[Pelis Agent Factory Advisor] Pelis Agent Factory Advisor Report: Opportunities to Enhance Agentic Workflows

[github-actions[bot]]

📊 Executive Summary

Current Maturity: Advanced (3/5) - The gh-aw-firewall repository demonstrates strong security focus and comprehensive issue management automation with 16 active agentic workflows. However, significant opportunities exist to add meta-agent observability, continuous quality improvements, and operational monitoring patterns inspired by Pelis Agent Factory best practices. Implementing P0 recommendations could elevate the repository to Expert-level (4-5/5) maturity.

Top Opportunities:

1. Add Workflow Metrics Collector - Track performance/cost across all 16 workflows (P0)
2. Implement Firewall Escape Tester - Automated security validation specific to firewall domain (P0)
3. Create Code Simplification Workflow - Continuous quality improvement for TypeScript codebase (P1)

---

🎓 Patterns Learned from Pelis Agent Factory

Key Discoveries

1. Workflow Specialization Philosophy

• Factory approach: Many specialized workflows > one monolithic agent
• Specialization reveals possibilities not obvious with general-purpose agents
• Each workflow has focused, specific purpose

2. Meta-Agent Pattern (Critical Missing Piece)

• Metrics Collector: Central nervous system tracking performance across agent ecosystem
• Portfolio Analyst: Identifies cost reduction opportunities ("chatty" agents)
• Audit Workflows: Meta-agent that audits other agents' runs
• Key Insight: "You can't optimize what you don't measure"

3. Continuous Quality Patterns

• Continuous Simplicity: Analyzes recent commits, creates PRs with simplifications
• Duplicate Code Detector: Uses semantic analysis (Serena) for intelligent duplication detection
• Focus: Trail behind developers, constantly sweeping and polishing

4. Security Workflow Layers

• Daily secrets analysis for exposed credentials
• Daily malicious code scan for suspicious patterns
• Static analysis reports combining multiple tools (zizmor, poutine, actionlint)
• Firewall testing to validate network rules

5. Safe Outputs Pattern

• Pre-approved write operations (create-issue, create-pull-request, add-comment)
• Allows agent actions without dangerous write permissions
• Constraints prevent abuse (max comments, title prefixes, label restrictions)

6. Cost Optimization Lessons

• Portfolio Analyst identifies unnecessarily expensive workflows
• Token usage and estimated cost tracking per run
• Cost-quality tradeoffs: Longer analyses aren't always better

Comparison with gh-aw-firewall

Strengths:

• ✅ Strong security focus (5 security workflows)
• ✅ Comprehensive CI/CD automation (CI Doctor, gap assessment)
• ✅ Advanced issue management (Issue Monster with sophisticated prioritization)
• ✅ Meta-awareness (Pelis Agent Factory Advisor workflow)

Gaps:

• ❌ No metrics collector or cost analysis
• ❌ No continuous quality workflows (simplification, refactoring)
• ❌ Missing domain-specific security testing (firewall escape scenarios)
• ❌ No operational monitoring (performance benchmarks, log analysis)

---

📋 Current Agentic Workflow Inventory

Workflow	Purpose	Trigger	Assessment
Security & Compliance
security-review.md	Comprehensive security review + threat modeling	Daily	✅ Excellent, 45min deep analysis
security-guard.md	Secret verification checks	On workflow_run	✅ Good pre-flight validation
dependency-security-monitor.md	CVE monitoring for dependencies	Daily	✅ Comprehensive (7640 lines)
container-scan.yml	Trivy container scanning	Regular	✅ Traditional YAML, good
codeql.yml	Static code analysis	Regular	✅ Traditional YAML, good
CI/CD & Quality
ci-doctor.md	CI failure investigation	On workflow failure	✅ Excellent, creates investigation issues
ci-cd-gaps-assessment.md	Identifies CI/CD gaps	Periodic	✅ Good meta-analysis
test-coverage-improver.md	Security-focused coverage PRs	Weekly	✅ Excellent, targeted approach
doc-maintainer.md	Documentation maintenance	Periodic	⚠️ Basic, could be enhanced
Issue Management
issue-monster.md	Assigns issues to Copilot	Hourly	✅ Excellent, sophisticated (19k lines)
issue-duplication-detector.md	Detects duplicate issues	On issue creation	✅ Good deduplication
plan.md	Creates implementation plans	On discussion	✅ Excellent planning workflow
pelis-agent-factory-advisor.md	Meta-analysis of workflows	Periodic	✅ Self-aware! (this workflow)
Release
release.md	Full release automation	On tag	✅ Comprehensive (17k lines)
update-release-notes.md	Updates release notes	Post-release	✅ Good automation
smoke-claude.md, smoke-copilot.md	AI engine smoke tests	On changes	✅ Good validation

