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Show current context usage, active spec status, project progress, omega memory state, and continuation info at a glance.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | context-status |
| description | Show current context usage, active spec status, project progress, omega memory state, and continuation info at a glance. |
| user-invocable | true |
Display the current session state with project-level info.
~/.next-level/sessions/*/context_state for the current session~/.next-level/specs/*.jsoncontinuation.md files from previous sessionsomega_query() with the current project name to show recent decisions/milestonesgh issue list --milestone <current-epic> --state all for progressNEXT-LEVEL STATUS
=====================
Context: 67% ██████████████░░░░░░ (OK)
Active spec: "add-user-auth" — IMPLEMENTING (step 3/7)
Continuation: None pending
PROJECT PROGRESS (if available)
=====================
Epic: "Quality Layer"
Issues: 5/8 closed
Current: #23 Python checker (in-progress)
Blocked: None
RECENT DECISIONS (omega, if available)
=====================
- Chose ruff over black for Python formatting (2 days ago)
- TypeScript checker uses project-local eslint first (3 days ago)
RECENT SESSIONS
=====================
abc123: 45% (completed normally)
def456: 92% (handed off — continuation.md exists)
Display the continuation.md contents and ask:
"A previous session left handoff notes. Resume this work?"
If yes, read the notes and continue from where the previous session stopped. If no, acknowledge and start fresh.
NEXT-LEVEL STATUS
=====================
No active specs or sessions.
Start with: /next-level:spec "your task description"
Primary review entrypoint for plans, diffs, commits, subagent results, and pre-submit checks. Selects quick, standard, or deep mode by scale and risk.
Planner-led repo improvement loops with subagents, review gates, Mermaid DAGs, and workflow-aware commits. Use for improve, refactor, audit, or continue-loop requests.
Triage and apply code review feedback with technical verification. Use when a human, reviewer subagent, CI, or adversarial reviewer returns findings or requested changes.
Heavy review mode normally invoked by request-code-review for risky checkpoints, pre-submit stacks, OpenSpec/cflx acceptance, security-sensitive work, or explicit deep review, audit, or red-team requests.
Post-implementation cleanup for recent changes using parallel reuse, quality, and efficiency reviewers. Use after building a feature or fix, before commit, PR, or submit.
End-of-workflow learning loop for skill-guided work. Use at session/checkpoint closeout to classify GitHub/CodeRabbit/human/CI findings, log missed-review patterns, and propose skill improvements only when evidence justifies them.