long-running-agent-workflow

# Long-Running Agent (LRA) Workflow Load this skill when you are about to work on a **large project that will span multiple sessions / context windows** and you need continuity, atomic handoffs, and recovery from broken states. The skill gives you a tiny CLI (`scripts/lra_cli.py`) that maintains a `.lra/` directory: a machine-readable `feature-list.json` (atomic features with acceptance criteria and status) and a human/agent-readable `progress.txt` (session log). The protocol turns "one big vague task" into a sequence of small, fully-tested, check-pointed features. --- ## Overview — the problem AI agents working across many context windows hit three failure modes: - **Context amnesia** — each new session has no memory of prior work. - **One-shot tendency** — trying to do too much at once, leaving half-done features. - **Incomplete features** — work spans sessions with no clear acceptance gate, so "done" is never verified. LRA fixes this with: structured init, one atomic feature per session, an explicit test gate before `done`, and a checkpoint after every feature so the next session can recover. --- ## When to use - Long, multi-session projects (hours/days, many context windows). - Any task where you might lose context between runs. - Triggers: `lra`, `checkpoint`, `feature list`, `long running`, `продолжи работу над проектом`, `долгая сессия`, `план фич`, `статус проекта`. If the task is small and finishes in one session, you do **not** need this skill — jus...