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Factory Lookback

OrganizationPopular
BuilderIO
factory-lookback

Experimental workflow for auditing recurring feedback, telemetry, errors, and brittle delivery paths to find systemic fixes. Use for periodic or requested cross-source lookbacks, not routine single-item intake.

Overview

PublisherBuilderIO
Repositoryskills
Skill namefactory-lookback
Stars
4.6K
Forks
231
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 BuilderIO on GitHub. Read the source before you install it.

Installation

Install the Factory Lookback 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/BuilderIO/skills/tree/main/skills/factory-lookback
  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/BuilderIO/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/factory-lookback .claude/skills/factory-lookback
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Factory Lookback 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 Factory Lookback 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 Factory Lookback 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.

Factory Lookback

Read .agent-factory/config.yaml and apply the optional skill_prompts.factory-lookback entry as additional project guidance. Use this workflow when asked to look back across a bounded period or when an enabled workflows.lookback automation runs. Its purpose is to find patterns that normal item-by-item flows keep missing or fixing only temporarily.

Build a bounded evidence set

  1. Confirm the configured sources, scope, time window, comparison period, and output policy before querying. Use only connected sources the host can read.
  2. Enumerate the full configured range, follow every page or cursor, and record source filters, dates, counts, and unavailable or truncated sources. Do not report a complete lookback when coverage is partial.
  3. Include relevant feedback, analytics or telemetry, runtime errors, recurring CI or review failures, repeated recovery attempts, replies to earlier information requests, and prior fixes marked shipped or verified when those records are available.
  4. Cluster records by underlying symptom and affected boundary, not wording alone. Keep event counts, affected users or sessions, time range, versions, and distinct source links separate; do not infer identity or impact from unstable identifiers.

Find the systemic cause

For each recurring cluster, trace representative reports to their original source and inspect prior dispositions, commits, tests, and release evidence. Check whether the symptom recurred after a claimed fix, appeared through a sibling caller, or escaped because the normal workflow lacked a signal, owner, verification step, or recovery path.

For a report previously held for more information, inspect the full source thread for new replies. Re-triage the original report with the new details and check whether the normal fix flow now has enough evidence to proceed. Keep the item open when the answer is still incomplete; do not treat a reply as a fix or as permission for another action.

Reproduce a representative current case when possible. Trace related callers and surfaces to the shared boundary that can explain the evidence. Prefer one verified correction at that boundary over a pile of caller-specific patches. Separate confirmed causes from hypotheses, and state what evidence would disprove each proposed explanation.

Change and verify

Use workflows.lookback.implement to decide whether to recommend only, prepare a fix, or publish an authorized change. A recurring pattern is evidence for investigation, not automatic permission to edit code or take an external action. Keep reply, issue closure, PR approval, merge, deployment, and notification under their own configured policies.

When implementation is allowed, make the smallest systemic fix that addresses the confirmed cause. Add or update a regression check at the boundary, exercise the representative failure and relevant sibling paths, and inspect the same signals again after the fix. A test, merge, or “fixed” label alone does not prove that the live recurrence stopped.

Report

For each pattern, report:

  • the source links, date range, query coverage, counts, and impact evidence;
  • prior fixes or dispositions and whether the symptom returned;
  • information requests, answers received, and whether they changed the evidence or next step;
  • confirmed cause, alternatives still uncertain, and the shared boundary;
  • recommended or completed systemic change, regression proof, and remaining rollout or live-verification work;
  • independent reply, close, publish, approval, merge, deployment, and notify decisions, plus any exact human decision required.

Do not treat unavailable telemetry or an incomplete history as evidence that a pattern does not exist.

Frequently asked questions

What does the Factory Lookback AI skill do?

Experimental workflow for auditing recurring feedback, telemetry, errors, and brittle delivery paths to find systemic fixes. Use for periodic or requested cross-source lookbacks, not routine single-item intake.

Why use Factory Lookback on TypingMind?

Because you install it once and use it with any model. Factory Lookback 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 Factory Lookback in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BuilderIO/skills/tree/main/skills/factory-lookback. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Factory Lookback?

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 Factory Lookback?

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

Is the Factory Lookback AI skill free?

Yes. It is published on GitHub by BuilderIO 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.

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