Total: 16 agentic workflows (13 .md, 3 traditional YAML with security focus)

---

🚀 Actionable Recommendations

P0 - Implement Immediately

P0.1: Workflow Metrics Collector

What: Create a daily workflow that tracks performance, cost, and success rates across all 16 agentic workflows.

Why:

• Currently blind to workflow performance and costs
• Can't identify "chatty" workflows consuming excessive tokens
• No visibility into success/failure trends
• Can't optimize without measurement (Pelis Factory key lesson)

How:

---
on:
  schedule: daily
  workflow_dispatch:
permissions:
  contents: read
  actions: read
tools:
  agentic-workflows:
  github:
    toolsets: [actions]
  cache-memory: true
safe-outputs:
  create-issue:
    title-prefix: "[Metrics] "
timeout-minutes: 15
---

# Workflow Metrics Collector

Collect and analyze performance metrics for all 16 agentic workflows.

## Steps
1. Use `agentic-workflows.logs` to fetch last 7 days of runs
2. Aggregate metrics: token usage, cost estimates, duration, success rates
3. Store trends in cache-memory for historical comparison
4. Create issue if anomalies detected (cost spikes, failure increases)

## Output
- Per-workflow performance dashboard (stored in cache-memory)
- Alert issues for actionable problems

Effort: Low (2-4 hours)

• Uses existing agentic-workflows tool
• Simple aggregation logic
• Template from Pelis Factory Metrics Collector

Example: See Pelis Factory's Metrics Collector

---

P0.2: Firewall Escape Test Agent

What: Automated workflow testing firewall bypass scenarios specific to this security tool's domain.

Why:

• Security-critical tool needs continuous penetration testing
• Manual testing is infrequent and incomplete
• Regression prevention for firewall rule changes
• Validates iptables configuration, Squid ACLs, capability dropping

How:

---
on:
  schedule: daily
  workflow_dispatch:
  push:
    paths:
      - 'src/host-iptables.ts'
      - 'src/squid-config.ts'
      - 'containers/agent/setup-iptables.sh'
permissions:
  contents: read
  actions: read
tools:
  bash:
  github:
safe-outputs:
  create-discussion:
    title-prefix: "[Firewall Test] "
timeout-minutes: 30
---

# Firewall Escape Test Agent

Test firewall bypass scenarios and document findings.

## Test Scenarios
1. **Blocked domain access** - Verify denied domains are blocked
2. **Subdomain matching** - Test wildcard patterns
3. **DNS exfiltration** - Attempt DNS tunneling
4. **IPv6 bypass** - Test IPv6 filtering
5. **Protocol bypass** - Test non-HTTP protocols (FTP, SSH, etc.)
6. **Capability escape** - Verify NET_ADMIN drop is enforced
7. **localhost bypass** - Ensure container can't reach host

## Output
- Discussion with test results
- Pass/fail for each scenario
- Comparison with security-review.md findings

Effort: Medium (4-8 hours)

• Requires test script development
• Integration with existing firewall codebase
• Automated execution is straightforward

Example: Pattern from Pelis Factory Firewall workflow

---

P0.3: Portfolio Cost Analyzer

What: Weekly workflow analyzing workflow run costs and identifying optimization opportunities.

