Automate the entire dev workflow. Not just the coding.

11 plugins · 40 agents · 26 skills · 26k lines of lib code · 3,357 tests · 3 platforms

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AI models can write code. That's not the hard part anymore. The hard part is everything else—picking what to work on, managing branches, reviewing output, cleaning up artifacts, handling CI, addressing comments, deploying. awesome-slash automates the entire workflow, not just the coding.

Building custom skills, agents, hooks, or MCP tools? agnix is the CLI + LSP linter that catches config errors before they fail silently - real-time IDE validation, auto suggestions, auto-fix, and 155 rules for Cursor, Claude Code, Cline, Copilot, Codex, Windsurf, and more.

Production-ready skills, agents, and commands for Claude Code, OpenCode, and Codex CLI.

Install the plugins → get the skills → your agents become more capable. Each piece was built to work with the others. The whole system is E2E tested.

From messy project to clean codebase. From drifted plan to focused execution. From task to merged PR.

Works with: Claude Code · OpenCode · Codex CLI

Code does code work. AI does AI work.

• Detection: regex, AST analysis, static analysis—fast, deterministic, no tokens wasted
• Judgment: LLM calls for synthesis, planning, review—where reasoning matters
• Result: 77% fewer tokens for /drift-detect vs multi-agent approaches, certainty-graded findings throughout

Certainty levels exist because not all findings are equal:

This came from testing on 1,000+ repositories.

Each command works standalone. Together, they form complete workflows.

26 skills included across the plugins:

Skills give your agents specialized capabilities. When you install a plugin, its skills become available to all agents in that session.

Purpose: Complete task-to-production automation.

What happens when you run it:

1. Policy Selection - Choose task source (GitHub issues, GitLab, local file), priority filter, stopping point
2. Task Discovery - Shows top 5 prioritized tasks, you pick one
3. Worktree Setup - Creates isolated branch and working directory
4. Exploration - Deep codebase analysis to understand context
5. Planning - Designs implementation approach
6. User Approval - You review and approve the plan (last human interaction)
7. Implementation - Executes the plan
8. Pre-Review - Runs deslop-agent and test-coverage-checker
9. Review Loop - Multi-agent review iterates until clean
10. Delivery Validation - Verifies tests pass, build passes, requirements met
11. Docs Update - Updates CHANGELOG and related documentation
12. Ship - Creates PR, monitors CI, addresses comments, merges

Phase 9 uses the orchestrate-review skill to spawn parallel reviewers (code quality, security, performance, test coverage) plus conditional specialists.

Agents involved:

Cross-plugin agent:

Usage:

/next-task              # Start new workflow
/next-task --resume     # Resume interrupted workflow
/next-task --status     # Check current state
/next-task --abort      # Cancel and cleanup

Full workflow documentation →

Purpose: Lint agent configurations before they break your workflow. The first dedicated linter for AI agent configs.

agnix is a standalone open-source project that provides the validation engine. This plugin integrates it into your workflow.

The problem it solves:

Agent configurations are code. They affect behavior, security, and reliability. But unlike application code, they have no linting. You find out your SKILL.md is malformed when the agent fails. You discover your hooks have security issues when they're exploited. You realize your CLAUDE.md has conflicting rules when the AI behaves unexpectedly.

agnix catches these issues before they cause problems.

What it validates:

155 validation rules (57 auto-fixable) derived from:

• Official tool specifications (Claude Code, Cursor, GitHub Copilot, Codex CLI, OpenCode, Gemini CLI, and more)
• Research papers on agent reliability and prompt injection
• Real-world testing across 500+ repositories
• Community-reported issues and edge cases

Supported files:

CI/CD Integration:

agnix outputs SARIF format for GitHub Code Scanning. Add it to your workflow:

- name: Lint agent configs
  run: agnix --format sarif > results.sarif
- uses: github/codeql-action/upload-sarif@v3
  with:
    sarif_file: results.sarif

Usage:

