Claude Code Memory Frameworks: Strategic Analysis for RSOLV (v2.0 - Revised)

autogrep-rfc003-evaluation.md

AutoGrep Evaluation for RFC-003

Date: 2026-01-02 Purpose: Evaluate AutoGrep as an accelerator for RFC-003 (Learning Security System)

---

Executive Summary

AutoGrep is an open-source tool (Apache 2.0) that automates Semgrep rule generation from vulnerability patches using LLMs. It directly addresses the core challenge of RFC-003: converting security fixes into reusable detection rules.

Key Findings

Aspect	Assessment
Relevance	HIGH - Directly implements RFC-003's rule generation step
Maturity	PROVEN - 39,931 patches → 645 validated rules
License	PERMISSIVE - Apache 2.0, commercial use allowed
Integration Path	REFERENCE IMPLEMENTATION - Adopt patterns, not code
Effort to Leverage	3-5 weeks for adapted implementation

Recommendation

Adopt AutoGrep's proven techniques (prompting, filtering, validation) as reference patterns for an Elixir implementation. Do not fork/integrate the Python code directly.

---

Part 1: AutoGrep Technical Analysis

1.1 What AutoGrep Does

Input: Vulnerability Patch (before/after code)
   ↓
LLM Analysis (DeepSeek Chat via OpenRouter)
   ↓
Candidate Semgrep Rule (YAML)
   ↓
Three-Stage Filtering:
   1. Duplicate Detection (embeddings, 0.9 threshold)
   2. Quality Evaluation (LLM project-specificity check)
   3. Validation (Semgrep CLI against vulnerable/fixed code)
   ↓
Output: Validated Semgrep Rule

1.2 Evaluation Statistics

Metric	Value
Patches Processed	39,931
Unique CVEs	26,617
Source Repositories	6,945
Languages Covered	20
Initial Rules Generated	3,591 (8.99% of patches)
Final Rules After Filtering	645 (17.96% retention)
End-to-End Yield	1.6%
False Positive Rate	18-25%

1.3 Filtering Pipeline Breakdown

Stage	Rules Removed	Percentage
Duplicates (embedding similarity)	386	10.75%
Trivial (exact string matches)	5	0.14%
Overly Specific (project-dependent)	2,555	71.15%
Retained	645	17.96%

1.4 Language Distribution (Final Rules)

Language	Rules	Percentage
JavaScript	172	26.67%
Python	156	24.19%
Java	103	15.97%
Go	68	10.54%
PHP	46	7.13%
Ruby	33	5.12%
C	27	4.19%

1.5 Vulnerability Types Covered

Type	Rules	Percentage
Injection Attacks	142	22.02%
Cross-Site Scripting (XSS)	97	15.04%
Path Traversal	63	9.77%
Insecure Cryptography	57	8.84%
Authentication Bypass	47	7.29%

---

Part 2: RFC-003 Gap Analysis

2.1 RFC-003 Learning Loop

RFC-003 Vision:
Fix Security Issue → Generate Semgrep Rule → Test Rule
      ↓                      ↓                   ↓
Store in Org DB → Scan Entire Codebase → Fix All Instances
      ↓                      ↓                   ↓
Track Effectiveness → Refine Rules → [Loop Back]

2.2 What AutoGrep Provides vs RFC-003 Requirements

RFC-003 Requirement	AutoGrep Provides	Gap
Generate Semgrep rule from fix	✅ Full implementation	None
Test rule with +/- examples	✅ Validates against vulnerable/fixed code	None
Quality filtering	✅ Three-stage filtering	None
Duplicate detection	✅ Embedding-based similarity	None
Language detection	✅ Automatic from file extensions	None
Organization-specific storage	❌ Not implemented	Major gap
Codebase scanning workflow	❌ Not implemented	Major gap
Fix generation for new matches	❌ Not implemented	Major gap
Effectiveness tracking	❌ Not implemented	Major gap
Rule refinement based on metrics	❌ Not implemented	Major gap
Real-time learning integration	❌ Batch processing only	Major gap

2.3 Conclusion

AutoGrep implements Phase 1 of RFC-003 (rule generation) but not the learning loop (storage, scanning, tracking, refinement). It's a component, not a complete solution.

