rulest - GPU Rules Extractor
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Test done on 1050Ti with a target depth of 6 and a target runtime of 8 hours (the job was completed in about 2 hours with 1,311,335 generated chains and 724,887 valid unique chains found - you can think of the --target-hours flag as a multiplier of generated rules for the appropriate depths - on different GPUs these values will be different from each other in automatic mode).
Minor improvements:
- The extracted rules are now sorted by frequency (most hits first) for better prioritization, with the empty rule : placed at the top.
- A short header with metadata (total unique rules and total hits) is added to the output file for clarity.
- Removed global cap limit of generated chains (can be limited with flags).
# Generated by rulest_v2.0.py # Total unique rules: 878446 # Total hits (sum of frequencies): 377080316 : z6 x05 z8 '5 x33 z8 x23 y4 '0 y4 z8 x44 Z3 i4% z7 i8E x74 i60 x22 y4 z5 x30 R0 z7 z9 x26 x55 Z9 i0+ x77 Z5 x44 p4 z9 x73 z7 x16 x44 y5 o0[ x55 p9 x23 y6 x66 Z3 D2 i5m o4. i4v z8 x51 ... -
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Removed hardcoded depth limit;
--max-depthnow accepts any value, kernel constant set dynamically, enabling arbitrary chain depths via recursive rules expansion. -
The new optional
--seed-rulesparameter allows loading a file with previously successful rules. These seeds are grouped by depth and used at every stage of chain generation. For each target depth, the algorithm first adds existing seeds of that depth, then extends seeds from the previous depth by appending a random rule, and finally fills any remaining budget with random (hot‑biased) chains. Newly generated chains become seeds for deeper levels, enabling a recursive expansion that builds upon proven patterns. This focused approach makes it feasible to efficiently generate and test rule chains well beyond depth 6, leveraging prior knowledge to explore the most promising combinations.
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Not right, not left - straight ahead.
If you are working on sets of rules, I also have a concentrator tool, there is a link here on the forum and on discord. It can process very large sets of rules and debugged data, created mainly for optimizing, but I don't know what problem you are dealing with and whether it will be useful for anything. -
Fixes
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Rule Validator – Added $ and ^ to the branch that expects exactly one following character. Without this, those rules were filtered out and never made it to the GPU kernel.
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Seed Loading – The same validator now accepts $ and ^ rules, so seed files containing them are fully loaded and used.
# Generated by rulest_v2.0.py (seeded) # Total unique rules: 145287 # Total hits (sum of frequencies): 52455891 : ] l c ^s ^o ^l ^l ^o ^p '2 x65 '0 x54 x00 x01 o1@ x00 ^e ^k ^i ^r ^t ^s R2 x11 i0d x06 x11 x32 R3 *64 o0q '0 R0 x33 D1 $8 $5 $0 $0 x87 o48 '0 o28 R0 o1E x33 x34 p2 ^F x22 E ...https://gist.github.com/A113L/5ef467ced57f40bc7dadedce775cda6a
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Added missing GPU-compatible Hashcat rules: bitwise shifts (L, R), +1/-1 substitution (. and ,), range skip (O), and switch after Nth delimiter (3). Updated rule generation to add O for all digit pairs and 3NX for all digits and delimiters. Improved the kernel to support these operations, ensuring full GPU compatibility. No memory or discard rules were added.
Note: BTW, after extraction, since all rules are Hashcat-compatible, it is still recommended running the resulting ruleset by concentrator functional minimizing function. -
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The signature-based minimizer eliminates functional redundancy: it builds a deterministic probe set of base words (≥ min‑word‑len) and computes each rule’s output signature. Rules that produce identical outputs on all probe words are considered equivalent; only the one with the highest GPU hit‑count survives. This reduces the final rule set dramatically while preserving all unique transformations.
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The numeric‑seed extraction pass tests three built‑in families (pure prepend, pure append, and mixed prepend/append) up to depth 4 as direct GPU chain candidates—independent of random chain generation. It captures common numeric mutations (e.g., ^1 ^2, $9 $8, ^1 $2) efficiently, ensuring these high‑value patterns are never missed, even if the random chain budget is small.
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# rulest — GPU-Compatible Hashcat Rules Engine # Generated : 2026-03-28 04:14:53 # Base : hashmob.mini.txt # Target : hashmob.small.found # Depth : 1–10 # Bloom : 512 MB # # GPU raw candidates : 268,674 (bloom hits, includes false positives) # Post-processing : signature-based minimization # Probe words : 21 (min length 10) # Equiv. rules removed : 106,848 # # Rules kept : 161,825 (d1:1,856 d2:42,820 d3:34,157 d4:42,742 d5:10,555 d6:9,387 d7:7,435 d8:5,792 d9:3,892 d10:3,189) # Sorted by : GPU frequency (descending, UTF-8) : $1 $2 $3 $1 $2 $3 $4 x45 i5b 31n $2 $3 $4 $4 $5 $6 $1 $0 $1 $2 $0 $0 $0 $2 $0 $1 $0 $2 $0 $0 $2 x03 36e $2 $0 $1 $2 $3 $2 $1 $2 $0 $0 $1 $2 $0 $0 $3 $0 $0 $7 $2 $0 $0 $4 $2 $0 $0 $5 $1 $0 $0
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