Why:

• AI agents aren't free - token usage adds up
• Some workflows may be unnecessarily expensive (calling LLM too many times)
• Early identification prevents runaway costs
• Pelis Factory discovered "chatty" agents costing money

How:

---
on:
  schedule: weekly
  workflow_dispatch:
permissions:
  contents: read
  actions: read
tools:
  agentic-workflows:
  cache-memory: true
safe-outputs:
  create-issue:
    title-prefix: "[Cost] "
timeout-minutes: 20
---

# Portfolio Cost Analyzer

Analyze workflow costs and identify optimization opportunities.

## Analysis
1. Fetch last 30 days of workflow runs
2. Calculate token usage and estimated cost per workflow
3. Compare with historical baselines (cache-memory)
4. Identify top 3 most expensive workflows
5. Suggest optimizations (reduce frequency, shorter context, etc.)

## Thresholds
- Alert if any workflow > $10/week
- Alert if total cost > 20% increase week-over-week

Effort: Low (2-3 hours)

• Uses existing agentic-workflows.logs with jq: '.summary.metrics'
• Simple cost calculation logic
• Template from Pelis Factory Portfolio Analyst

Example: Pelis Factory Portfolio Analyst

---

P1 - Plan for Near-Term

P1.1: Continuous Code Simplifier

What: Weekly workflow analyzing recently modified TypeScript code and creating PRs with simplifications.

Why:

• Technical debt accumulates during rapid development
• Manual refactoring is inconsistent
• Agents can catch complexity that humans miss (nested conditionals, verbose error handling)
• Aligned with "continuous AI" philosophy from Pelis Factory

How:

---
on:
  schedule: weekly
  workflow_dispatch:
  skip-if-match:
    query: 'is:pr is:open in:title "[Simplify]"'
    max: 1
permissions:
  contents: read
  actions: read
tools:
  bash:
  github:
safe-outputs:
  create-pull-request:
    draft: true
    title-prefix: "[Simplify] "
timeout-minutes: 20
---

# Continuous Code Simplifier

Analyze recently modified TypeScript code and propose simplifications.

## Focus Areas
- Extract repeated logic into helper functions
- Convert nested if-statements to early returns
- Simplify boolean expressions
- Use standard library functions over custom implementations
- Consolidate error handling patterns

## Scope
- Last 10 commits on main branch
- Only .ts files in src/
- One file per PR (focused changes)

Effort: Medium (4-6 hours)

• Requires TypeScript analysis logic
• PR creation with safe-outputs
• Testing simplifications don't break functionality

Example: Pelis Factory Automatic Code Simplifier

---

P1.2: Container Image CVE Monitor

What: Daily workflow scanning base Docker images (ubuntu:22.04, ubuntu/squid:latest) for new CVEs.

Why:

• Firewall runs in containers - container security is critical
• Base images get CVE updates regularly
• Proactive monitoring prevents security debt
• Complements existing Trivy scan (which only runs on built images)

How:

---
on:
  schedule: daily
  workflow_dispatch:
permissions:
  contents: read
tools:
  bash:
safe-outputs:
  create-issue:
    title-prefix: "[Container CVE] "
timeout-minutes: 10
---

# Container Image CVE Monitor

Scan base images for CVEs and create issues for critical vulnerabilities.

## Images to Scan
1. ubuntu:22.04 (agent container base)
2. ubuntu/squid:latest (Squid proxy base)

## Process
1. Pull latest images
2. Run Trivy scan with --severity HIGH,CRITICAL
3. Compare with previous scan results (cache-memory)
4. Create issue for new HIGH/CRITICAL CVEs
5. Include remediation guidance (update base image, apply patches, etc.)

Effort: Low (2-3 hours)

• Trivy already in use (container-scan.yml)
• Simple script to scan base images
• Issue creation straightforward

---

P1.3: Daily Secrets Scanner

What: Automated daily scan for exposed credentials in commits, discussions, and issues.

Why:

• Secrets leakage is common (API keys, tokens, etc.)
• Security-guard only runs on workflow failures
• Daily proactive scanning catches leaks early
• Pelis Factory pattern: Daily Secrets Analysis

How:

---
on:
  schedule: daily
  workflow_dispatch:
permissions:
  contents: read
  issues: read
  discussions: read
tools:
  bash:
  github:
safe-outputs:
  create-issue:
    title-prefix: "[Security Alert] "
    labels: [security, urgent]
timeout-minutes: 15
---

# Daily Secrets Scanner

Scan repository for exposed credentials and secrets.