/agnix                       # Validate current project
/agnix --fix                 # Auto-fix fixable issues
/agnix --strict              # Treat warnings as errors
/agnix --target claude-code  # Only Claude Code rules
/agnix --format sarif        # Output for GitHub Code Scanning

Agent: agnix-agent (sonnet model)

External tool: Requires agnix CLI

npm install -g agnix         # Install via npm
# or
cargo install agnix-cli      # Install via Cargo
# or
brew install agnix           # Install via Homebrew (macOS)

Why use agnix:

• Catch config errors before they cause agent failures
• Enforce security best practices across your team
• Maintain consistency as your agent configs grow
• Integrate validation into CI/CD pipelines
• Support multiple AI tools from one linter

Purpose: Takes your current branch from "ready to commit" to "merged PR."

What happens when you run it:

1. Pre-flight - Detects CI platform, deployment platform, branch strategy
2. Commit - Stages and commits with generated message (if uncommitted changes)
3. Push & PR - Pushes branch, creates pull request
4. CI Monitor - Waits for CI, retries on transient failures
5. Review Wait - Waits 3 minutes for auto-reviewers (Copilot, Claude, Gemini, Codex)
6. Address Comments - Handles every comment from every reviewer
7. Merge - Merges when all comments resolved and CI passes
8. Deploy - Deploys and validates (if multi-branch workflow)
9. Cleanup - Removes worktree, closes issue, deletes branch

Platform Detection:

Review Comment Handling:

Every comment gets addressed. No exceptions. The workflow categorizes comments and handles each:

• Code fixes get implemented
• Style suggestions get applied
• Questions get answered
• False positives get explained

If something can't be fixed, the workflow replies explaining why and resolves the thread.

Usage:

/ship                       # Full workflow
/ship --dry-run             # Preview without executing
/ship --strategy rebase     # Use rebase instead of squash

Full workflow documentation →

Purpose: Finds AI slop—debug statements, placeholder text, verbose comments, TODOs—and removes it.

How detection works:

Three phases run in sequence:

1. Phase 1: Regex Patterns (HIGH certainty)

   • console.log, print(), dbg!(), println!()
   • // TODO, // FIXME, // HACK
   • Empty catch blocks, disabled linters
   • Hardcoded secrets (API keys, tokens)

2. Phase 2: Multi-Pass Analyzers (MEDIUM certainty)

   • Doc-to-code ratio (excessive comments)
   • Verbosity ratio (AI preambles)
   • Over-engineering patterns
   • Buzzword inflation
   • Dead code detection
   • Stub functions

3. Phase 3: CLI Tools (LOW certainty, optional)

   • jscpd, madge, escomplex (JS/TS)
   • pylint, radon (Python)
   • golangci-lint (Go)
   • clippy (Rust)

Languages supported: JavaScript/TypeScript, Python, Rust, Go, Java

Usage:

/deslop              # Report only (safe)
/deslop apply        # Fix HIGH certainty issues
/deslop apply src/ 10  # Fix 10 issues in src/

Thoroughness levels:

• quick - Phase 1 only (fastest)
• normal - Phase 1 + Phase 2 (default)
• deep - All phases if tools available

Pattern reference →

Purpose: Structured performance investigation with baselines, profiling, and evidence-backed decisions.

10-phase methodology (based on recorded real performance investigation sessions):

1. Setup - Confirm scenario, success criteria, benchmark command
2. Baseline - 60s minimum runs, PERF_METRICS markers required
3. Breaking Point - Binary search to find failure threshold
4. Constraints - CPU/memory limits, measure delta vs baseline
5. Hypotheses - Generate up to 5 hypotheses with evidence and confidence
6. Code Paths - Use repo-map to identify entrypoints and hot files
7. Profiling - Language-specific tools (--cpu-prof, JFR, cProfile, pprof)
8. Optimization - One change per experiment, 2+ validation passes
9. Decision - Continue or stop based on measurable improvement
10. Consolidation - Final baseline, evidence log, investigation complete

