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continuous-inverter

Real-time monitoring and automated remediation for proof system health.

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  • SKILL.md2.6 KB
  • CONCOMITANT_SKILLS.md677 B
  • continuous_inversion.jl18.6 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Continuous Inverter

Category: Real-Time Monitoring + CI/CD Type: Automated Measurement + Remediation Language: Julia Status: Production Ready Version: 1.0.0 Date: December 22, 2025

Overview

Real-time monitoring and automated remediation for proof system health. Runs on every commit to measure spectral gap across all 6 theorem provers in parallel, generates GitHub Actions CI/CD workflows, and provides automated suggestions when system health degrades.

Key Data Structures

struct CommitMetadata
    commit_hash::String
    timestamp::Float64
    author::String
    files_changed::Int
    gap_before::Dict{String, Float64}
    gap_after::Dict{String, Float64}
end

struct CommitAnalysis
    commits::Vector{CommitMetadata}
    trend::String
    violations::Int
    recommendation::String
end

Key Functions

  • analyze_commit(commit_hash): Measure gap before/after
  • check_all_provers(files): Parallel analysis across 6 provers
  • generate_remediation_suggestions(gap_before, gap_after): Automated advice
  • generate_ci_cd_template(): GitHub Actions workflow YAML
  • generate_monitoring_dashboard(): Trend visualization

Supported Provers

  • Dafny
  • Lean 4
  • Stellogen
  • Coq
  • Agda
  • Idris

Remediation Strategy

Alternating Möbius Weights for Resonance Patterns

When gap declines:

  1. Identify which prover degraded
  2. Run möbius_filter to find tangled paths
  3. Apply safe_rewriting recommendations
  4. Re-measure gap across all provers
  5. Deploy changes via CI/CD

Usage

using ContinuousInversion

# On every commit:
gap_after = compute_prover_gap(proofs)

# If gap < 0.25:
suggestions = suggest_remediation(prover, gap_before, gap_after)

# Generate CI/CD workflow:
yaml = generate_ci_cd_template()

Integration Points

  • GitHub Actions continuous deployment
  • Per-prover parallel checking
  • PR automation with gap status comments
  • Artifact upload for compliance tracking

Performance

  • Commit analysis: < 1 second
  • Per-prover check: Parallel across 6 provers
  • Dashboard generation: < 2 seconds
  • Total CI/CD latency: < 5 seconds

Deployment

# Save workflow to repo
julia continuous_inversion.jl > .github/workflows/spectral-health-check.yml

# Push to trigger
git add .github/workflows/spectral-health-check.yml
git commit -m "Add spectral health check"
git push

References

  • Continuous integration best practices
  • Automated remediation workflows
  • Möbius inversion for pattern detection

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