---

Part 3: RSOLV Advantages Over AutoGrep

RSOLV has significant advantages that would improve on AutoGrep's approach:

3.1 Better Input Data

Aspect	AutoGrep	RSOLV
Source	CVE patches (inferred context)	Our own fixes (known context)
Vulnerability Type	Inferred from patch	Known from detection
Confidence	Inferred	Calculated (8 factors)
Language	Inferred from extension	Known from AST parsing
Framework	Unknown	Often detected

RSOLV already knows it's SQL injection, XSS, etc. AutoGrep must infer everything.

3.2 Existing Infrastructure

Component	Status	Benefit for Rule Generation
confidence_scorer.ex	Production	Quality assessment baseline
fallback_strategy.ex	Production	Pattern extraction heuristics
76+ security patterns	Production	Vulnerability type classification
PostgreSQL multi-tenant	Production	Organizational storage ready
Claude integration	Production	Higher quality LLM than DeepSeek

3.3 Real-Time Integration Potential

AutoGrep is batch processing. RSOLV can generate rules immediately after each fix:

RSOLV Fix Completes
      ↓
Diff available (vulnerable → fixed)
      ↓
Vulnerability type KNOWN (SQL injection)
      ↓
Generate Semgrep rule with TARGETED prompt
      ↓
Validate against the actual files
      ↓
Store in org-specific table
      ↓
Optionally scan codebase for more instances

---

Part 4: Integration Strategy

4.1 Options Evaluated

Option	Description	Effort	Pros	Cons
A: Fork	Fork AutoGrep, adapt for RSOLV	2-3 weeks	Fastest start	Python dependency, architectural mismatch
B: Reference	Rewrite in Elixir using AutoGrep's patterns	3-4 weeks	Clean architecture, native integration	More initial work
C: Hybrid	Call AutoGrep as subprocess	1-2 weeks	Minimal code	External dependency, subprocess overhead

4.2 Recommendation: Option B (Reference Implementation)

Rewrite AutoGrep's proven patterns in Elixir for these reasons:

1. Native Platform Integration: Runs in RSOLV's BEAM cluster
2. PostgreSQL Storage: Use existing multi-tenant infrastructure
3. Claude Integration: Use existing AI provider setup (better than DeepSeek)
4. Confidence Scoring: Leverage existing confidence_scorer.ex
5. Real-Time Processing: GenServer-based, not batch
6. No Python Dependency: Cleaner deployment

---

Part 5: Implementation Plan

5.1 Phase 1: Rule Generation (Weeks 1-2)

# lib/rsolv/learning/rule_generator.ex
defmodule Rsolv.Learning.RuleGenerator do
  @moduledoc """
  Generates Semgrep rules from RSOLV fixes using LLM.
  Based on AutoGrep's proven methodology.
  """

  def generate_from_fix(fix, vulnerability, context) do
    # 1. Build prompt with RSOLV's rich context
    prompt = build_prompt(fix, vulnerability, context)

    # 2. Call Claude (existing integration)
    {:ok, candidate_rule} = Rsolv.AI.generate(prompt, type: :semgrep_rule)

    # 3. Parse and validate YAML
    {:ok, parsed} = parse_semgrep_yaml(candidate_rule)

    # 4. Return for filtering
    {:ok, parsed}
  end

  defp build_prompt(fix, vulnerability, context) do
    """
    Generate a Semgrep rule to detect this #{vulnerability.type} vulnerability.

