datalog-fixpoint

Datalog bottom-up fixpoint iteration for recursive queries

16 stars

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

$curl -o ~/.claude/skills/datalog-fixpoint/SKILL.md --create-dirs "https://raw.githubusercontent.com/plurigrid/asi/main/plugins/asi/skills/datalog-fixpoint/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/datalog-fixpoint/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How datalog-fixpoint Compares

Feature / Agentdatalog-fixpointStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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