Retrospect External Babysitter Run logo

Retrospect External Babysitter Run

OrganizationPopular
a5c-ai
retrospect-external-babysitter-run

For a repository in the babysitter-users catalog, locate its babysitter processes and any committed runs (.a5c/runs/<runId>/) and perform a retrospective on a chosen run -- what went well, what failed, process suggestions, quality of effect design, breakpoint patterns -- mirroring the /babysitter:retrospect workflow but applied to an external repo. Invoke when asked to "retrospect on repo X's run", "analyze how someone else used babysitter", or "review an external babysitter run".

Overview

Publishera5c-ai
Repositorybabysitter
Skill nameretrospect-external-babysitter-run
Stars
1.8K
Forks
112
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by a5c-ai on GitHub. Read the source before you install it.

Installation

Install the Retrospect External Babysitter Run AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/a5c-ai/babysitter/tree/main/.claude/skills/retrospect-external-babysitter-run
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/a5c-ai/babysitter.git /tmp/babysitter
mkdir -p .claude/skills
cp -r /tmp/babysitter/.claude/skills/retrospect-external-babysitter-run .claude/skills/retrospect-external-babysitter-run
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Retrospect External Babysitter Run in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Retrospect External Babysitter Run on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Retrospect External Babysitter Run is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Retrospect External Babysitter Run

Analyse a babysitter run that lives in an external public repository, using the same lens as the in-repo /babysitter:retrospect command. Produce a written retrospective with concrete suggestions for the process author (or, if the insight generalizes, for the babysitter project itself).

When to use

  • User names an external repo and asks for a retrospective.
  • User asks "find a babysitter run to retrospect on" (combine with the catalog-babysitter-users skill to pick one).
  • User asks "how are other people using babysitter processes? What do they get wrong?".

Prerequisites

  • gh CLI authenticated.
  • docs/repo-with-babysitter-processes.md exists (if not, run the catalog-babysitter-users skill first).
  • A workspace directory where external repos can be shallow-cloned (default: /tmp/babysitter-retrospect/ or .a5c/tmp/external-runs/).

Phase 1 -- Target selection

  1. Read docs/repo-with-babysitter-processes.md and list Active repos with stars + description. If the user already named a repo, skip to step 3.
  2. Ask the user which repo to retrospect on (use AskUserQuestion in interactive mode; if non-interactive, pick the highest-starred Active repo that wasn't retrospected in the last 30 days -- track via docs/retrospectives/<owner>-<name>/log.md).
  3. Confirm the target with the user before cloning.

Phase 2 -- Discover processes and runs

Shallow clone the target:

bash
mkdir -p .a5c/tmp/external-runs
cd .a5c/tmp/external-runs
gh repo clone <owner>/<name> -- --depth 50 --single-branch
cd <name>

Locate:

  • Process files: files importing defineTask from @a5c-ai/babysitter-sdk. Use Grep: grep -rl "from '@a5c-ai/babysitter-sdk'" -- . --include='*.js' --include='*.ts'.
  • Committed runs: .a5c/runs/<runId>/ directories. Many repos gitignore .a5c/runs/ entirely -- that's fine; note it and proceed with process-only retrospective. When runs ARE committed, look for run.json, journal/, tasks/, state/output.json.
  • Historical runs via git log: git log --all --diff-filter=A --name-only -- '.a5c/runs/' surfaces runs that existed at some commit even if later cleaned up. Check out the commit that introduced the run if you want the journal content.

Summarize to the user:

  • N process files found, by top-level directory
  • M run directories present in HEAD; P additional historical runs reachable via git history
  • Which runs completed vs failed (grep RUN_COMPLETED / RUN_FAILED in the journal)

Phase 3 -- Pick a run to retrospect

If multiple runs exist, ask the user (interactive) or default (non-interactive) to:

  • The most recent failed run (highest signal for process improvement), OR
  • If no failures, the most recent completed run.

If no runs are committed at all, switch to a process-only retrospective: analyse the process file(s) for quality issues without run evidence. Mark the output clearly as process-only.

Phase 4 -- Load the run

Inspect, in order:

  • .a5c/runs/<runId>/run.json -- processId, entrypoint, prompt, createdAt
  • .a5c/runs/<runId>/inputs.json -- user intent
  • .a5c/runs/<runId>/journal/*.json -- event stream (RUN_CREATED, EFFECT_REQUESTED, EFFECT_RESOLVED, RUN_COMPLETED / RUN_FAILED). Read every journal entry; it is the authoritative record.
  • .a5c/runs/<runId>/tasks/<effectId>/task.json + result.json -- per-effect definition and result
  • .a5c/runs/<runId>/state/output.json (if present) -- final output
  • The process file referenced by run.json.entrypoint -- cross-reference against the journal to see what the author intended vs what happened.

Phase 5 -- Retrospective analysis

Mirror the in-repo /babysitter:retrospect workflow. Produce notes under each heading:

5.1 Outcome

  • Success / partial success / failure.
  • Total iterations, duration, distinct effect count, retry count.
  • Final output quality (from state/output.json shape + content).

