Why ChessGrade exists
ChessGrade was created to bridge the gap between playing a game, finding a mistake, and knowing what to study next. Instead of an endless random queue, it organizes puzzles by theme, difficulty, and game phase. History and review tools bring back patterns that remain fragile.
The product combines tactical training, spaced repetition, Woodpecker cycles, endgames, and game analysis. Stockfish supplies an objective calculation reference; natural-language explanations translate evaluations into study questions. No feature guarantees rating gains. Consistency, reflection, and practical play remain the student's responsibility.
Who it is for
The main audience is beginner and intermediate players who know the rules and want a more deliberate routine. Advanced players can use filtering and review, while editorial material prioritizes clarity and recurring practical patterns.
How content is made
The ChessGrade team maintains the product and editorial pages. Articles use verifiable chess concepts, FEN examples, and technical analysis. Automated tools may assist calculation or drafting, but publication requires checks for legal moves, pedagogical clarity, attribution, and working references.
Readers can request corrections through the contact page. The editorial policy explains review and automation; the sources page identifies puzzle origins and third-party technology.
Why this differs from a random queue
Solving one isolated position can be enjoyable, but it does not reveal which patterns are stable. ChessGrade preserves enough context to compare performance by theme, difficulty, and time. Repeated errors in discovered attacks call for a different intervention than pawn-endgame or time-management errors.
A report is not a verdict on the player. It offers a study hypothesis to test in new positions and games. Connecting error, explanation, and repetition makes the next step observable: review a missed flight square, calculate one move deeper, or practice a specific conversion.
What we offer
- Filtered tactical puzzles and adaptive sessions.
- Woodpecker cycles and spaced review.
- Endgame and board-visualization training.
- PGN analysis with Stockfish as a calculation reference.
- Interpretive narratives and progress statistics.
- Public guides, articles, and annotated puzzles.
Transparency and limitations
The collection includes third-party data identified on the sources page. Curation, training flows, editorial writing, and links between resources are ChessGrade work. When Stockfish or AI contributes to an analysis, that role is disclosed; automated output never replaces checking the board.
We do not claim that more exercises automatically produce better results. Sleep, practice quality, competitive experience, instruction, and individual circumstances affect learning. Platform statistics describe activity inside the product and are not a professional assessment.
How to begin
Read one guide, try the annotated demo, and choose a theme that appears in your games. Run a short session without moving pieces during calculation. Review each error and write a concrete rule. The next week, look for evidence of that pattern in a game. That connection matters more than raw volume.
For project questions, support, or corrections, use the public contact page. Data requests have a dedicated privacy address. Visitors should be able to understand who we are, how the product works, and its limits before creating an account.
Commitment to public access
Guides, policies, and annotated puzzles remain readable without login and without an API call. Visitors can evaluate the method before registration, reference material during study, and see the educational foundation of the product. Personalized tools require accounts because they depend on history, but explanations of the methods do not remain hidden.
The project will continue to change, and every feature may not always be available. Material changes to data, providers, or editorial criteria will be reflected in the corresponding pages. We prefer accurately describing the current state over presenting future ideas as existing functionality.
How ChessGrade is different
Solving random puzzles is fun, but it rarely tells you what to work on. The difference with ChessGrade is context: every puzzle carries a theme, a difficulty estimate, and a game phase, so a session is not just a score but a map of your recurring strengths and gaps. When you miss a back-rank tactic three times, that signal is not lost — it comes back through spaced review and connects to the relevant guide and puzzle pages. The aim is to turn scattered practice into a deliberate routine you can actually sustain.
We are also deliberate about honesty. No feature promises a rating gain, and the methodology spells out where automation ends and your judgment begins. Stockfish gives an objective calculation reference, and AI explanations translate evaluations into study questions — but publication of every article passes human checks for legal moves, clarity, and working references, as described in our editorial policy.