    VULNERABLE CODE:
    ```#{context.language}
    #{fix.before_code}
    ```

    FIXED CODE:
    ```#{context.language}
    #{fix.after_code}
    ```

    VULNERABILITY DETAILS:
    - Type: #{vulnerability.type}
    - CWE: #{vulnerability.cwe}
    - Severity: #{vulnerability.severity}
    - File: #{context.file_path}

    REQUIREMENTS:
    1. Use metavariables ($VAR, $FUNC) for generalizable patterns
    2. Do NOT use exact string matches
    3. Include pattern-not for the fixed version
    4. Set appropriate severity level
    5. Include helpful message for developers

    OUTPUT FORMAT: Valid Semgrep YAML only
    """
  end
end

5.2 Phase 2: Filtering Pipeline (Week 2-3)

# lib/rsolv/learning/rule_filter.ex
defmodule Rsolv.Learning.RuleFilter do
  @moduledoc """
  Three-stage filtering based on AutoGrep methodology.
  """

  @duplicate_threshold 0.9

  def filter(rule, org_id) do
    with :ok <- check_not_duplicate(rule, org_id),
         :ok <- check_not_trivial(rule),
         :ok <- check_not_project_specific(rule),
         :ok <- validate_with_semgrep(rule) do
      {:ok, rule}
    else
      {:reject, reason} -> {:rejected, reason}
    end
  end

  # Stage 1: Duplicate detection using embeddings
  defp check_not_duplicate(rule, org_id) do
    existing_rules = Rsolv.Learning.Storage.list_rules(org_id)
    rule_embedding = compute_embedding(rule)

    duplicates = Enum.filter(existing_rules, fn existing ->
      similarity = cosine_similarity(rule_embedding, existing.embedding)
      similarity > @duplicate_threshold
    end)

    case duplicates do
      [] -> :ok
      [dup | _] -> {:reject, {:duplicate, dup.id}}
    end
  end

  # Stage 2: Trivial pattern detection
  defp check_not_trivial(rule) do
    has_metavariables = String.contains?(rule.pattern, "$")

    if has_metavariables do
      :ok
    else
      {:reject, :trivial_exact_match}
    end
  end

  # Stage 3: Project-specificity check (LLM-based)
  defp check_not_project_specific(rule) do
    prompt = """
    Evaluate this Semgrep rule for reusability:

    #{rule.yaml}

    Is this rule:
    A) Generic and reusable across projects (uses standard libraries)
    B) Project-specific (references custom classes, internal APIs)

    Reply with only A or B.
    """

    case Rsolv.AI.generate(prompt) do
      {:ok, "A" <> _} -> :ok
      {:ok, "B" <> _} -> {:reject, :project_specific}
      _ -> :ok  # Default to accept on ambiguous response
    end
  end

  # Stage 4: Semgrep CLI validation
  defp validate_with_semgrep(rule) do
    # Write rule to temp file
    # Run: semgrep --config temp_rule.yaml --validate
    # Check exit code
    case System.cmd("semgrep", ["--config", rule_path, "--validate"]) do
      {_, 0} -> :ok
      {error, _} -> {:reject, {:invalid_syntax, error}}
    end
  end
end

5.3 Phase 3: Storage & Tracking (Week 3-4)

# Migration: priv/repo/migrations/xxx_create_organization_rules.exs
defmodule Rsolv.Repo.Migrations.CreateOrganizationRules do
  use Ecto.Migration

  def change do
    create table(:organization_rules, primary_key: false) do
      add :id, :binary_id, primary_key: true
      add :organization_id, references(:organizations, type: :binary_id)
      add :rule_id, :string, null: false
      add :semgrep_yaml, :text, null: false
      add :vulnerability_type, :string
      add :language, :string
      add :embedding, {:array, :float}  # For duplicate detection
      add :source_fix_id, references(:fixes, type: :binary_id)
      add :is_active, :boolean, default: true
      add :confidence, :float, default: 0.5

      timestamps()
    end

    create unique_index(:organization_rules, [:organization_id, :rule_id])
    create index(:organization_rules, [:organization_id, :is_active])
    create index(:organization_rules, [:vulnerability_type])

    create table(:rule_metrics) do
      add :rule_id, references(:organization_rules, type: :binary_id)
      add :scan_id, :string
      add :matches_found, :integer, default: 0
      add :true_positives, :integer, default: 0
      add :false_positives, :integer, default: 0
      add :fixes_generated, :integer, default: 0

