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1 change: 1 addition & 0 deletions docs/README.skills.md
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| [oo-component-documentation](../skills/oo-component-documentation/SKILL.md)<br />`gh skills install github/awesome-copilot oo-component-documentation` | Create or update standardized object-oriented component documentation using a shared template plus mode-specific guidance for new and existing docs. | `assets/documentation-template.md`<br />`references/create-mode.md`<br />`references/update-mode.md` |
| [openapi-to-application-code](../skills/openapi-to-application-code/SKILL.md)<br />`gh skills install github/awesome-copilot openapi-to-application-code` | Generate a complete, production-ready application from an OpenAPI specification | None |
| [optimize-simplicite-logs](../skills/optimize-simplicite-logs/SKILL.md)<br />`gh skills install github/awesome-copilot optimize-simplicite-logs` | capability to parse Simplicité logs from a raw `.txt` file, filter fields to reduce noise, and output the result as structured JSON. | `scripts/SimpliciteLog2Json.ps1`<br />`scripts/simplicite-log2json.py` |
| [orca-replay](../skills/orca-replay/SKILL.md)<br />`gh skills install github/awesome-copilot orca-replay` | Answers questions about a past agent run from its recording rather than from memory, and replays or forks that run. Use when asked why an earlier run did something, or to reproduce a failure. | None |
| [pdftk-server](../skills/pdftk-server/SKILL.md)<br />`gh skills install github/awesome-copilot pdftk-server` | Skill for using the command-line tool pdftk (PDFtk Server) for working with PDF files. Use when asked to merge PDFs, split PDFs, rotate pages, encrypt or decrypt PDFs, fill PDF forms, apply watermarks, stamp overlays, extract metadata, burst documents into pages, repair corrupted PDFs, attach or extract files, or perform any PDF manipulation from the command line. | `references/download.md`<br />`references/pdftk-cli-examples.md`<br />`references/pdftk-man-page.md`<br />`references/pdftk-server-license.md`<br />`references/third-party-materials.md` |
| [penpot-uiux-design](../skills/penpot-uiux-design/SKILL.md)<br />`gh skills install github/awesome-copilot penpot-uiux-design` | Comprehensive guide for creating professional UI/UX designs in Penpot using MCP tools. Use this skill when: (1) Creating new UI/UX designs for web, mobile, or desktop applications, (2) Building design systems with components and tokens, (3) Designing dashboards, forms, navigation, or landing pages, (4) Applying accessibility standards and best practices, (5) Following platform guidelines (iOS, Android, Material Design), (6) Reviewing or improving existing Penpot designs for usability. Triggers: "design a UI", "create interface", "build layout", "design dashboard", "create form", "design landing page", "make it accessible", "design system", "component library". | `references/accessibility.md`<br />`references/component-patterns.md`<br />`references/platform-guidelines.md`<br />`references/setup-troubleshooting.md` |
| [performance-review-writer](../skills/performance-review-writer/SKILL.md)<br />`gh skills install github/awesome-copilot performance-review-writer` | Draft performance reviews, self-assessments, peer reviews, and upward feedback in your own voice. Analyzes your contributions, emails, and meeting history via WorkIQ, then produces honest, impact-focused drafts using the STAR format. USE FOR: write my performance review, draft self-assessment, peer review, 360 feedback, annual review, mid-year review, upward feedback, write review for colleague, performance appraisal. | None |
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---
name: orca-replay
description: Answers questions about a past agent run from its recording rather than from memory, and replays or forks that run. Use when asked why an earlier run did something, or to reproduce a failure.
license: Apache-2.0
compatibility: Requires the `orcareplay` npm package (Node 20+) with its MCP server registered as `orca`, and at least one recorded run in the project's .orca/runs directory.
metadata:
author: Continuum-AI-Corp
version: "0.1"
homepage: https://github.com/Continuum-AI-Corp/OrcaReplay
---

# Reading a recorded agent run

A recording is evidence. Your memory of a session is not, and neither is a transcript you were
handed — both are missing the tool results, the exit codes, and the files that changed without
anyone mentioning it.

**The rule: when a question is about something that already happened, read the trace before you
answer.** Do not reconstruct it. If a recording exists, guessing is the wrong move even when the
guess would have been right.

