Skip to content
cccskills
FreePublished on GitHub

orca-replay

Investigate past agent runs using recorded tool calls, exit codes, and file changes. Replay failures, distinguish observed evidence from inferred causes, and compare models from the same saved point when requested.

OriginalShow the original 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.

See how to install

Good for

  • Find why an agent overwrote a file
  • Identify which step broke a build
  • Replay a previously recorded failure
  • Compare models from the same saved point

Files included (1)

  • SKILL.md4.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

OrcaReplay Run Reader

Coding agents are confidently wrong about their own history. A summary written from memory is missing the tool results, the exit codes, and the files that changed without anyone mentioning them.

This skill enforces one rule: when a question is about something that already happened, read the trace before answering. 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?" or "Which step broke the build?"
  • "Can you reproduce yesterday's failure?"
  • "Would a different model have got this right?"

What This Skill Does

  1. Finds the run — orca_list_runs, newest first, naming the run each fork came from.
  2. Narrows to the cause — orca_graph with to: <event seq> returns only the chain that produced one event. That is the shape of an answer to why; the full timeline (orca_show_run) is the shape of an answer to what.
  3. Separates evidence from inference — every edge is labelled recorded (the recorder watched it happen) or inferred (derived just now from a named rule). "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 collapsing them into one confident sentence is the exact failure this skill exists to prevent.
  4. Reproduces before explaining — orca_replay re-runs the recording offline and reports what could not be reproduced.
  5. Compares models only when asked — orca_compare forks one run onto several models from the same checkpoint, graded by a shell command whose exit code is the verdict.

How to Use

Basic Usage

Why did the last run overwrite src/config.ts?

The agent reads the trace instead of recalling the session, and answers from the tool call that did it.

Does yesterday's failure still reproduce?

Example

User: "Which step broke the build?"

Output:

Step 14 did. From the recording (recorded edge, not inferred):

  14  TOOL   file_editor   {"command":"str_replace","path":".../tsconfig.json"}
  15  SHELL  npm run build  exit 2
  16  FILE   tsconfig.json  modified +1 -1

The edit at 14 removed "strict": true, and the build at 15 exited 2. I replayed the run
(reused=5/5, divergences=0) and it fails the same way.

Tips

  • Pass worktree: true to replay. Without it, replay restores the recorded filesystem over the working tree for the duration of the run, so uncommitted work is absent in the meantime — and stays absent if the replay is interrupted.
  • Check what re-executes before the first replay. Replay serves the model's answers from the trace, but the agent process runs again for real, so its shell commands run again too. A run that only read files and edited the repository is free to replay; one that touched Docker, a database or a remote host is not.
  • Replay is not a sandbox. It blocks provider egress, not the network.
  • A matching replay is not a determinism result. The model is not re-asked, its recorded answers are served back — so replay cannot tell you whether a fresh run would fail the same way. Say so rather than implying it did.
  • reused=3/5 is usually not a failure. Harnesses make calls for themselves — a quota probe, a session-naming request — and replay does not repeat them.
  • No recording means no answer. Say that plainly and offer to start one with orca record claude, rather than falling back to a reconstruction.

Requirements

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. Apache-2.0 — Continuum-AI-Corp/OrcaReplay.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Handle ActiveCampaign contact and marketing tasks through Rube MCP, a connection for agent tools. Find or create contacts, manage tags and subscriptions, enroll contacts in existing automated workflows, and create follow-up tasks.

  • Finding or registering contacts
  • Adding or removing contact tags
  • Subscribing contacts to lists or unsubscribing them
davepoon/buildwithclaude3.7KUpdated Oct 20, 2026

Research competitors’ live Meta and Google ads to identify opening hooks, offers, calls to action, and landing pages. Receive a sourced creative brief and up to three test ideas adapted to your product.

  • Find angles for new ad copy
  • Plan creative tests using competitor ads
  • Choose a landing page message
davepoon/buildwithclaude3.7KUpdated Oct 20, 2026

Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.

The English summary is being prepared. Showing the original description.

davepoon/buildwithclaude3.7KUpdated Oct 20, 2026

Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.

The English summary is being prepared. Showing the original description.

davepoon/buildwithclaude3.7KUpdated Oct 20, 2026

Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.

The English summary is being prepared. Showing the original description.

davepoon/buildwithclaude3.7KUpdated Oct 20, 2026

Checks which private files (.env, keys, secrets) Claude Code and its subagents reached in this project, whether a value came into an agent's context, and which key to change, by running the open source agentwhy CLI over the session records Claude Code already keeps on disk. Use when the user asks whether the agent or a subagent read their .env or other secrets, which private files a session touched, whether a key leaked into the conversation, or which key to rotate. Runs locally and never prints a secret value.

The English summary is being prepared. Showing the original description.

davepoon/buildwithclaude3.7KUpdated Oct 20, 2026

See all skills by davepoon

Report a problem with this skill