5.2 What went well

  • Effects that resolved on first try.
  • Process sections with clear inputs/outputs and no re-runs.
  • Useful breakpoints that caught real issues before they propagated.

5.3 What went poorly

  • Effects that were re-dispatched (same invocationKey or similar taskId appearing repeatedly).
  • Long gaps between EFFECT_REQUESTED and EFFECT_RESOLVED (external bottlenecks).
  • Breakpoints that looped (approval -> reject -> retry -> reject).
  • Tasks that crashed and what the error category was (Configuration / Validation / Runtime / External / Internal).
  • Any RUN_FAILED: trace the last few events and the thrown error.

5.4 Process-quality review

Evaluate the process file itself against these criteria:

  • Determinism: does every effect have stable invocation keys (processId:stepId:taskId)? Any non-deterministic branching based on wall-clock time, random, or unpinned env vars?
  • Effect granularity: are tasks too coarse (one huge agent task vs several narrower ones) or too fine (dozens of tiny tasks)?
  • Idempotency: can the process be re-run safely? Does it use ctx.task() for all side effects, or does it write files outside a task?
  • Breakpoint discipline: are breakpoints used to gate irreversible actions? Do they follow the robust rejection pattern (loop with feedback)?
  • Error surfacing: does the process throw with useful context, or swallow errors?
  • Labels: are task labels meaningful and consistent (enables filtering / observability)?
  • Re-use: could any section be replaced by a shared component from library/processes/shared/?

5.5 Suggestions

Concrete, actionable suggestions in three buckets:

  • For the run (if still in progress): what to retry, rollback, or fix first.
  • For the process (always): specific edits to the process file -- split this task, add this breakpoint, move that side-effect inside a task, use stableKey here.

Can it be generalized into a reusable pattern or library process in the processes library? If so, suggest that too. (also using /babysitter:contrib library ...)

  • For babysitter upstream (when the insight generalizes): missing primitives, confusing SDK behavior, documentation gaps worth filing via /babysitter:contrib.

Every suggestion must cite evidence -- a journal event, a file path, a line range.

Phase 6 -- Write the retrospective

Write to docs/retrospectives/<owner>-<name>/<runId-or-process-name>.md with this structure:

markdown
# Retrospective: <owner>/<name> -- <runId or process name>

Date: YYYY-MM-DD
Source commit: <sha>
Process: <relative path>
Run: <runId or "process-only">
Outcome: <success | failure | process-only>

## Context
<1-3 sentences on what the process is trying to do and the user intent from inputs.json>

## Timeline
<bullet timeline of key journal events with timestamps and durations>

## What went well
...

## What went poorly
...

## Process-quality review
...

## Suggestions
### For the run
### For the process
### For babysitter upstream

## Evidence
<links to specific journal event files, task.json files, line-anchored process file refs>

Also append a one-line entry to docs/retrospectives/<owner>-<name>/log.md with the date, runId, and outcome, so we don't re-retrospect the same run.

Phase 7 -- Cleanup and callbacks

  • Leave the shallow clone under .a5c/tmp/external-runs/ in place (it's cheap). If disk pressure, note this to the user; do NOT auto-delete.
  • Suggest the user use /babysitter:contrib for any upstream-worthy insight:
    • Process/skill improvement idea -> /babysitter:contrib library contribution: [description]
    • SDK/CLI bug or missing primitive -> /babysitter:contrib bug report: [description]
    • Documentation gap that tripped the external author -> /babysitter:contrib documentation question: [what was unclear]
  • If the process author is findable (repo owner, git author of the process file), suggest opening an issue on their repo with a pointer to the retrospective document.

Notes

  • Honour the target repo's LICENSE when quoting code in the retrospective. Short excerpts for analysis are fair use; do not wholesale copy process files into this repo.
  • Never execute the external process -- retrospectives are read-only analysis.
  • If the run journal is very large (>500 events), sample: first 20, last 20, plus every EFFECT that transitioned to resolved or failed.

Frequently asked questions

What does the Retrospect External Babysitter Run AI skill do?

For a repository in the babysitter-users catalog, locate its babysitter processes and any committed runs (.a5c/runs/<runId>/) and perform a retrospective on a chosen run -- what went well, what failed, process suggestions, quality of effect design, breakpoint patterns -- mirroring the /babysitter:retrospect workflow but applied to an external repo. Invoke when asked to "retrospect on repo X's run", "analyze how someone else used babysitter", or "review an external babysitter run".

Why use Retrospect External Babysitter Run on TypingMind?

Because you install it once and use it with any model. Retrospect External Babysitter Run is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Retrospect External Babysitter Run in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/a5c-ai/babysitter/tree/main/.claude/skills/retrospect-external-babysitter-run. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Retrospect External Babysitter Run?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Retrospect External Babysitter Run?

As many as you like. As long as a model supports skills, you can use Retrospect External Babysitter Run with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Retrospect External Babysitter Run AI skill free?

Yes. It is published on GitHub by a5c-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