## Scan Targets
1. Recent commits (last 24 hours)
2. New/updated discussions
3. New/updated issues and comments
4. Pull request descriptions

## Detection Patterns
- GitHub tokens (ghp_, gho_, etc.)
- AWS credentials
- Private keys
- API keys (common patterns)

## Action
- Create URGENT issue if secrets found
- Include redacted example and remediation steps

Effort: Medium (3-5 hours)

• Secret scanning patterns (regex or gitleaks)
• GitHub API integration for discussions/issues
• Alert creation

Example: Pelis Factory Daily Secrets Analysis

---

P1.4: Performance Benchmark Monitor

What: Weekly workflow running performance benchmarks for firewall overhead and creating trend issues.

Why:

• Performance regressions can creep in unnoticed
• Firewall overhead is a key metric for users
• Automated benchmarks provide objective data
• Trends over time reveal performance degradation

How:

---
on:
  schedule: weekly
  workflow_dispatch:
permissions:
  contents: read
  actions: read
tools:
  bash:
  cache-memory: true
safe-outputs:
  create-issue:
    title-prefix: "[Performance] "
timeout-minutes: 20
---

# Performance Benchmark Monitor

Run firewall performance benchmarks and track trends.

## Benchmarks
1. Startup time (container launch to ready state)
2. Request latency (HTTP/HTTPS overhead)
3. Throughput (requests per second)
4. Memory usage (container footprint)

## Process
1. Run benchmarks using existing scripts
2. Compare with historical baseline (cache-memory)
3. Calculate % change from last week
4. Alert if > 10% regression in any metric

Effort: Medium (4-6 hours)

• Benchmark script development
• Historical tracking in cache-memory
• Integration with existing test infrastructure

---

P2 - Consider for Roadmap

P2.1: Duplicate Code Detector with Semantic Analysis

What: Weekly workflow using semantic analysis (AST parsing) to detect code duplication in TypeScript.

Why:

• Textual diffing misses semantic duplication (same logic, different names)
• TypeScript AST provides rich semantic information
• Reduces technical debt through intelligent refactoring suggestions

How: Use ts-morph or TypeScript Compiler API for AST analysis, identify similar function signatures and logic blocks, create issues with refactoring suggestions.

Effort: High (8-12 hours) - Requires TypeScript AST expertise

Example: Pelis Factory Duplicate Code Detector (uses Serena)

---

P2.2: Example Usage Validator

What: Daily workflow extracting and testing all code examples from README, docs, and documentation site.

Why:

• Broken examples frustrate users
• Documentation drifts from implementation
• Manual testing is error-prone

How: Extract code blocks from markdown files, execute them in isolated environment, verify expected output/behavior, create issue if examples fail.

Effort: Medium (5-7 hours)

---

P2.3: API Documentation Generator

What: Weekly workflow auto-generating API documentation from TypeScript interfaces and JSDoc comments.

Why:

• API docs lag behind code changes
• TypeScript provides rich type information
• Automated generation ensures accuracy

How: Use TypeDoc or similar tool to generate docs from src/, commit updated docs to docs-site/, create PR if changes detected.

Effort: Medium (4-6 hours)

---

P2.4: Squid Log Analyzer

What: Daily workflow analyzing Squid access.log patterns for security insights and optimization opportunities.

Why:

• Logs contain valuable security intelligence
• Patterns reveal attack attempts or misconfigurations
• Automated analysis catches anomalies humans miss

How: Parse access.log from recent runs, identify: frequently blocked domains, unusual traffic patterns, potential exfiltration attempts, performance bottlenecks. Create issue if anomalies detected.

Effort: Medium (5-7 hours)

---

P2.5: Integration Test Gap Remediation

What: Monthly workflow that not only assesses gaps (already exists) but creates PRs to fill them.

Why:

• Current ci-cd-gaps-assessment.md only identifies gaps
• Closing the loop: identify → implement → verify
• Automated test generation saves developer time

How: Enhance existing workflow to generate test skeletons for identified gaps, create draft PR with TODO tests, assign to Issue Monster for completion.