Agents and skills:

Usage:

/perf                 # Start new investigation
/perf --resume        # Resume previous investigation

Phase flags (advanced):

/perf --phase baseline --command "npm run bench" --version v1.2.0
/perf --phase breaking-point --param-min 1 --param-max 500
/perf --phase constraints --cpu 1 --memory 1GB
/perf --phase hypotheses --hypotheses-file perf-hypotheses.json
/perf --phase optimization --change "reduce allocations"
/perf --phase decision --verdict stop --rationale "no measurable improvement"

Purpose: Compares your documentation and plans to what's actually in the code.

The problem it solves:

Your roadmap says "user authentication: done." But is it actually implemented? Your GitHub issue says "add dark mode." Is it already in the codebase? Plans drift from reality. This command finds the drift.

How it works:

1. JavaScript collectors gather data (fast, token-efficient)

   • GitHub issues and their labels
   • Documentation files
   • Actual code exports and implementations

2. Single Opus call performs semantic analysis

   • Matches concepts, not strings ("user auth" matches auth/, login.js, session.ts)
   • Identifies implemented but not documented
   • Identifies documented but not implemented
   • Finds stale issues that should be closed

Why this approach:

Multi-agent collection wastes tokens on coordination. JavaScript collectors are fast and deterministic. One well-prompted LLM call does the actual analysis. Result: 77% token reduction vs multi-agent approaches.

Tested on 1,000+ repositories before release.

Usage:

/drift-detect              # Full analysis
/drift-detect --depth quick  # Quick scan

Purpose: Multi-agent code review that iterates until issues are resolved.

What happens when you run it:

Up to 10 specialized role-based agents run based on your project:

Findings are collected and categorized by severity (critical/high/medium/low). All non-false-positive issues get fixed automatically. The loop repeats until no open issues remain.

Usage:

/audit-project                   # Full review
/audit-project --quick           # Single pass
/audit-project --resume          # Resume from queue file
/audit-project --domain security # Security focus only
/audit-project --recent          # Only recent changes

Agent reference →

Purpose: Analyzes your prompts, plugins, agents, docs, hooks, and skills for improvement opportunities.

Seven analyzers run in parallel:

Each finding includes:

• Certainty level (HIGH/MEDIUM/LOW)
• Specific location (file:line)
• What's wrong
• How to fix it
• Whether it can be auto-fixed

Auto-learning: Detects obvious false positives (pattern docs, workflow gates) and saves them for future runs. Reduces noise over time without manual suppression files.

Usage:

/enhance                    # Run all analyzers
/enhance --focus=agent      # Just agent prompts
/enhance --apply            # Apply HIGH certainty fixes
/enhance --show-suppressed  # Show what's being filtered
/enhance --no-learn         # Analyze but don't save false positives

Purpose: Builds an AST-based map of symbols and imports for fast repo analysis.

What it generates:

• Cached file→symbols map (exports, functions, classes)
• Import graph for dependency hints

Output is cached at {state-dir}/repo-map.json and exposed via the MCP repo_map tool.

Why it matters:

Tools like /drift-detect and planners can use the map instead of re-scanning the repo every time.

Usage:

/repo-map init        # First-time map generation
/repo-map update      # Incremental update
/repo-map status      # Check freshness

Required: ast-grep (sg) must be installed.

Purpose: Sync documentation with actual code changes—find outdated refs, update CHANGELOG, flag stale examples.

The problem it solves:

You refactor auth.js into auth/index.js. Your README still says import from './auth'. You rename a function. Three docs still reference the old name. You ship a feature. CHANGELOG doesn't mention it. Documentation drifts from code. This command finds the drift.

What it detects:

Auto-fixable vs flagged:

Usage:

/sync-docs              # Check what docs need updates (safe)
/sync-docs apply        # Apply safe fixes
/sync-docs report src/  # Check docs related to src/
/sync-docs --all        # Full codebase scan

Purpose: Research any topic online and create a comprehensive learning guide with RAG-optimized indexes.