      timestamps()
    end

    create index(:rule_metrics, [:rule_id])
  end
end

5.4 Phase 4: Integration Hook (Week 4-5)

# lib/rsolv/learning/fix_hook.ex
defmodule Rsolv.Learning.FixHook do
  @moduledoc """
  Hook that triggers rule generation after successful fixes.
  """

  def after_fix_applied(fix, vulnerability, context) do
    # Only generate rules for high-confidence fixes
    if fix.confidence > 0.7 and fix.tests_pass do
      Task.start(fn ->
        generate_and_store_rule(fix, vulnerability, context)
      end)
    end
  end

  defp generate_and_store_rule(fix, vulnerability, context) do
    with {:ok, candidate} <- Rsolv.Learning.RuleGenerator.generate_from_fix(fix, vulnerability, context),
         {:ok, filtered} <- Rsolv.Learning.RuleFilter.filter(candidate, context.org_id),
         {:ok, stored} <- Rsolv.Learning.Storage.store_rule(filtered, context.org_id) do

      # Optionally trigger codebase scan
      if context.org_settings.auto_scan_enabled do
        Rsolv.Learning.Scanner.scan_with_rule(stored, context.org_id)
      end

      {:ok, stored}
    else
      {:rejected, reason} ->
        Logger.info("Rule generation rejected: #{inspect(reason)}")
        {:rejected, reason}

      error ->
        Logger.error("Rule generation failed: #{inspect(error)}")
        error
    end
  end
end

---

Part 6: Expected Outcomes

6.1 Quality Improvements Over AutoGrep

Metric	AutoGrep	Expected RSOLV
False Positive Rate	18-25%	10-15% (better input data)
Rule Yield	1.6%	5-10% (known vuln type)
Validation Accuracy	Single patch test	Full test suite + AST
Organizational Relevance	Generic	Org-specific patterns

6.2 Timeline

Phase	Duration	Deliverable
Phase 1: Rule Generation	2 weeks	RuleGenerator module
Phase 2: Filtering Pipeline	1 week	RuleFilter module
Phase 3: Storage & Tracking	1 week	Schema + Storage module
Phase 4: Integration Hook	1 week	FixHook + real-time generation
Total MVP	5 weeks	End-to-end rule learning

6.3 Success Metrics

Metric	Target
Rules generated per org per month	10+
Rule retention after filtering	>30%
False positive rate	<15%
Additional vulnerabilities found by rules	2x baseline

---

Part 7: Risks & Mitigations

Risk	Likelihood	Impact	Mitigation
LLM generates invalid Semgrep syntax	High	Medium	Retry with error feedback (AutoGrep pattern)
Rules too project-specific	Medium	Medium	LLM quality check + metavariable requirement
Storage costs grow	Low	Low	Rule deduplication + archival policy
Semgrep CLI dependency	Low	High	Docker containerization + fallback

---

Part 8: Conclusions

8.1 AutoGrep Value

AutoGrep provides proven, validated techniques for LLM-based Semgrep rule generation:

1. Prompting strategies that produce generalizable patterns
2. Three-stage filtering that reduces false positives
3. Validation methodology using Semgrep CLI
4. Quantified results (645 rules from 39,931 patches)

8.2 RSOLV Integration Path

1. Adopt AutoGrep's patterns, not its code
2. Implement in Elixir for native platform integration
3. Leverage existing infrastructure (confidence scoring, AI providers, PostgreSQL)
4. Improve on AutoGrep with better input data (known vuln types)

8.3 Final Recommendation

Proceed with Option B: Build an Elixir implementation using AutoGrep as reference. Estimated 5 weeks to MVP. Expected improvement: 2-3x better rule yield than AutoGrep due to richer input data.