Effort: High (8-12 hours) - Requires test generation logic

---

P3 - Future Ideas

P3.1: Community Health Reporter

What: Monthly workflow tracking issue response times, PR velocity, contributor activity.

Why: Maintain healthy community engagement, identify burnout risks for maintainers.

Effort: Low (2-3 hours)

---

P3.2: Knowledge Base Builder

What: Quarterly workflow aggregating common issues/questions into FAQ document.

Why: Reduce support burden by documenting frequent questions.

Effort: Medium (4-5 hours)

---

P3.3: Changelog Generator

What: Workflow generating user-facing CHANGELOG.md from conventional commits.

Why: Release notes are great, but CHANGELOG.md is standard for projects.

Effort: Low (2-3 hours) - Many tools available (conventional-changelog)

---

P3.4: Dependency Update Campaign

What: Quarterly orchestrated campaign updating dependencies with automated testing.

Why: Keep dependencies current, security patches applied, coordinated approach reduces conflicts.

Effort: High (10+ hours) - Campaign orchestration is complex

---

📈 Maturity Assessment

Current Level: Advanced (3/5)

Strengths:

• ✅ Strong security automation (5 dedicated workflows)
• ✅ Comprehensive CI/CD (failure investigation, gap assessment)
• ✅ Advanced issue management (sophisticated prioritization, deduplication)
• ✅ Meta-awareness (Pelis Agent Factory Advisor)
• ✅ Release automation (highlights generation, complete pipeline)

Gaps:

• ❌ No meta-agent observability layer (metrics, cost analysis)
• ❌ Missing continuous quality workflows (simplification, refactoring)
• ❌ No operational monitoring (performance, log analysis)
• ❌ Limited domain-specific testing (firewall escape scenarios)

Target Level: Expert (4-5/5)

Roadmap to Expert:

1. Add Observability Layer (P0.1, P0.3) - Metrics collector + cost analyzer
2. Domain-Specific Security (P0.2) - Firewall escape testing
3. Continuous Quality (P1.1) - Code simplification workflow
4. Operational Monitoring (P1.2, P1.4) - CVE monitoring + performance benchmarks
5. Proactive Security (P1.3) - Daily secrets scanning

Timeline:

• Phase 1 (Weeks 1-2): Implement P0 recommendations (3 workflows)
• Phase 2 (Weeks 3-6): Implement P1 recommendations (4 workflows)
• Phase 3 (Months 2-3): Consider P2 recommendations based on impact

Gap Analysis

Area	Current	Target	Priority Actions
Security	4/5	5/5	Add firewall escape tester (P0.2), daily secrets scan (P1.3)
Observability	1/5	5/5	Add metrics collector (P0.1), cost analyzer (P0.3)
Quality	2/5	4/5	Add code simplifier (P1.1), duplicate detector (P2.1)
Operations	2/5	4/5	Add performance monitor (P1.4), CVE monitor (P1.2)
Documentation	3/5	4/5	Add example validator (P2.2), API doc gen (P2.3)

---

🔄 Comparison with Best Practices

What gh-aw-firewall Does Well

1. Security-First Approach ✅

   • Multiple security workflows at different layers
   • Security-focused test coverage improver
   • Daily comprehensive security review (45min)
   • Better than: Many repos with only basic security scanning

2. Advanced Issue Management ✅

   • Issue Monster with sophisticated prioritization (19k lines)
   • Parent/child issue awareness (campaign pattern)
   • Sibling PR coordination to prevent conflicts
   • Better than: Basic issue triage agents

3. Meta-Awareness ✅

   • Pelis Agent Factory Advisor (this workflow!)
   • CI/CD gap assessment
   • Self-improving system
   • At parity with: Pelis Factory meta-agent philosophy

What Could Be Improved

1. Missing Observability ⚠️

   • No metrics collector tracking all workflows
   • No cost analysis identifying expensive agents
   • No audit workflow for quality control
   • Recommendation: Implement P0.1 and P0.3 immediately

2. No Continuous Quality ⚠️

   • No code simplification workflow
   • No duplicate code detection
   • No continuous refactoring
   • Recommendation: Start with P1.1 (code simplifier)

