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ai-coding-agents-state【设计与创意】Designs session and task state for coding-agent runtimes: resume, checkpoints, compaction, cancellation, background and scheduled runs. Use when persisting agent work.
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Use this skill to design or review the durable state of a coding-agent runtime: sessions (IDs, transcripts, resume, checkpoints, compaction) and tasks (types, lifecycle, claiming, cancellation, background work, triggers). Both are host-owned runtime state, so they share one store, one identity model, and one recovery story.
This skill is about runtime state, not product backlog planning. For persistent repo instructions and always-loaded memory files, use ../agents-memory/SKILL.md.
| Question | Read | Outcome |
|---|---|---|
| What belongs in a session model? | references/session-lifecycle-and-resume.md | Session IDs, picker flows, stale-cache reset, resume semantics, store schema and retention |
| How do transcripts recover across worktrees and summaries? | references/transcript-restoration-and-cross-worktree-recovery.md | Restoration boundaries, search, cross-project safeguards, replay rules |
| Which resume path applies (session ID / picker / ACP re-attach / recipe re-seed)? | references/resume-path-decision-tree.md | Decision tree, path comparison, prompt-cache economics in subagent spawning |
| Continue, rewind, clear, compact or subagent at this turn? | references/context-lifecycle-and-branching.md | Context rot zone, rewind over correction, compact-vs-clear, subagent mental test |
| How do forked subagents change cache, isolation, resume and cost? | references/context-forking.md | Named-vs-forked startup, cache-prefix economics, isolation behavior |
| Checkpoints, rewind vs git vs branch, background-session resume, compaction contract | references/session-background-checkpointing-and-compaction.md | Mode rules, coverage blind spots, comparison table, re-injection contract |
| How does Codex split Session / Task / Turn state? | references/openai-codex-session-task-turn-protocol.md | SQ/EQ protocol, response bookmarks, one-active-task invariant, interruption |
| How does Codex persist, resume, fork and cloud-resume sessions? | references/openai-codex-session-persistence.md | Rebuildable index over the rollout log, resume / fork / cloud semantics |
| What task types should the runtime support? | references/task-types-and-lifecycle.md | Task families, statuses, background eligibility, host ownership |
| How do task lists and teammate routing work? | references/task-list-coordination-and-teammate-routing.md | File-watched lists, claiming, blockers, teammate routing, response loops |
| How do Codex cloud tasks and agent graphs behave? | references/openai-codex-cloud-tasks-and-agent-graph.md | Task CLI lifecycle, best-of-N, partial apply states, persisted topology |
| Where is the runtime surface for loops and cyclic/workflow graphs? | references/loop-and-graph-runtime-surfaces.md | Surface matrix, queue-vs-cyclic-graph data model, portable loop contract |
| How do Anthropic-hosted routines (schedule / API / GitHub) behave? | references/claude-code-routines.md | Trigger types, /fire, fresh-session model, cap-drop behaviour |
| How do I trigger agents from webhooks, queues or schedules at scale? | references/webhook-and-queue-triggers.md | SQS / Streams / Kafka / EventBridge, idempotency, dedup, DLQ, rate-limit slots |
| How do I make triggered runs durable across crashes? | references/durable-trigger-integration.md | Temporal / Inngest / Restate / Step Functions, agent-as-activity, sagas, replay |
| Task sizing, scheduled-trigger table, background-subagent semantics | references/task-sizing-triggers-and-background-semantics.md | Sizing gate, routines vs /loop vs desktop tasks, pending-input sub-state |
| Goal-mode loops (act, score, check goal) | references/goal-mode-loops.md | Failure modes, bottlenecks, filesystem state for long runs |
| Goose session re-attach, recipe seeds, typed recipe blueprints, sub-recipes | references/goose-session-and-task-patterns.md | ACP re-attach, capability-narrowed subagents, sub-recipe lifecycle |
| Where do I host the trigger and agent (Vercel, Fly, CF, Render)? | ../software-paas-hosting/references/agent-hosting-matrix.md | Per-shape PaaS stacks and reference architectures |
| Validate a recipe blueprint YAML statically | scripts/recipe_scanner.py | Fail-closed validator: required fields, parameter types, extension risk gates, placeholders |
| Need | Use Instead |
|---|---|
| Persistent repo instructions and shared memory files | ../agents-memory/SKILL.md |
| Remote execution, bridge sessions, TUI and dialogs | ../ai-coding-agents-surfaces/SKILL.md |
| Tool-call permission gating, approval modes, subagent policy inheritance | ../ai-coding-agents-safety-envelope/SKILL.md |
| Multi-agent planning and ownership contracts | ../agents-swarm-orchestration/SKILL.md |
| Loop shapes, termination predicates, convergence detection (owner; references/goal-mode-loops.md is pending a move there) | ../agents-swarm-orchestration/references/loop-orchestration.md |
| Script-held deterministic control flow (Claude Code Workflows) | ../agents-swarm-orchestration/references/scripted-workflows.md |
/clear starts a new resumable session and leaves the old one rewindable.../foundations-distributed-systems/assets/templates/distributed-systems/08-leases-fencing.md.cancelling; after the worker acknowledges or a hard-kill deadline expires, kill the whole process group and cascade to child tasks. The task is terminal only after children are reaped.completed status in one atomic write, or write the result first and flip the status idempotently. A retry after a claim checks for an existing result before re-executing side-effecting work.completed yields "done with no output" or a re-run of finished side-effecting work.Task to Agent) treated as two task families instead of one primitive under an old name.references/.scripts/recipe_scanner.py with tests in scripts/test_recipe_scanner.py (python3 -m unittest test_recipe_scanner from scripts/).data/sources.json holds primary documentation and source references.../agents-memory/SKILL.md, ../ai-coding-agents-surfaces/SKILL.md, ../agents-swarm-orchestration/SKILL.md, ../ai-agents/references/autonomous-loop-patterns.md (when a trigger drives an autonomous loop, not a single run), ../agents-hooks/references/budget-and-loop-hooks.md (budget, iteration-cap, stagnation and kill-switch enforcement for triggered runs).When prior decisions or pitfalls are relevant, consult learnings.consolidated.md if present; use learnings.md only for needed history or as the available fallback. Otherwise skip both.
After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.