| name | aidlc-replay |
| description | Print a structured session narrative for stakeholders who weren't in the room. Numbers (stage counts, phase rollup, duration) come from `aidlc-runtime.ts summary`; prose comes from the audit trail and artefacts. Renders to the terminal only — writes no file, never mutates workflow state, never emits audit events.
|
| argument-hint | |
| user-invocable | true |
| classification | read-only |
AI-DLC Session Replay
Purpose
Turn a workflow's audit trail and artefacts into a readable story: what
was decided, in what order, and why. For async review, for stakeholders
who weren't present, or as a post-session record. Not a raw log dump.
Classification
Read-only. This skill renders the narrative to the terminal and writes
no file. It never advances the workflow stage pointer and never
emits an audit event.
The counting rule
All counts and aggregates — number of stages, per-phase breakdown,
duration, approved/failed/pending tallies, learnings captured — come
from the tool, not from eyeballing files:
bun .kiro/tools/aidlc-runtime.ts summary --json
The narrative prose (what happened, key decisions, reasoning) is yours
to synthesise from the active record's audit shards and artefacts. The
skeleton numbers are the tool's. Never hand-count stages or artefacts
when the tool already reports the figure.
Steps
Step 1: Read the aggregates
Run bun .kiro/tools/aidlc-runtime.ts summary --json.
If it exits non-zero (no runtime-graph.json yet), print:
No session data yet — start a workflow with /aidlc before running
/aidlc-replay.
and STOP. Otherwise keep the parsed JSON; you'll cite its fields for
every number in the report.
Step 2: Read the narrative sources
Resolve <active-space> from aidlc/active-space (default default) and
<active-intent> from
aidlc/spaces/<active-space>/intents/active-intent. The active workflow
record is
aidlc/spaces/<active-space>/intents/<active-intent>.
- Every
.md shard under <record>/audit/ — the full event trail; order
events by their Timestamp fields for the narrative.
<record>/aidlc-state.md — the active-stage cursor.- The artefacts recursively under
<record>/<phase>/ — what each stage
produced, including per-unit Construction outputs.
These are your sources for prose. Do not derive counts from them when
Step 1's JSON already carries the count.
Step 3: Render the replay
Print the narrative to the terminal in this shape (write no file):
# Session Replay
**Workflow**: {summary.workflow_id}
**Scope**: {summary.scope}
**Duration**: {summary.duration_minutes} min (or "in progress")
**Stages**: {summary.stages.approved} approved / {summary.stages.total} total
## Executive Summary
{3-5 sentences: what was built or decided, key choices, constraints, outcome}
## Timeline
{For each phase in summary.by_phase, in workflow order:}
### {Phase} Phase — {by_phase[phase].approved}/{by_phase[phase].total} stages approved
#### {Stage Name}
**What happened**: {1-2 sentences from the audit trail}
**Key decisions**: {bullets, with reasoning drawn from the audit shards}
**Artefacts produced**: {list with one-line descriptions}
{...repeat per stage that executed...}
## Decisions Register Summary
{Table: decision | alternatives considered | chosen option | rationale}
## Learnings Captured
From orchestrator: {summary.learnings.from_orchestrator}
From user additions: {summary.learnings.from_user_addition}
{Then narrate the notable ones from the stage memory.md diaries.}
## What's Next
{Outstanding open threads from the last audit entries / open questions}
Step 4: Offer adjustments
You may offer: "Want a different tone (more technical / more executive)
or an added section?" — re-render to the terminal if asked. Do not loop
on it; one offer is enough. If the user wants the replay saved, point
them at /aidlc-outcomes-pack (the skill that writes a file) rather
than writing one here.