---

Appendix: AutoGrep Source References

• Repository: https://github.com/lambdasec/autogrep
• Paper: https://lambdasec.github.io/AutoGrep-Automated-Generation-and-Filtering-of-Semgrep-Rules-from-Vulnerability-Patches/
• License: Apache 2.0
• Data Source: MoreFixes dataset (https://zenodo.org/records/13983082)
• LLM Used: DeepSeek Chat via OpenRouter
• Embedding Model: all-MiniLM-L6-v2 (sentence-transformers)

---

Evaluation completed 2026-01-02

elf-rsolv-analysis-report-v2.md

Claude Code Memory Frameworks: Strategic Analysis for RSOLV

Report Date: 2026-01-02 Version: 2.0 (Revised) Prepared By: RSOLV Engineering

---

Executive Summary

This report evaluates persistent memory frameworks for Claude Code, with focus on the Emergent Learning Framework (ELF), and assesses relevance to RSOLV's security platform.

Key Findings

Finding	Implication
RSOLV already has sophisticated confidence scoring	No need to adopt ELF's simpler approach; extend existing system instead
AutoGrep directly implements RFC-003's vision	Open-source tool for Semgrep rule generation from patches - potential accelerator
claude-flow is the more serious framework	87+ MCP tools, enterprise-grade; ELF is simpler but less capable
RSOLV's gap is the learning loop, not detection	Strong foundations exist; need fix→rule→accumulate workflow

Verdict

ELF: Conceptual inspiration only. Not an integration candidate. AutoGrep: Investigate for RFC-003 acceleration. claude-flow: Monitor as potential competitive threat if it expands to security.

---

Part 1: Competitive Landscape

1.1 Emergent Learning Framework (ELF)

Repository: Spacehunterz/Emergent-Learning-Framework_ELF Stars: ~170 Focus: Individual developer productivity

Capability	Implementation
Persistent Memory	SQLite at ~/.claude/emergent-learning
Pattern Tracking	Confidence 0.0→1.0, "Golden Rules" promotion
Multi-Agent	100+ personas via /swarm command
Cost Optimization	Haiku for monitoring, Opus for complex only
Session Continuity	PreToolUse/PostToolUse hooks

Architecture: Local-first, single-user, general-purpose coding assistance.

1.2 claude-flow (More Sophisticated Alternative)

Repository: ruvnet/claude-flow Focus: Enterprise-grade multi-agent orchestration

Capability	Implementation
Agent System	64 specialized agents with hive-mind coordination
MCP Tools	87-100 tools for swarm, memory, GitHub, analysis
Vector Search	96-164x faster with HNSW indexing
Memory	Hybrid AgentDB + ReasoningBank with SQLite
Performance	84.8% SWE-Bench solve rate

Why It Matters: If claude-flow expands into security domain, it's a more serious competitive threat than ELF. Its enterprise focus and sophisticated agent coordination could enable security-specific workflows.

1.3 AutoGrep (Directly Relevant)

Repository: lambdasec/autogrep Focus: Automated Semgrep rule generation from vulnerability patches

Capability	Implementation
Rule Generation	LLM-powered analysis of CVE patches
Quality Control	Embedding-based duplicate detection
Validation	Tests against known vulnerabilities
Data Source	MoreFixes dataset (CVE fix commits)
Licensing	Apache 2.0 (permissive)

Critical Relevance: AutoGrep implements exactly what RFC-003 proposes - generating Semgrep rules from vulnerability fixes using LLMs. This is directly applicable prior art that could accelerate RSOLV's learning roadmap.

---

Part 2: RSOLV Current State

2.1 What RSOLV Already Has (Production Code)

Component	Location	Lines	Description
Confidence Scoring	lib/rsolv/ast/confidence_scorer.ex	197	8-factor scoring system
Fallback Heuristics	lib/rsolv/ast/fallback_strategy.ex	967	Pattern detection when AST fails
Context Analyzer	lib/rsolv/ast/context_analyzer.ex	~400	Code context understanding
Test Scorer	lib/rsolv/ast/test_scorer.ex	~100	Integration suitability ranking
Security Patterns	lib/rsolv/security/patterns/	76+	Static vulnerability patterns

