datalog-fixpoint
Datalog bottom-up fixpoint iteration for recursive queries
Best use case
datalog-fixpoint is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Datalog bottom-up fixpoint iteration for recursive queries
Teams using datalog-fixpoint should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/datalog-fixpoint/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How datalog-fixpoint Compares
| Feature / Agent | datalog-fixpoint | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Datalog bottom-up fixpoint iteration for recursive queries
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Datalog Fixpoint Skill Bottom-up fixpoint iteration for recursive Datalog queries without explicit recursion. ## Core Concept Datalog computes fixpoints via iterative saturation: ``` T^0(∅) → T^1 → T^2 → ... → T^ω (fixpoint) ``` Where T is the immediate consequence operator. ## Scientific Skill Interleaving This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem: ### Dataframes - **polars** [○] via bicomodule - High-performance dataframes ### Bibliography References - `algorithms`: 19 citations in bib.duckdb ## Cat# Integration Fixpoint computation maps to Cat# via coalgebraic semantics: ``` Trit: 0 (ERGODIC - iterative bridge) Home: Prof (profunctors/bimodules) Poly Op: ⊗ (parallel saturation) Kan Role: Adj (Kleisli adjunction) ``` ### GF(3) Naturality Datalog fixpoint iteration is inherently ERGODIC: - Each iteration step is a natural transformation - Convergence = reaching the terminal coalgebra - The fixpoint IS the bicomodule equilibrium
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