Skip to content

Near-Match Plugin Adaptation Lifecycle

Pitch

Enable safe, low-friction adaptation of existing plugins when they almost fit a new task, reducing duplication and increasing reuse while maintaining quality controls.

Why

Most new plugin requests are variants of existing capabilities. Without a formal adaptation lifecycle, teams either fork plugins ad hoc or rebuild from scratch, creating fragmentation and quality drift.

Problem

  • Duplicate plugins proliferate without a clear adaptation path.
  • Unreviewed modifications introduce bugs and regressions.
  • No consistent record of what changed, why, and with what impact.

Proposed Solution

Create a formal adaptation lifecycle with stages:

  1. Detection of near-match plugins.
  2. Structured gap analysis.
  3. Controlled modification and testing.
  4. Validation and promotion to production.

Lifecycle Stages

Stage 1: Near-Match Detection

  • Use semantic similarity on plugin metadata and required outputs.
  • Identify the closest plugin candidates.
  • Produce a ranked short list with compatibility scores.

Stage 2: Gap Analysis

  • Compare expected inputs/outputs with target requirements.
  • Identify missing capabilities and output mismatches.
  • Classify gaps as minor (parameter changes) or major (logic change).

Stage 3: Adaptation

  • Apply targeted modifications:
  • Input schema extensions
  • Output formatting changes
  • Parameter tuning
  • New edge-case handling

Stage 4: Testing

  • Run benchmark tests against known scenarios.
  • Compare performance with original plugin.
  • Validate output schema compatibility.

Stage 5: Promotion

  • Approve adapted plugin into registry.
  • Assign new semantic version.
  • Attach adaptation notes and rationale.

Output Schema

{
  "plugin_id": "plug_301",
  "adapted_from": "plug_212",
  "gap_summary": ["Add JSON schema X", "Handle multi-currency"],
  "test_status": "pass",
  "version": "2.1.0"
}

Integration Points

  • Linked to plugin hub discovery and benchmarking harness.
  • Uses safety governance for runtime loading.
  • Feeds change logs into audit trails.

Success Metrics

  • Reduction in duplicate plugins.
  • Faster delivery of adapted plugins.
  • Lower regression rates after adaptation.

Risks

  • Over-reliance on near-match detection can hide better designs.
  • Incomplete testing leads to silent failures.
  • Version sprawl without governance.

Future Enhancements

  • Automated adaptation suggestions.
  • Cross-plugin dependency mapping.
  • Adaptation impact scoring.

Detailed Implementation Plan

Phase A — Near-Match Retrieval

  1. Retrieve top-k plugins by capability similarity.
  2. Compute fit score against target contract.
  3. Select adaptation candidate above threshold.

Phase B — Adaptation Pipeline

  1. Branch plugin version for adaptation.
  2. Apply scoped modifications with test inheritance.
  3. Validate backward compatibility and performance.

Phase C — Promotion and Rollback

  1. Canary adapted versions in limited traffic.
  2. Auto-promote on success criteria.
  3. Auto-rollback on regression signals.

Validation Checklist

  • Adaptation success vs full re-synthesis
  • Regression incidence after promotion
  • Rollback mean time to recovery