Confidence Scoring Detail

RSOLV's existing confidence scorer uses 8 contextual factors:

# From confidence_scorer.ex - PRODUCTION CODE
confidence =
  base_confidence
  |> adjust_for_ast_match(context)        # Exact vs partial match
  |> adjust_for_user_input(context)       # Input presence (+15% boost)
  |> adjust_for_framework_protection(context)  # Protection (0.4x)
  |> adjust_for_code_complexity(context)  # Low/high complexity
  |> adjust_for_language(context, language)    # Language-specific
  |> adjust_for_file_context(context)     # Test files (0.3x)
  |> adjust_for_severity(context)         # RCE boost (1.15x)
  |> adjust_for_taint_analysis(context)   # Sanitization (0.6x)

Base Confidence by Pattern Type:

• Remote Code Execution: 0.85
• Code/Command Injection: 0.80
• Hardcoded Secrets: 0.80
• SQL Injection: 0.75
• XSS: 0.70
• Weak Random: 0.60

This is more sophisticated than ELF's simpler confidence approach.

2.2 What RSOLV Lacks (RFC Stage Only)

Capability	RFC	Status	Gap
Semgrep Integration	RFC-003	Draft	No implementation
Dynamic Rule Generation	RFC-003	Draft	No implementation
Organizational Learning	RFC-050	Proposed	No implementation
Nx/ML Pattern Analysis	RFC-016	Proposed	No implementation
Cross-Fix Knowledge	RFC-050	Proposed	No implementation

Key Insight: RSOLV has strong detection foundations but lacks the learning loop that converts successful fixes into reusable rules.

2.3 Relevant RFCs (Corrected)

RFC	Title	Actual Focus
RFC-003	Learning Security System	Semgrep rule generation from fixes
RFC-014	Parallel AI Execution	Multi-approach fix generation (3-5 strategies)
RFC-016	Elixir/Nx Learning Engine	ML infrastructure for pattern analysis
RFC-050	Self-Improving Security Platform	Organizational intelligence accumulation
RFC-089	Multi-Model Fix Engine	GPT-5.2-Codex + Claude routing

Note: RFC-014 is about parallel fix generation, NOT learning. RFC-050 covers the self-improving platform vision.

---

Part 3: Strategic Analysis

3.1 ELF vs RSOLV: Fundamental Differences

Dimension	ELF	RSOLV
Target	Individual developers	Organizations
Storage	Local SQLite	Multi-tenant PostgreSQL
Security	Stores all locally	Client-side encryption, no code storage
Learning Domain	General coding patterns	Security vulnerabilities → Semgrep rules
Scale	Single machine	Distributed cloud platform
Output	Session context	Actionable security rules

Conclusion: Architectures are fundamentally incompatible. Direct integration is not feasible.

3.2 What ELF Validates

Despite incompatibility, ELF validates several concepts:

1. Tiered Model Usage Works: Haiku for routine, Opus for complex parallels RFC-089's GPT-5.2/Claude routing
2. Hook-Based Observation is Clean: PreToolUse/PostToolUse pattern is less invasive than full ML pipelines
3. Confidence Graduation is Effective: Repeated validation → promotion works for institutional knowledge
4. Local-First Has Trade-offs: Simplicity vs. multi-user/organization needs

3.3 What AutoGrep Offers

AutoGrep is directly relevant to RFC-003:

AutoGrep Workflow:
Vulnerability Patch → LLM Analysis → Semgrep Rule → Quality Filter → Validated Rule

RFC-003 Vision:
Security Fix → Generate Rule → Validate → Store → Scan Codebase → More Fixes

Potential Integration:

• AutoGrep's rule generation logic could accelerate RFC-003
• MoreFixes dataset provides training/validation data
• Quality filtering with embedding-based deduplication is proven
• Apache 2.0 license allows commercial use

---

Part 4: Opportunities

4.1 Extend Existing Confidence Scoring for Learning

RSOLV already has sophisticated detection scoring. Extend it to track fix outcomes:

# NEW: Track which confidence factors correlate with successful fixes
defmodule Rsolv.Learning.FixOutcomeTracker do
  def record_fix_outcome(vulnerability, fix, success) do
    %{
      pattern_type: vulnerability.pattern_type,
      initial_confidence: vulnerability.confidence_score,
      confidence_factors: vulnerability.confidence_factors,
      fix_approach: fix.approach,
      model_used: fix.model,
      success: success,
      timestamp: DateTime.utc_now()
    }
    |> store_for_analysis()
  end

  # Over time: identify which confidence factors predict fix success
  def analyze_success_patterns(org_id) do
    # Statistical analysis of what predicts good fixes
  end
end

4.2 Add Hook-Based Observation to RSOLV-action

Implement PreToolUse/PostToolUse pattern for fix generation:

// RSOLV-action: src/learning/hooks.ts
export class FixLearningHooks {
  // Before generating fix: query past successful approaches
  async preFix(vulnerability: Vulnerability): Promise<FixContext> {
    const history = await this.platform.get('/api/v1/learning/history', {
      pattern_type: vulnerability.type,
      language: vulnerability.language,
      limit: 5
    });

    return {
      successfulApproaches: history.filter(h => h.success),
      failedApproaches: history.filter(h => !h.success),
      suggestedModel: history.bestModel || 'claude'
    };
  }

  // After fix: record outcome for future learning
  async postFix(attempt: FixAttempt): Promise<void> {
    await this.platform.post('/api/v1/learning/record', {
      vulnerability_id: attempt.vulnerability.id,
      pattern_type: attempt.vulnerability.type,
      model: attempt.model,
      approach: attempt.approach,
      success: attempt.testsPass,
      duration_ms: attempt.duration
    });
  }
}

4.3 Investigate AutoGrep for RFC-003

AutoGrep could accelerate Semgrep rule generation:

AutoGrep Component	RFC-003 Application
Patch processor	Parse RSOLV fix diffs
LLM rule generator	Generate Semgrep patterns from fixes
Quality filter	Deduplicate/validate generated rules
Validation framework	Test rules against known vulnerabilities

Action: Clone AutoGrep, evaluate integration effort, assess licensing compatibility.

4.4 Persona-Based Prompting for Complex Fixes

Add specialized security personas without full swarm infrastructure:

// src/prompts/security-personas.ts
export const SECURITY_PERSONAS = {
  sql_injection: {
    role: "SQL Injection Prevention Specialist",
    expertise: ["Parameterized queries", "ORM patterns", "Input validation"],
    systemPrompt: `You specialize in SQL injection prevention...`
  },
  xss: {
    role: "XSS Prevention Specialist",
    expertise: ["Context-aware encoding", "CSP", "DOM sanitization"],
    systemPrompt: `You specialize in cross-site scripting prevention...`
  },
  // ... additional personas
};

---

Part 5: Risks

5.1 Competitive Threats

Threat	Likelihood	Impact	Mitigation
AutoGrep + fix engine = competitor	Medium	High	Move faster on RFC-003; consider acquisition/partnership
claude-flow enters security	Low	High	Monitor repository; differentiate on security depth
Semgrep adds learning	Medium	Medium	Semgrep Assistant already does remediation; they could add learning loop
ELF security fork	Low	Medium	Monitor forks; community engagement

5.2 Technical Risks

Risk	Description	Mitigation
Over-engineering	RFC-016's Nx/Scholar stack may be premature	Start with simpler learning loop, graduate to ML with data
Rule quality	Generated Semgrep rules may have false positives	Confidence thresholds, human review for high-impact rules
Storage scale	Per-org rule libraries could grow large	Deduplication, rule consolidation, archival policies

5.3 Strategic Risks

Risk	Description	Mitigation
Distraction	Learning features delay core security value	Ship learning as enhancement, not replacement
Complexity	Multi-model + learning + rules = complex system	Clear architecture boundaries, incremental rollout

