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Hindsight

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EfficientStreet
hindsight

Reviews everything accomplished in the current session end-to-end, distills genuine process lessons (not one-off content specifics), and saves the durable ones as persistent memory — updating an existing related memory in place rather than duplicating it. Trigger on "get some hindsight," "run a hindsight pass," "what should we improve," "review today and notate lessons," "what did you learn today," "save what's worth remembering from this session." Does not redo or re-open any of the session's actual work, and never fabricates a lesson just to have something to report.

Overview

PublisherEfficientStreet
Repositoryhindsight
Skill namehindsight
Stars
174
Forks
27
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by EfficientStreet on GitHub. Read the source before you install it.

Installation

Install the Hindsight 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/EfficientStreet/hindsight/tree/main
  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/EfficientStreet/hindsight.git \
  .claude/skills/hindsight
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hindsight 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 Hindsight 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 Hindsight 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.

Hindsight

A session-end self-improvement pass. Read-only against the session's actual work — it never redoes or re-opens a task — and write-only against memory. User-triggered only: a hindsight pass writes persistent memory, so timing stays the user's call, never something run on a hunch.

When this fires

On-demand only: "get some hindsight," "run a hindsight pass," "what should we improve," "review today and notate lessons," "what did you learn today," "save what's worth remembering from this session."

Process

Review the full body of work completed in this session — every task, decision, correction, and course-change from start to finish, not just the most recent exchange. Then produce a structured hindsight report covering the following:

  1. Identify genuine process failures, not just outcomes. For each meaningful mistake, inefficiency, or round of back-and-forth correction, name the root cause — not the surface symptom. If something took multiple attempts to get right, ask what specifically caused each failed attempt, and whether a single upstream fix (a question asked earlier, a check performed differently, a different verification method) would have prevented the whole chain. Distinguish between mistakes that were reasonable given the information available at the time, and mistakes that came from skipping a step, assuming instead of asking, or verifying the wrong thing.

  2. Separate durable lessons from one-off noise. A lesson is worth keeping only if it would change how a future task gets handled — not just this one. Discard anything that's purely specific to this session's content and has no bearing on method or process going forward. For each lesson that survives that filter, state it as a concrete, actionable rule — not a vague sentiment like "be more careful." Say precisely what should happen differently: what question should be asked, at what point, or what check should be performed, and why the check that was actually used wasn't sufficient.

  3. Verify claims before writing them down. If a lesson references specific behavior, a tool's output, a file, or a technical constraint, confirm it against what actually happened in this session rather than reconstructing it from memory. Don't generalize from a single data point into a universal rule — note when something might be a one-time fluke versus a confirmed, repeatable pattern (e.g., something that failed identically on a second attempt is a stronger signal than something that failed once).

  4. Before saving any lesson, ask explicitly: did this session prove it, or only suggest it? "Proved" means the same failure happened more than once, the root cause was actually confirmed against real evidence (a tool's output, a file's contents, a technical constraint you checked), or a fix was verified to work. "Suggested" means it happened once, reads as plausible, but wasn't independently confirmed — a reasonable guess, not a demonstrated fact. This is a distinct check from Step 3's fact-verification — a claim can be accurately quoted from this session and still only be suggestive of a broader rule, not proof of one.

    • A proved lesson gets persisted as a firm rule.
    • A merely-suggested lesson does not get persisted as settled fact. Either hold it out of memory and name it in the final report as a watch-item pending a second occurrence, or persist it with the uncertainty stated plainly in the entry itself (e.g. "seen once, not yet confirmed as a pattern") — never write a single-occurrence guess as an unqualified rule.
    • If it's genuinely unclear which bucket a lesson falls into and the user is available, ask them rather than deciding unilaterally.
  5. Persist the lessons, don't just narrate them. If a persistent memory or notes system is available, write each surviving lesson into it as a discrete, well-labeled entry — check first whether an existing note already covers the same topic and update it in place rather than creating a near-duplicate. If no persistent memory system exists, produce the hindsight report as a clearly organized, standalone document the user can save themselves, written so it remains useful without the original conversation as context.

  6. Keep the scope honest. A handful of well-reasoned, specific lessons is more useful than a long list of shallow ones. If a session genuinely went well with nothing worth changing, say so plainly rather than manufacturing findings to fill space. If real infrastructure or environment constraints were discovered (a tool that doesn't work as documented, a permission that's more restrictive than expected, a connector that failed to connect), record those as factual findings distinct from process lessons about how the work itself was approached.

  7. Report back concisely. After persisting or writing up the hindsight report, summarize for the user in a few short sections what was learned and what will change — not a restatement of the whole session, just the lessons and their practical effect on future work. Flag any watch-items held back per Step 4 explicitly, so they don't just silently disappear.

In this environment

Check whether a persistent memory system is actually available before assuming Step 5's fallback — most agent harnesses that have one expose it distinctly from ordinary project files:

  • Claude Code keeps per-project memory under ~/.claude/projects/<project-slug>/memory/ (the slug is derived from the project's working-directory path — different per project, not a fixed path), one markdown file per fact, each with name/description/ metadata.type frontmatter. type: feedback fits nearly every hindsight lesson — it's guidance on how to work, with a why. That same folder's MEMORY.md is the index loaded into every session.
  • Whatever the mechanism, the same rule applies: read the index or browse existing entries first, to find one that already covers the lesson's topic. Existing topic → edit that file (append a dated addendum or finding, matching its existing structure) rather than creating a new one. New topic → new atomic file, one fact per file, cross-linked to related entries if the system supports it.
  • Every new or newly-relevant file needs a matching entry added to whatever serves as the index — a memory nothing points to is easy to miss next session.
  • If no such system exists in the current environment, fall back to Step 5's standalone-document path — write the hindsight report as one self-contained file the user can save wherever they keep notes.

Definition of done

  • Every persisted lesson traces to a real, specific event from this session — not a generic best practice restated.
  • Every persisted lesson was explicitly checked against Step 4: proved lessons are stated as firm rules; merely-suggested ones are either held as a reported watch-item or persisted with the uncertainty stated plainly — nothing single-occurrence is written as an unqualified rule.
  • Nothing written duplicates an existing memory's topic; existing entries were checked and updated in place where one already applied.
  • The memory index (or equivalent) reflects every new file.
  • The final report to the user is a few short sections, not a full session recap — lessons and their practical effect, nothing else.
  • If nothing genuinely durable came up, the report says so plainly instead of manufacturing filler.

Scope boundary

This skill does not redo, re-verify, or re-open any task from the session — it only reviews and reports on work already done. It does not edit project code or content as part of running; its only writes are to memory (and the memory index).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Hindsight AI skill do?

Reviews everything accomplished in the current session end-to-end, distills genuine process lessons (not one-off content specifics), and saves the durable ones as persistent memory — updating an existing related memory in place rather than duplicating it. Trigger on "get some hindsight," "run a hindsight pass," "what should we improve," "review today and notate lessons," "what did you learn today," "save what's worth remembering from this session." Does not redo or re-open any of the session's actual work, and never fabricates a lesson just to have something to report.

Why use Hindsight on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EfficientStreet/hindsight/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Hindsight?

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 Hindsight?

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

Is the Hindsight AI skill free?

Yes. It is published on GitHub by EfficientStreet 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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