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Chess Position

Community
brumar
chess-position

Deep study of ONE chess position (a FEN, a screenshot, a diagram, or "move N of game X"), not a whole game. First asks the user whether they want a POSITIONAL analysis (structure, plans for both sides, where each piece belongs) or a TACTICAL one (forcing lines, what works and what fails), then investigates with Stockfish as a fact-checker and explains the position in human terms. Optionally ends with a narrated video of the position via the chess-video skill. Use when the user shares a single position and asks "what is the plan here", "analyze this position", "what should White do", "explain this position", "is there a tactic", or sends a board image with no game attached.

Overview

Publisherbrumar
Repositorychess-postmortem-skills
Skill namechess-position
Stars
76
Forks
1
Bundled files
2
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 brumar on GitHub. Read the source before you install it.

Installation

Install the Chess Position 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/brumar/chess-postmortem-skills/tree/main/skills/chess-position
  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/brumar/chess-postmortem-skills.git /tmp/chess-postmortem-skills
mkdir -p .claude/skills
cp -r /tmp/chess-postmortem-skills/skills/chess-position .claude/skills/chess-position
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chess Position 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 Chess Position 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 Chess Position 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.

Single position: ask, investigate, explain

Goal: the user understands the position after reading you. The engine is a fact-checker, not the author. A list of Stockfish lines is not an answer; an explanation that a strong human would give, with every claim in it verified, is.

Tools come from the sibling skills: chess-analysis/scripts/query.py (eval, multipv, --restrict, --moves), chess-analysis/scripts/render_fen.py, chess-analysis/scripts/get_stockfish.sh, and for the video chess-video/scripts/make_video.py. Same venv convention (.venv-chess).

0. Ask first. Always.

Before any engine call, ask the user which analysis they want, with AskUserQuestion (skip only if their message already says it in so many words):

  • Positional: no forcing line decides the position. The question is what each side is trying to do: pawn structure, good and bad pieces, weak squares, pawn breaks, where every piece belongs, the plan for both sides.
  • Tactical: there may be a combination, a trap, or a forcing sequence. The question is what works, what fails, and why.

Do not guess from the position. A quiet-looking position can hide a tactic the user wants found, and a sharp one can be a question about plans. The two modes produce different deliverables, and the user loses time reading the wrong one.

In the same question round, settle what is missing:

  • Side to move. Never infer it. A screenshot or diagram never says whose turn it is, so for an image input always ask, even when the answer looks obvious (board orientation, last-move highlight, "it's my position"). Only a FEN's side-to-move field or the user's own words settle it. A wrong guess changes every line and every plan in the answer.
  • Level to write for, unless already known (CLAUDE.md, memory, or the chess-analysis skill's audience.level). Calibrate with chess-analysis/references/style.md.
  • Video at the end? (optional, see §4).

1. Get the position right

  • From an image: transcribe the FEN rank by rank, then render it with render_fen.py and compare the render to the image square by square. Count material both ways. A single misplaced pawn invalidates the whole analysis.
  • Castling rights: infer from king and rook squares; when both are castled, use -.
  • Record the FEN and the assumptions (side to move, castling) and state them in the final answer.

2. Investigate

  • Positional: follow references/positional.md. This is the demanding mode. You must reach an understanding you can state as rules, and then prove each rule with the engine.
  • Tactical: follow references/tactical.md.

Parallel probes: run 3-5 query.py calls at once in one shell command (( ... ) & ( ... ) & wait). For a large probe budget, subagents with the chess-analysis investigator.md posture work too, but one position is usually small enough to do yourself, and doing it yourself is how you actually understand it.

3. Deliver in chat

Lead with the answer, not the method. Positional shape:

  1. What the position is about: structure name (if any) and the one or two facts everything else follows from.
  2. Constraints: what each side cannot do yet, and why (one clause of proof each, with the number).
  3. Plan for each side as 3-5 rules, each with its reason. Rules, not move lists: "h3 first, because it opens h2 as a retreat for the bishop", not "1.h3 g6 2.Rac1".
  4. What not to do, with the cost in pawns.
  5. Verdict: the square(s) or piece(s) both plans are about, the eval, and the honesty-gate spread ("five moves within 0.1: the plan matters, the move does not").

Tactical shape: the key line first, then the refutation of each tempting alternative, then the idea in one sentence the user can reuse.

State your assumptions (side to move) at the top. If you corrected yourself during the work, say so plainly. The user needs to know which parts are solid.

4. Optional video

Uses the chess-video skill. make_video.py accepts a PGN with a [FEN] tag and no moves, so every shot is "ply": 0 and every line is a var. If the side to move cannot start the line you want to show (e.g. a Black idea with White to move), prefix it with the side to move's best quiet move (the engine's first choice), not a random pass, and say so in the narration or pick a move that belongs to the plan anyway (h3 here).

Storyboard for one position (~3-4 min, piper ~160 words/min):

  1. The frozen position: structure, the key imbalance, the honesty-gate number. hold 2.
  2. The constraints: each "cannot yet" shown as a short var.
  3. One segment per side's plan: route arrows first, then a 4-6 ply var.
  4. Double-edged pieces or squares, if the position has one.
  5. What not to do.
  6. Verdict: the rules, spoken, both sides.

Then verify as the chess-video skill says: extract frames, check captions are not clipped (keep them under ~18 characters, e.g. ...g6!, not a sentence), check arrows do not pile up on one square, and read the .srt end to end as prose. Re-read every factual sentence against the board: a narration line like "no black pawn can attack c5" is exactly the kind of claim that sounds right and is false (...b6 does).

Worked example of the whole skill: examples/position-carlsbad/.

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 Chess Position AI skill do?

Deep study of ONE chess position (a FEN, a screenshot, a diagram, or "move N of game X"), not a whole game. First asks the user whether they want a POSITIONAL analysis (structure, plans for both sides, where each piece belongs) or a TACTICAL one (forcing lines, what works and what fails), then investigates with Stockfish as a fact-checker and explains the position in human terms. Optionally ends with a narrated video of the position via the chess-video skill. Use when the user shares a single position and asks "what is the plan here", "analyze this position", "what should White do", "explai...

Why use Chess Position on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brumar/chess-postmortem-skills/tree/main/skills/chess-position. 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 Chess Position?

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 Chess Position?

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

Is the Chess Position AI skill free?

It is published on GitHub by brumar. Check the repository for licensing terms. 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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