---

Part 6: Recommendations

6.1 Immediate (This Week)

Action	Owner	Effort
Clone and evaluate AutoGrep	Engineering	1 day
Add fix outcome tracking to platform	Backend	2 days
Document existing confidence scoring as strength	Docs	1 day

6.2 Short-Term (Q1 2026)

Action	Priority	Effort
Implement hook-based observation in RSOLV-action	High	1 week
Add /api/v1/learning/* endpoints to platform	High	1 week
Prototype Semgrep rule generation (leverage AutoGrep patterns)	Medium	2 weeks
Add security personas to fix generation	Medium	3 days

6.3 Medium-Term (Q2-Q3 2026)

Action	Priority	Effort
Evaluate Nx/ML upgrade based on accumulated data	Medium	2 weeks
Build organization learning dashboard	Medium	2 weeks
A/B test learning-informed vs baseline fixes	High	3 weeks

6.4 What NOT to Do

1. Don't adopt ELF directly - Incompatible architectures
2. Don't rebuild confidence scoring - Already have sophisticated implementation
3. Don't start with full Nx/ML stack - Overkill before proving learning value
4. Don't ignore AutoGrep - Directly relevant prior art

---

Part 7: Summary

RSOLV's Position

Aspect	Status
Detection	Strong (76+ patterns, 8-factor confidence scoring)
Fix Generation	Strong (Claude Code SDK, RFC-089 multi-model)
Learning Loop	Gap (no fix→rule→accumulate workflow)
Organizational Memory	Gap (no cross-fix knowledge accumulation)

What to Learn from Each Framework

Framework	Key Lesson
ELF	Hook-based observation is clean and less invasive
claude-flow	Enterprise swarm coordination is sophisticated; monitor for security expansion
AutoGrep	LLM-based Semgrep rule generation from patches is proven

Final Verdict

ELF: Conceptual inspiration only. The tiered model and hook patterns validate RSOLV's direction, but RSOLV's existing confidence scoring is already more sophisticated. Not an integration candidate.

AutoGrep: High-priority investigation. Directly implements RFC-003's vision with Apache licensing. Potential accelerator for learning roadmap.

claude-flow: Strategic monitoring target. If it expands to security, it's a serious threat. Currently focused on general development.

RSOLV's Path Forward: Extend existing strengths (detection, confidence scoring) with learning loop (hook-based observation, fix outcome tracking, rule generation). Don't rebuild what exists; add what's missing.

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Appendix A: Source References

GitHub Repositories

• ELF: https://github.com/Spacehunterz/Emergent-Learning-Framework_ELF (~170 stars)
• claude-flow: https://github.com/ruvnet/claude-flow
• AutoGrep: https://github.com/lambdasec/autogrep

RSOLV RFCs

• RFC-003: Learning Security System
• RFC-014: Parallel AI Execution
• RFC-016: Elixir/Nx Learning Engine
• RFC-050: Self-Improving Security Platform
• RFC-089: Multi-Model Fix Engine

RSOLV Production Code

• lib/rsolv/ast/confidence_scorer.ex (197 lines)
• lib/rsolv/ast/fallback_strategy.ex (967 lines)
• lib/rsolv/ast/context_analyzer.ex (~400 lines)

External Documentation

• Claude Code Hooks Guide
• Semgrep Autofix Documentation
• AutoGrep Paper

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Appendix B: Corrections from Version 1.0

Original Claim	Correction
"RSOLV could learn from ELF's confidence scoring"	RSOLV already has more sophisticated 8-factor confidence scoring in production
RFC-014 mentioned as learning/self-improving	RFC-014 is Parallel AI Execution; RFC-050 is Self-Improving Platform
Only analyzed ELF	Added claude-flow (more capable) and AutoGrep (directly relevant)
Suggested implementing "Simple Learning MVP" with confidence scoring	Confidence scoring exists; gap is learning loop (fix→rule→accumulate)
Report was 2000+ words with repetitive sections	Consolidated to focused strategic analysis

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Report Version 2.0 - Revised 2026-01-02