zereight

Run iterative agent evaluation and improvement loops (judge, improve, scenarios, playbooks) via pi-autocontext or autoctx CLI. Use when improving repeatable agent workflows (review rubrics, skill quality, scenario-based feedback), checking run status, or accumulating knowledge—not for one-off code edits or repo pattern drift (use continuity) or single PR review (use zereight-review).

Pi-native evaluate→improve→persist harness. Provides a systematic loop for generating candidates, judging them against rubrics, and accumulating knowledge as playbooks. Use for iterative output improvement, not for one-off edits (use continuity) or single PR review (use zereight-review).

Audit agent skills — token cost, duplicates, outdated plugin versions, unused skills, and overly long descriptions. Use when trimming skill prompt budget, finding duplicate or unused skills, auditing plugin versions, or deciding which skills to remove.

Diagnose why a skill is missing from Pi /skill or Cursor lists. Use when a skill exists on disk but does not appear, Pi shows only project or extension skills, or after adding a repo skill under .agents/skills/.

Comprehensive code review skill for practical PR feedback. Use for feature, bugfix, and refactor reviews. Prioritizes correctness, edge cases, logic invariants, fallback-chain safety, async state transitions, architecture analysis, OWASP security, and clear actionable feedback.

Skill map and inventory workflow. Use for scanning installed skills, skill overview, 스킬 목록, skill map.

Inspects Cursor agent transcript history, analyzes repeated assistant mistakes, and turns durable learnings into corrective skills or context updates. Use when the user asks to review previous Cursor chats, learn from a failed interaction, run /session-lessons, or stop the model repeating a mistake.

Comprehensive code review skill for practical PR feedback. Use for feature, bugfix, and refactor reviews. Prioritizes correctness, edge cases, logic invariants, fallback-chain safety, async state transitions, architecture analysis, OWASP security, and clear actionable feedback.

Use this skill when working with the GitLab MCP server tools for merge requests, issues, repositories, pipelines, work items, variables, dependency proxy, webhooks, search, CI catalog, and related GitLab workflows.

Clean AI-generated code slop with regression-safe workflow. Activate when user says: deslop, anti-slop, AI slop, cleanup slop, clean up this code, remove bloat.

Cancel any active OMG mode and clean up state. Activate when user says: cancel, stop, cancelomg, stopomg, abort, kill it, stop everything.

Claude-Codex-Gemini tri-model orchestration for multi-perspective analysis. Activate when user says: ccg, tri-model, three models, multi-model, cross-validate, get multiple opinions, compare models.

Canonical cross-language coding standards reference. Shared rules embedded by reviewer agents. Activate when: viewing coding standards, checking naming rules, reviewing style baseline, consulting style guide, what are the rules.

2-stage pipeline: trace (causal investigation) then deep-interview (requirements). Activate when user says: deep dive, deep-dive, investigate deeply, trace and interview.

Socratic deep interview with mathematical ambiguity gating. Activate when user says: deep interview, interview me, ask me everything, don't assume, make sure you understand, ouroboros, socratic, I have a vague idea, not sure exactly what I want.

Full autonomous execution from idea to working code. Activate when user says: autopilot, auto-pilot, autonomous, build me, create me, make me, full auto, handle it all, or "I want a/an..."

Use this skill to inspect and operate GitLab projects without MCP by calling the GitLab REST API directly. Use it for GitLab merge requests, issues, repository files, code search, pipelines, jobs, and review comments when a GitLab token is available.