What does this skill do with Claude.ai exports?

It compares two different export files to find what has changed, including new conversations and updates to existing ones, using advanced mathematical verification.

How does bisimulation verification work?

It checks if two conversation states are observationally equivalent by comparing conversation IDs, current nodes, and mapping lengths to detect hidden mutations.

Which tools are required to run the delta derivation?

The core pipeline uses standard Unix utilities like unzip, jq, and comm, while the advanced analysis utilizes Clojure-based logic.

What are ACSets in this context?

Attributed C-Sets are used as a schema to model the relationships between conversations, messages, and nodes as a graph structure for precise delta computation.

Conversation Delta Derivation

Name: Conversation Delta Derivation
Author: plurigrid

byplurigrid

•

Data Science & ML

Extracts and verifies information deltas between Claude.ai conversation exports using ACSets morphisms and bisimulation verification.

The Delta Derivation skill provides a mathematically rigorous way to analyze changes across Claude.ai conversation history exports. By treating conversation structures as Attributed C-Sets (ACSets), it identifies newly created threads and detects 'mutated' conversations where states have diverged. This skill is ideal for researchers and developers who need to quantify information gain, audit AI behavior over time, or manage large-scale conversation datasets using topological and category-theoretic principles.

Key Features

01Bisimulation verification to detect observational equivalence and state divergence

02Automated extraction and comparison of Claude.ai ZIP exports

03GF(3) triadic analysis for balanced validation, coordination, and generation roles

04Skill mention extraction to track the frequency of specific tool invocations

05Identification of new and mutated conversation threads using JQ and Comm

068 GitHub stars

Use Cases

01Auditing and verifying consistency in automated agent-to-agent interactions

02Tracking research progress and information gain across periodic AI data backups

03Analyzing the evolution of specific skill usage patterns within large-scale conversation logs

Key Features

01Bisimulation verification to detect observational equivalence and state divergence

02Automated extraction and comparison of Claude.ai ZIP exports

03GF(3) triadic analysis for balanced validation, coordination, and generation roles

04Skill mention extraction to track the frequency of specific tool invocations

05Identification of new and mutated conversation threads using JQ and Comm

068 GitHub stars

Use Cases

01Auditing and verifying consistency in automated agent-to-agent interactions

02Tracking research progress and information gain across periodic AI data backups

03Analyzing the evolution of specific skill usage patterns within large-scale conversation logs