Abductive Repl

by plurigrid

skill

Hypothesis-Test Loops via REPL for Exploratory Abductive Inference with Gay.jl colors

Skill Details

Repository Files

1 file in this skill directory


name: abductive-repl description: "Hypothesis-Test Loops via REPL for Exploratory Abductive Inference with Gay.jl colors"

abductive-repl

Hypothesis-Test Loops via REPL for Exploratory Abductive Inference

Version: 1.1.0 (music-topos enhanced) Trit: 0 (Ergodic - coordinates inference) Bundle: repl

Overview

Abductive-REPL enables exploratory abductive reasoning through an interactive REPL. Given observed outcomes, it generates hypotheses, tests them, and refines understanding through iterative loops.

Core Concept

Observation → Generate Hypotheses → Test → Refine → Repeat

Abduction: Given effect E and rule "A implies E",
           hypothesize A as possible cause.

Enhanced Integration: Interpreters

Julia (Gay.jl) - Primary

# Start abductive REPL with Gay.jl
julia --project=Gay.jl -e 'using Gay; Gay.repl()'

# In REPL:
gay> !abduce 216 125 157
# Searches invader space for color match

Hy (HyJAX) - Secondary

;; thread_relational_hyjax.hy integration
(import lib.thread_relational_hyjax :as tra)

(defn abduce-from-color [r g b]
  "Abduce invader ID from observed RGB"
  (let [target [r g b]
        analyzer (tra.ThreadRelationalAnalyzer)]
    ;; Search hypothesis space
    (lfor id (range 1 10000)
          :if (color-match? id target 0.05)
          {:hypothesis id :confidence (- 1.0 (color-distance id target))})))

Babashka (bb) - Scripting

;; abductive_repl.bb
(require '[babashka.process :refer [shell]])

(defn abduce [observed-color]
  (let [result (shell {:out :string} 
                      "julia" "--project=Gay.jl" "-e"
                      (format "using Gay; Gay.abduce(RGB(%s))" 
                              (clojure.string/join "," observed-color)))]
    (parse-hypotheses (:out result))))

REPL Commands Enhanced

Command Description Interpreter
!teleport <id> Jump to invader's world state Julia
!abduce r g b Infer invader from observed RGB Julia/Hy
!test [n] Run n abductive roundtrip tests Julia
!hy-analyze Run HyJAX relational analysis Hy
!bb-export Export hypotheses via Babashka Babashka

Properties (Testable Predicates)

# world_broadcast.rb integration
module AbductiveProperties
  def self.spi_determinism(id, seed)
    # Same input always produces same output
    c1 = WorldBroadcast::CondensedAnima.liquid_norm([id], r: 0.5)
    c2 = WorldBroadcast::CondensedAnima.liquid_norm([id], r: 0.5)
    c1 == c2
  end
  
  def self.abductive_roundtrip(id, seed)
    # Forward → Abduce → Verify
    forward = CondensedAnima.analytic_stack([id])
    cellular = CondensedAnima.to_cellular_sheaf(forward)
    cellular[:vertices].include?(id)
  end
end

GF(3) Triad Integration

Trit Skill Role
-1 slime-lisp Validates REPL expressions
0 abductive-repl Coordinates inference
+1 cider-clojure Generates evaluations

Conservation: (-1) + (0) + (+1) = 0 ✓

Justfile Recipes

# Start abductive REPL
abduce-repl:
    julia --project=Gay.jl -e 'using Gay; Gay.repl()'

# Run via Hy
abduce-hy:
    uv run hy -c '(import lib.thread_relational_hyjax) (print "HyJAX ready")'

# Babashka roundtrip test
abduce-bb-test n="100":
    bb -e '(println "Abductive tests:" {{n}})'

Related Skills

  • world-hopping - Possible world navigation
  • unworld - Derivation chains
  • gay-mcp - Color generation
  • condensed-analytic-stacks - 6-functor sheaf bridge

Related Skills

Attack Tree Construction

Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.

skill

Grafana Dashboards

Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.

skill

Matplotlib

Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.

skill

Scientific Visualization

Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.

skill

Seaborn

Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.

skill

Shap

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model

skill

Pydeseq2

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

skill

Query Writing

For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations

skill

Pydeseq2

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

skill

Scientific Visualization

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.

skill

Skill Information

Category:Skill
Last Updated:12/22/2025