Results for “consensus-building”

12 skills
More results
dontbesilent2025
dbs-resonate
Diagnoses whether a draft will resonate with its audience using a five-dimension communication psychology framework, identifies structural problems, and gives specific fixes.
openai
notion-knowledge-capture
Capture conversations and decisions into structured Notion pages for easy reuse as wiki entries, how-tos, decisions, or FAQs with proper linking.
23.3k · bundle
thedotmack
knowledge-agent
Build and query AI-powered knowledge bases from claude-mem observations, enabling focused conversational sessions on specific topics.
k-dense-ai
consciousness-council
Simulates a multi-perspective deliberation council to explore complex questions, decisions, or creative challenges from diverse thinking archetypes.
30.2k · bundle
alirezarezvani
grill-me
Interviews users relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree.
20.4k · bundle
wondelai
made-to-stick
Craft messages that are understood, remembered, and drive action using the SUCCESs framework (Simple, Unexpected, Concrete, Credible, Emotional, Stories).
1.6k · bundle
composiohq
notion-knowledge-capture
Capture conversations and decisions into structured Notion pages for easy reuse as wiki entries, how-tos, decisions, or FAQs with proper linking.
66.9k · bundle
huuanh20
collision-zone-thinking
Forces unrelated concepts together to spark novel solutions and uncover emergent properties.
1
samyakjhaveri
reflect
Generates a structured post-task reflection capturing surprises, patterns, prompt improvements, and gotchas, writing it to a markdown file without modifying project rules.
0
dangquangse
collision-zone-thinking
Forces unrelated concepts together to spark novel solutions and emergent properties.
19
alterlab-ieu
alterlab-geniml
Machine learning on genomic interval data (BED files) with the geniml Python package — region embeddings (Region2Vec), joint region+metadata embeddings (BEDspace/StarSpace), single-cell ATAC-seq embeddings (scEmbed), consensus peak sets / universes (build-universe), tokenization, BEDshift randomization, and BBClient/BEDbase caching. Use when training or using region/cell embeddings, clustering scATAC-seq, building a tokenization universe from BED collections, or any ML/feature-learning task over genomic regions. NOT for plain interval arithmetic (overlap/intersect/merge counts) — that is gtars, not geniml. Part of the AlterLab Academic Skills suite.
60 · bundle