Propose development tasks and generate STV specs and trace artifacts from git history.
npx skills add https://github.com/2lab-ai/oh-my-claude --skill plan-new-task
Please help me install this Agent Skill. Skill: plan-new-task Source: https://github.com/2lab-ai/oh-my-claude/tree/main/plugins/stv/skills/plan-new-task Command: npx skills add https://github.com/2lab-ai/oh-my-claude --skill plan-new-task
When a repository's visible backlog is empty or contains only trivial leftovers, teams can stall without clear next steps; this Skill reviews completed work and project context to propose meaningful follow-up features and produce structured STV spec and trace artifacts ready for execution.
Ask the skill to propose next work and generate an STV spec and trace for the best candidate feature based on completed docs and git history.
High-intent search queries and answers about installing and using this skill.
To propose next features when the backlog is empty, analyze completed trace files and git history to identify natural follow-ups. This skill reviews docs/*/trace.md and recent commits to surface prioritized, sized feature candidates ready for execution.
Yes, you can automatically generate STV specs and trace artifacts for new tasks. After analyzing project context and presenting 2-4 candidate features, the skill invokes stv:new-task to create structured spec and trace outputs for your selected idea.
The best way to identify follow-up features from completed work is to review completed specs, trace artifacts, and recent git history. This approach surfaces natural follow-up features tied to recent development or visible project gaps.
Yes, this feature ideation tool works directly with existing STV workflows. It applies to repositories using docs/*/trace.md, reads implementation status, and invokes stv:new-task to seamlessly generate new structured spec and trace artifacts.
You estimate task size tiers for backlog candidates by analyzing recent commits and project context alongside completed specs. The skill evaluates this historical data to assign size estimates to the 2-4 proposed candidate features.
During backlog analysis, tiny carryover items are surfaced alongside the confirmation that no meaningful unfinished scenarios remain. The skill scans trace files and implementation status to expose these trivial leftovers before proposing new features.
Elad73Reads project status and git state to produce a concise session briefing.
2lab-aiDecompose vague requirements into STV-based specs and trace artifacts.
2lab-aiGenerate a formal feature spec with decision gates and changelog.
BrennonTWilliamsCross-reference git commits and completed issues to detect documentation gaps.