## When to Use This Skill

- "Why did you delete/overwrite/move X?"
- "What changed this file?" / "Which step broke the build?"
- "Can you reproduce yesterday's failure?"
- "Does this still reproduce?" (see the limit on that in step 4 — replay cannot tell you
whether a *fresh* run would fail again)
- "Would a different model have got this right?"

## Workflow

### 1. Find the run

`orca_list_runs` — newest first, and it names the run each fork came from. Skip this only when the
user clearly means the most recent one; every other tool defaults to `run: "last"`.

### 2. Narrow to the chain that produced the thing being asked about

`orca_show_run` gives the whole timeline: model turns with token counts and stop reasons, tool
calls with arguments and results, shell commands with exit codes, and every file the run changed.
Good for orientation, long for a specific question.

`orca_graph` is usually the better tool. It returns causal edges — which event produced which. Pass
`to: <event seq>` to get **only** the chain that produced one event. That is the shape of an answer
to "why did this happen", where the full timeline is the shape of an answer to "what happened".

### 3. Report `recorded` and `inferred` differently

Every edge from `orca_graph` is labelled:

- **`recorded`** — the recorder watched it happen and wrote it into the trace.
- **`inferred`** — derived just now from a rule the edge names. The trace does not vouch for it.

Carry that distinction into your answer. "The trace shows the `rm` at step 14 removed it" and "this
looks like the `rm` at step 14, going by timing" are different claims, and flattening them into one
confident sentence is the specific failure this tool exists to prevent. Name the rule when you lean
on an inferred edge.

### 4. Reproduce it before explaining it

`orca_replay` re-runs the recording and reports what could not be reproduced — divergences, and
requests the recording could not serve.

**What "offline" covers, and what it does not.** Every model response comes from the trace and the
proxy forwards nothing upstream, so no provider is contacted and no tokens are spent. An unmatched
request halts the replay rather than falling through to the network, unless `--loose` was asked for.

That covers the model traffic. It does not cover the agent's own subprocesses: unless the recording
used `--tls-intercept` — in which case replay re-establishes interception for the hosts it recorded
— a `curl`, `npm install`, `git push` or database call inside a recorded shell command goes
straight out. Replay is not a sandbox; only a network-isolated container makes it one.

**What a matching replay proves, and what it does not.** It shows the recorded decisions reproduce
against today's environment. It cannot show the failure is deterministic, because the model is not
being asked again — the same recorded responses are served back. If the user wants to know whether a
fresh run would fail the same way, say that replay cannot answer it; that needs real runs.

**Replay re-executes the agent, not just its model traffic.** The recorded model responses are
served from the trace, but the agent process runs again for real — so every shell command it issued
runs again too. `worktree: true` isolates repository files and nothing else. Anything the run
touched outside the tree — `/tmp`, Docker, a local database, a package manager, another host — is
mutated a second time.

**So check before the first replay of a run, not after.** Read its shell commands with
`orca_show_run` and tell the user what will re-execute. If any of it reached outside the working
tree, get approval for that specifically or replay inside a container; do not treat the earlier
`worktree` answer as covering it. A run that only read files and edited the repository is free and
repeatable, and worth replaying before committing to any explanation.

**Pass `worktree: true`.** It replays into a scratch copy and leaves the working tree alone.

Without it, replay is destructive for as long as it runs: it restores the recorded filesystem over
the working tree and puts the tree back when the replay ends. Uncommitted work is absent in the
meantime, and stays absent if the replay is interrupted before it can restore. Run an in-place
replay only when the user has been told that and has agreed to it. "They do not appear to be
typing" is not consent.

A replay reporting `reused=3/5` on an interactive recording is not a partial failure. Harnesses make
calls for themselves — a quota probe, a session-naming request — and a replay does not repeat them.

### 5. Only then consider comparing models

`orca_compare` forks one run onto several models from the same checkpoint: same files, same
conversation prefix, so the model is the only variable. Pick the fork point with `orca_checkpoints`
and pass it as `from`.

Grade with `verify` — a shell command whose exit code is the verdict. Use something the repository
already declares (`"npm test"`, `"npm run typecheck"`) or an explicitly local binary
(`"./node_modules/.bin/tsc --noEmit"`), not `npx <tool>`: with no local install, npx runs whatever
the registry has under that name, and `npx tsc` resolves a package deprecated in 2016 that is not
TypeScript.

**`orca_compare` uploads the recording to other people's models, and spends real money doing it.**
Each model named receives the same files and conversation prefix the original run had — so whatever
that run touched (source, prompts, configuration, anything a credential was pasted into) is sent to
every provider behind those model ids.