3. Limited Operational Monitoring ⚠️

   • No performance benchmarking
   • No log analysis automation
   • No trend tracking over time
   • Recommendation: Implement P1.2 and P1.4

Unique Opportunities (Firewall/Security Domain)

This repository has unique opportunities given its security focus:

1. Firewall Escape Testing (P0.2) 🎯

   • Domain-specific security testing
   • Validates core product functionality
   • Continuous penetration testing
   • Unique to firewall tools - standard Pelis Factory patterns don't cover this

2. Network Pattern Analysis 🎯

   • Analyze Squid logs for attack patterns
   • Detect exfiltration attempts
   • Optimize domain whitelists based on usage
   • Leverages unique data - access logs provide security intelligence

3. Container Security Focus 🎯

   • Daily CVE monitoring of base images (P1.2)
   • Capability drop validation
   • Seccomp profile analysis
   • Critical for container-based security tool

4. Performance vs. Security Tradeoffs 🎯

   • Benchmark firewall overhead (P1.4)
   • Identify performance regressions that might lead users to disable firewall
   • Balance security with usability
   • Unique constraint - firewalls must be fast AND secure

---

📝 Notes for Future Runs

Stored in /tmp/gh-aw/cache-memory/

Patterns Observed (2026-01-22)

1. Strong Security Foundation: Repository already has comprehensive security workflows, more than typical projects
2. Missing Meta-Layer: No metrics/cost tracking despite having 16 workflows running regularly
3. Issue Management Excellence: Issue Monster is sophisticated with parent/child awareness
4. CI Doctor Well-Implemented: Good pattern for failure investigation with domain knowledge
5. Release Automation Mature: Comprehensive release workflow with AI-generated highlights

Recommendations Tracking

Implemented: (none yet - first run)

In Progress: (none yet)

High-Value Quick Wins:

• P0.1 Workflow Metrics Collector (2-4 hours, high impact)
• P0.3 Portfolio Cost Analyzer (2-3 hours, prevents runaway costs)
• P1.2 Container Image CVE Monitor (2-3 hours, critical for security tool)

Repository Evolution Notes

• 2026-01-22: 16 agentic workflows, strong security focus, missing observability layer
• Watch for: Cost increases as workflows scale, performance regressions in firewall overhead
• Next review: Check if P0 recommendations were implemented, measure impact on workflow visibility

Workflow Health Indicators

Current snapshot to track over time:

• Total workflows: 16 (13 agentic .md + 3 security YAML)
• Weekly runs: ~50-60 estimated (Issue Monster hourly, others daily/weekly)
• Cost visibility: ❌ None (P0.1 addresses this)
• Failure investigation: ✅ Good (CI Doctor)
• Meta-analysis: ✅ Exists (this workflow)
• Cost analysis: ❌ Missing (P0.3 addresses this)

---

🎯 Closing Thoughts

The gh-aw-firewall repository is well ahead of most projects in agentic workflow maturity, particularly in security automation and issue management. The missing piece is the observability layer - the "central nervous system" that tracks workflow health, cost, and effectiveness.

Start with P0 recommendations to gain visibility into the workflow ecosystem. Once you can measure and monitor, the path to optimization becomes clear. The Pelis Agent Factory lesson "you can't optimize what you don't measure" is especially relevant here.

Domain-specific opportunities around firewall testing and network pattern analysis represent genuine innovations beyond standard Pelis Factory patterns. These leverage the unique data and requirements of a security tool.

Estimated ROI:

• P0 implementations (8-12 hours total): High visibility, cost control, security validation
• P1 implementations (18-26 hours total): Quality improvements, operational monitoring
• Total time to Expert level: ~30-40 hours over 4-6 weeks

The factory is already impressive - these enhancements will make it world-class. 🚀

---

Generated by Pelis Agent Factory Advisor
Based on analysis of Pelis Agent Factory documentation and agentics repository

AI generated by Pelis Agent Factory Advisor

• expires on Jan 29, 2026, 9:01 PM UTC

[github-actions[bot]]

This discussion was automatically closed because it expired on 2026-01-29T21:01:09.220Z.