. The results are grouped, counted, and presented as a consensus action plan plus discussion points.

How does the guru panel decide which issues to act on?▼

Findings are grouped semantically and counted across the eight reviewers. Points reaching 6 or more of 8 become the action plan; lower-consensus points appear as discussion items and are only executed with explicit user approval.

Does the review cover the whole codebase or just the diff?▼

By default it reviews the full codebase, since diff-only review misses problems in unchanged files. A narrower scope such as a file, directory, or commit range is used only when the user explicitly requests it.

Can I use this skill without subagent support in my AI host?▼

Yes. When native subagents are unavailable, the same protocol runs as eight clearly separated single-session passes, and the final review states that the fallback mode was used.

What happens after the review is presented?▼

Typing "doe het" executes the consensus points sequentially with one commit per logically independent change. Alternatively, the points can be handed to an explicitly named autonomous-execution skill, which stops once all points are committed.