**And each fork is a live agent, not a replay.** From the fork point onward the model is really
being asked, and whatever it decides to do, it does — its shell commands execute for real, and so
does the `verify` command you pass. Each fork gets its own worktree, so repository files are
isolated per model; nothing outside the tree is. A fork can also take actions the original run never
took, because it is a different model making fresh decisions.

So the approval has three parts, and they are not the same question:

1. **Disclosure** — what context is uploaded, and to which providers. Approving a bill is not
approving a disclosure, and the two need separate answers when the recording is from a private
codebase. `orca scrub` is for when the comparison is worth running but the trace is not safe to
send as-is.
2. **Side effects** — what the recorded run did outside its worktree, since each fork may repeat it
and may go further. Same check as step 4, `orca_show_run`, and the same answer if it reached
Docker, a database, a deployment or another host: get approval for that specifically, or run the
comparison in an isolated environment.
3. **Cost** — how many models times how many forks.

Never run it to satisfy curiosity the user did not express.

## If there is no recording yet

Say so plainly rather than falling back to guessing, and offer to start one.

If `orca` is already installed:

```console
orca record claude # or codex, opencode, openclaw, grok
```

If it is not, ask before installing it — a global install changes the user's machine, and that is
their call, not a detail of your task:

```console
npm i -g orcareplay # ask first
```

`orca record <agent>` runs the agent unmodified behind a local proxy. Nothing about the agent
changes; two environment variables get set. Recording a session now is what makes the next "why did
it do that" answerable.

For a run started with a prompt in argv — `orca record claude -- -p "…"` — the replay is exact. A
session someone typed into replays approximately, because the prompts were never on the wire and
are recovered from the harness's own transcript; `orca replay` says which is which rather than
papering over it.

## Sharing a run with someone else

`orca export last -o run.html` writes one self-contained file. A trace holds whatever the run held,
so run `orca scrub` before sending one anywhere.

Scrubbing is best-effort, not a guarantee. It matches known key shapes and high-entropy strings; it
cannot know that a particular internal hostname, customer name, or unreleased feature is
confidential to this user. So scrub, then have the user look at what is actually going out, and get
their agreement — do not describe a scrubbed trace as safe on the strength of the scrubber alone.

## Limitations

- **It only sees what was recorded.** Runs started without `orca record` leave no trace, and
nothing here recovers them. The answer to "why did it do that" in an unrecorded session is
honestly "there is no recording", not a reconstruction.
- **A typed session replays approximately, not exactly.** Prompts entered at a terminal were never
on the wire; orca recovers them from the harness's own transcript. Only a run started with the
prompt in argv (`orca record claude -- -p "…"`) replays byte-for-byte.
- **Some turns are not repeated.** A harness makes calls for itself — a quota probe, a
session-naming request — and a replay steps over them. Tools that need a person
(`AskUserQuestion`, plan mode) are absent when the same agent runs without one, which can make a
replayed request differ from the recorded one by enough to halt.
- **`inferred` edges are not evidence.** They are derived from a named rule at query time. Treat
them as a reading of the trace, never as something the recorder witnessed.
- **Not every harness is recordable.** Agents that read no base-URL variable and pin their own
origin need `--tls-intercept`, and some cannot be reached at all. A recording that came back
empty means the harness was not captured, not that nothing happened.
- **Replay is not a time machine, and not a sandbox.** It reproduces the agent's side of the run
against today's world. External state the run depended on — a database row, a remote branch, the
clock — is whatever it is now, and the run's own shell commands reach it for real.
- **A matching replay is not a determinism result.** The model is not re-asked; its recorded
responses are served back. Whether a fresh run would fail the same way is a different question
that replay cannot answer.

## Tools

| tool | arguments | notes |
|---|---|---|
| `orca_list_runs` | — | newest first, names the parent of each fork |
| `orca_show_run` | `run` | the full timeline |
| `orca_checkpoints` | `run` | where a fork can start |
| `orca_graph` | `run`, `to` | causal edges; `to` narrows to one chain |
| `orca_replay` | `run`, `worktree` | offline, free, repeatable |
| `orca_compare` | `run`, `models`*, `from`, `verify` | **spends real tokens** |

`run` accepts a run id or `"last"`, and defaults to `"last"`. Replay traces are skipped when
resolving `"last"`, so it means the newest run you actually recorded.