Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add amadeus-dlc/amadeus --skill amadeus-replaygit clone --depth 1 https://github.com/amadeus-dlc/amadeusWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/amadeus-dlc/amadeus/amadeus-replay)<a href="https://agentmods.dev/skills/amadeus-dlc/amadeus/amadeus-replay"><img src="https://agentmods.dev/badge/skills/amadeus-dlc/amadeus/amadeus-replay/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/amadeus-dlc/amadeus/amadeus-replay"><img src="https://agentmods.dev/badge/skills/amadeus-dlc/amadeus/amadeus-replay.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.00905 |
| Opus 5 | $0.00036 | $0.00452 |
| Sonnet 5 | $0.00014 | $0.00181 |
| Haiku 4.5 | $0.00007 | $0.00090 |
Grade A, and why
amadeus-replay scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 {{HARNESS_DIR}}/tools/amadeus-runtime.ts summary --json
The narrative prose (what happened, key decisions, reasoning) is yours
to synthesise from audit.md and the 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 {{HARNESS_DIR}}/tools/amadeus-runtime.ts summary --json.
If it exits non-zero (no runtime-graph.json yet), print:
No session data yet — start a workflow with /amadeus before running
/amadeus-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
amadeus-docs/audit.md— the full event trail (for ordering and the reasoning behind each decision).amadeus-docs/amadeus-state.md— the active-stage cursor.- The artefacts under
amadeus-docs/<phase>/<stage>/— for what each stage produced.
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 audit.md}
**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}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 115 lines · 72 tokens per session scan A a9f398aac2bd
amadeus-replay is a skill published in the GitHub repository amadeus-dlc/amadeus (8 stars, last pushed 20d ago), licensed Apache-2.0. It adds 72 tokens to every session and 905 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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