skills/checkpoint-summary/SKILL.md
Summarize artifacts produced by liza agents for human checkpoint review
npx skillsauth add liza-mas/liza checkpoint-summaryInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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After agents complete a planning or writing phase (epic planning, story writing, spec generation), summarize their output so a human can efficiently review what was decided, what remains open, and where their attention is needed.
This skill answers: "What did the agents produce, what did they decide, and what do I need to weigh in on?"
The agents already did the work — planning, reviewing, approving. This skill reads their outputs and distills them into a checkpoint summary that respects the human's time.
Distinct from spec-review: spec-review audits spec quality. This skill summarizes what was already reviewed and approved, surfacing only what needs human judgment.
Use this skill when:
The single entry point is .liza/state.yaml — the source of truth for all Liza state.
From state.yaml, the skill reads:
goal.spec_ref: the upstream source document the agents worked fromtasks[]: each task with its scope, status, output capabilities, approvals, and historytasks[].plan_ref / tasks[].arch_ref — task-level planning and architecture artifactstasks[].output[].plan_ref / tasks[].output[].arch_ref — output-entry artifactstasks[].spec_ref — task-level spec (may differ from goal.spec_ref)tasks[].approvals[]: review verdicts with provider/diversity context (canonical);
fall back to tasks[].approved_by if approvals[] is absenttasks[].history[]: full event timeline (claimed, checkpoint, submitted, approved, merged)sprint.status and sprint.checkpoint_trigger: why the checkpoint was triggeredNo other discovery is needed. Read every artifact file referenced by the ref fields above.
Read the upstream source (goal.spec_ref). Everything else is in the state file itself.
Read .liza/state.yaml to understand the full pipeline state: goal, tasks, agents,
sprint status, and checkpoint trigger.
Read the upstream source (goal.spec_ref) to understand what the agents were working
from — entities, decisions, constraints, interactions, scope boundaries.
Read all produced artifacts referenced by task-level refs (plan_ref, arch_ref)
and output-entry refs (output[].plan_ref, output[].arch_ref). Skip entries where
no ref field points to a file. For each artifact read:
tasks[].approvals[]; fall back to approved_by)tasks[].history[] (rejections, re-reviews, anomalies)From the artifacts and agent outputs, extract three categories:
Choices the agents made that the upstream source left open. For each:
Items that remain unresolved after the agent work:
Implementation risks the artifacts create or carry forward:
Not everything needs human attention. Classify each item:
| Priority | Meaning | Action needed | |----------|---------|---------------| | Decide | Human must make a choice before next phase starts | Present the decision with options | | Confirm | Agents made a reasonable choice — human should validate | Present the decision, default is accept | | Note | Worth knowing, no action needed | Include in summary, don't interrupt for it |
Prioritization heuristics:
Present in this format. Decide items first, then Confirm, then Notes.
# Checkpoint Summary: [Phase Name]
## Status
| Artifact | Scope | Verdict |
|----------|-------|---------|
| [name] | [one-line scope] | [Approved / Rejected / Conditional] |
> N decisions needing input · M items to confirm · K notes
## Decisions Needing Human Input
Items where the human must choose before the next phase starts.
### [Decision Title]
- **Context:** [What the upstream says or doesn't say]
- **Agent decision:** [What the agents chose]
- **Why it needs you:** [What makes this non-obvious — departure from upstream,
irreversible, low confidence, or gap]
- **Options:** [If applicable — confirm agent choice, override with X, or defer]
## Decisions to Confirm
Agents made reasonable choices. Confirm or override.
### [Decision Title]
- **Agent decision:** [What was chosen]
- **Rationale:** [Why it's reasonable]
- **Override if:** [When the human might want something different]
## Open Points
Unresolved items carried forward.
### [Item Title]
- **What's open:** [Description]
- **Impact:** [What's affected if unresolved]
- **Where it surfaces:** [Which artifacts reference this]
## Risks
### [Risk Title]
- **What could go wrong:** [Description]
- **Which artifacts:** [Where this risk lives]
- **Mitigation:** [If any exists in the plans, or "none specified"]
## Notes
Brief items worth knowing but not requiring action.
- [Item]: [One-line description]
| Skill | Relationship | |-------|-------------| | epic-writing | Upstream producer. Summarize epic plans at the planning checkpoint. | | user-story-writing | Upstream producer. Summarize stories at the story-writing checkpoint. | | spec-review | Complementary. spec-review finds spec defects; this summarizes agent decisions. Different purposes, can run on the same artifacts. |
Pairing mode: Present the summary interactively. Walk through "Decide" items one at a time, collecting the human's decision before moving to the next. For "Confirm" items, present as a batch — the human can scan and override selectively. End with a count of decisions made and items still open.
Liza mode: Checkpoint Summary operates autonomously within task scope. Write the
report to the worktree (e.g. docs/checkpoint-summary.md) and submit for review.
If any "Decide" items exist, mark BLOCKED with blocked_reason summarizing the
decisions needed and blocked_questions listing each one — the human must resolve
them before the next phase starts. If only "Confirm" and "Note" items exist, submit
normally.
| Pairing Prompt | Liza Behavior | |----------------|---------------| | "N decisions need your input — walk through them?" | Mark BLOCKED; report in worktree | | "Agents made N decisions — all look reasonable. Confirm?" | Submit for review; report in worktree | | "No open points — ready for next phase" | Submit for review; report in worktree |
development
Coordinate Pairing-mode doer/reviewer sessions through a Markdown blackboard. Use when the user invokes /adversarial-pairing with role and blackboard-path arguments or asks multiple pairing agents to coordinate plan review, implementation, staged code review, and follow-up review rounds without Liza multi-agent mode.
data-ai
Analyze Liza agents logs
development
Code Review Protocol
tools
Analyze Liza `.liza/agent-prompts/` and `.liza/agent-outputs/` from a context-engineering perspective: prompt payload shape, context budget use, cacheability, duplicated or missing context, instruction hierarchy, tool-output pressure, role-specific context fit, and prompt-output feedback loops. Use when diagnosing agent context bloat, prompt drift, poor agent handoffs, repeated misunderstandings, excessive tool output, or whether Liza agents received the right information at the right time.