Menu
Truncate LLM output to exactly N sentences and fall back to a known-good baseline string when output is empty or too short
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Memory-efficient Keras generator that streams sharded CSV files in chunks, renders strokes to images on-the-fly, and yields batches for training on datasets too large for memory
Stack 1D convolutions for local feature extraction before bidirectional LSTMs to classify variable-length stroke sequences into hundreds of doodle categories
Partition a large dataset into N balanced shards using integer key modulo arithmetic for reproducible, class-interleaved splits across CSV files
Normalize raw stroke coordinates to 0-255 range, resample at uniform arc-length spacing, then apply Ramer-Douglas-Peucker simplification
Render stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order
Score each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function
| name | llm-sentence-truncation-fallback |
| description | Truncate LLM output to exactly N sentences and fall back to a known-good baseline string when output is empty or too short |
LLM outputs for structured tasks (prompt recovery, classification rationale, single-line answers) often include unwanted preamble, repetition, or rambling. Truncate to the first N sentences, then check length โ if the result is too short or empty, substitute a well-scoring baseline string. This two-step post-processing ensures consistent submission quality.
import re
BASELINE = "Improve the following text using a more formal and academic tone while maintaining the original meaning"
def truncate_sentences(text, max_sentences=1):
sentences = re.split(r'(?<=[.!?])\s+', text.strip())
result = ' '.join(sentences[:max_sentences])
return result
def postprocess(raw_output, min_length=15):
cleaned = truncate_sentences(raw_output, max_sentences=1)
cleaned = cleaned.strip().strip('"').strip("'")
if len(cleaned) < min_length:
return BASELINE
return cleaned
predictions = [postprocess(output) for output in raw_outputs]