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Tactical pattern
recognition for chess.

A specialized inference engine that detects, classifies, and explains tactical patterns in any chess position. One API call. Deterministic results.

3ms

L1 Median Latency

42ms

L2 Full Analysis

Tactical Patterns

99.7%

Uptime SLA

Try the Playground Read the Docs

POST /api/v1/extract

Request

{
  "fen": "r1bqkb1r/pppp1ppp/ 2n2n2/4p2Q/2B1P3/ 8/PPPP1PPP/RNB1K1NR w KQkq - 4 4",
  "depth": "L2",
  "with_sequence": true
}

Response

{
  "patterns": [{
    "type": "fork",
    "trigger": "Qxf7+",
    "targets": ["e8", "h8"],
    "is_mate": true,
    "sequence": ["Qxf7#"]
  }],
  "meta": {
    "depth": "L2",
    "latency_ms": 87
  }
}

87ms — Fork detected (Qxf7+ targets king & rook)

How it works

Three stages. One response.

ChessGrammar processes each position through a deterministic analysis pipeline, from structural scan to forcing tree validation.

Parse

FEN or PGN input is validated, normalized, and converted to an internal board representation.

< 1ms

Detect

L1 structural scan identifies candidate patterns. L2 generates forcing trees to validate each tactic.

3ms (L1) — 42ms (L2)

Classify

Each confirmed pattern is typed, annotated with trigger moves, targets, material gain, and optional sequences.

Deterministic output

Pattern Taxonomy

10 tactical patterns. Formally defined.

Each pattern follows a strict definition from chess theory, with clear trigger conditions and validation rules.

Fork

One piece attacks two+

Piece shields higher value

Skewer

Reverse pin on a line

Discovered Attack

Moving piece reveals attack

Double Check

Two pieces give check

Back Rank Mate

King trapped on last rank

Smothered Mate

Knight mates boxed king

Deflection

Lure defender away

Interference

Block defensive line

Trapped Piece

No safe squares remain

Detection Pipeline

Two-layer depth model

L1 scans structure. L2 validates with forcing trees. Choose precision or speed per request.

Input

FEN / PGN

Validated & normalized

→

Layer 1

Structural Scan

Geometric pattern matching on board geometry

~3ms

→

Layer 2

Forcing Tree

Alpha-beta validation via forced move sequences

~42ms

→

Output

Classified Patterns

Type, trigger, targets, material, sequences

Performance

Built for production workloads.

Measured on real-world positions from international tournament databases. All benchmarks are reproducible.

3ms

L1 Median Latency

p99 < 15ms

42ms

L2 Full Analysis

p99 < 500ms

97.3%

Detection Accuracy

vs. 25k annotated positions

Pattern	L1 Latency (p50)	L2 Latency (p50)	Accuracy
Fork	2ms	38ms	98.1%
Pin	4ms	38ms	97.8%
Skewer	1ms	1ms	96.5%
Discovered Attack	5ms	114ms	97.2%
Double Check	6ms	457ms	99.1%
Back Rank Mate	2ms	20ms	98.4%
Smothered Mate	3ms	7ms	99.3%
Deflection	2ms	132ms	95.2%
Interference	3ms	246ms	94.8%
Trapped Piece	10ms	111ms	96.1%

Why ChessGrammar

Not another chess engine.

Stockfish evaluates positions. ChessGrammar classifies tactics. Different problem. Purpose-built solution.

⚖

Classification, not evaluation

General engines answer "what's the best move?" — ChessGrammar answers "what tactic exists here?" with formal pattern labels and structured metadata.

⚡

Deterministic & reproducible

Same input, same output. Every time. No neural network variance, no stochastic search. Results you can rely on for production systems.

⚙

Forcing tree validation

L2 analysis builds complete forcing sequences to confirm each tactic. Not heuristic guesses — validated move-by-move with refutation handling.

⇄

API-first architecture

Designed as infrastructure from day one. RESTful, stateless, zero dependencies. Integrate into any stack with a single HTTP call.

Explore ChessGrammar.

Developer preview. No API key required. Rate limits apply.

Read the Documentation Try the Playground

Coming soon

v3 is coming

A complete engine rewrite with a formal 8-level architecture. More patterns, deeper analysis, higher precision.

Formal grammar

8-level detection hierarchy

L5 combinations

Multi-move tactical sequences

Certified detection

Improved precision across all patterns

Detection coverage

Every pattern v3 will detect

L2State patterns

Composites — evaluate a position

Pin
Hanging Piece
Trapped Piece
Overloading
X-Ray
Battery

L3Action patterns

Tactics — evaluate a move

Fork
Skewer
Discovered Attack
Deflection
Double Check

L4Confirmation

Validation & positional eval

Forcing Tree
King Safety
Exposed King

L5Combinations

Multi-move sequences & annotations

Mate-in-N
Back Rank Mate
Smothered Mate
Attraction
Clearance
Interference

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Tactical patternrecognition for chess.