What it does:

1. Progressive Discovery - Uses funnel approach (broad → specific → deep) to find quality sources
2. Quality Scoring - Scores sources by authority, recency, depth, examples, uniqueness
3. Just-In-Time Extraction - Fetches only high-scoring sources to save tokens
4. Synthesis - Creates structured learning guide with examples and best practices
5. RAG Index - Updates CLAUDE.md/AGENTS.md master index for future lookups
6. Enhancement - Runs enhance:enhance-docs and enhance:enhance-prompts

Depth levels:

Output structure:

agent-knowledge/
  CLAUDE.md                    # Master index (updated each run)
  AGENTS.md                    # Index for OpenCode/Codex
  recursion.md                 # Topic-specific guide
  resources/
    recursion-sources.json     # Source metadata with quality scores

Usage:

/learn recursion                    # Default (20 sources)
/learn react hooks --depth=deep     # Comprehensive (40 sources)
/learn kubernetes --depth=brief     # Quick overview (10 sources)
/learn python async --no-enhance    # Skip enhancement pass

Agent: learn-agent (opus model for research quality)

Standalone use:

/deslop apply          # Just clean up your code
/sync-docs             # Just check if docs need updates
/ship                  # Just ship this branch
/audit-project         # Just review the codebase

Integrated workflow:

When you run /next-task, it orchestrates everything:

/next-task picks task → explores codebase → plans implementation
    ↓
implementation-agent writes code
    ↓
deslop-agent cleans AI artifacts
    ↓
Phase 9 review loop iterates until approved
    ↓
delivery-validator checks requirements
    ↓
sync-docs-agent syncs documentation
    ↓
[/ship](#ship) creates PR → monitors CI → merges

The workflow tracks state so you can resume from any point.

/plugin marketplace add avifenesh/awesome-slash
/plugin install next-task@awesome-slash
/plugin install ship@awesome-slash

npm install -g awesome-slash && awesome-slash

Interactive installer for Claude Code, OpenCode, and Codex CLI.

# Non-interactive install
awesome-slash --tool claude              # Single tool
awesome-slash --tools "claude,opencode"  # Multiple tools
awesome-slash --development              # Dev mode (bypasses marketplace)

Full installation guide →

The shared library is published as @awesome-slash/lib for direct use:

npm install @awesome-slash/lib

const { platform, patterns, state, sources, xplat } = require('@awesome-slash/lib');

Zero dependencies, CommonJS, Node.js >= 18.

Required:

• Git
• Node.js 18+

For GitHub workflows:

• GitHub CLI (gh) authenticated

For GitLab workflows:

• GitLab CLI (glab) authenticated

For /repo-map:

• ast-grep (sg) installed

For /agnix:

• agnix CLI installed (cargo install agnix-cli or brew install agnix)

Local diagnostics (optional):

npm run detect   # Platform detection (CI, deploy, project type)
npm run verify   # Tool availability + versions

This project is built on research, not guesswork.

Knowledge base (agent-docs/): 8,000 lines of curated documentation from Anthropic, OpenAI, Google, and Microsoft covering:

• Agent architecture and design patterns
• Prompt engineering techniques
• Function calling and tool use
• Context efficiency and token optimization
• Multi-agent systems and orchestration
• Instruction following reliability

Testing:

• 1,818 tests passing
• Drift-detect validated on 1,000+ repositories
• E2E workflow testing across all commands
• Cross-platform validation (Claude Code, OpenCode, Codex CLI)

Methodology:

• /perf investigation phases based on recorded real performance investigation sessions
• Certainty levels derived from pattern analysis across repositories
• Token optimization measured and validated (77% reduction in drift-detect)

• Issues: github.com/avifenesh/awesome-slash/issues
• Discussions: github.com/avifenesh/awesome-slash/discussions

MIT License | Made by Avi Fenesh