11 plugins · 40 agents · 26 skills

Automate the entire dev workflow.
Not just the coding.

From task selection to merged PR. Works with Claude Code, OpenCode, and Codex CLI. 3,357 tests. Production-grade.

zsh

Paused

AI models 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 AI artifacts. Handling CI. Addressing reviewer comments. Deploying. awesome-slash automates all of it.

0 Plugins

0 Agents

0 Skills

0 Tests Passing

11 Commands. One Toolkit.

Each works standalone. Together, they automate everything.

/next-task

Task to production, fully automated

12-phase pipeline: discovery through deployment
Multi-agent review loop (code, security, perf, tests)
Persistent state -- resume from any phase
GitHub Issues, GitLab, or local task files

$ /next-task              # Start new workflow
$ /next-task --resume    # Resume interrupted workflow

/agnix

Lint agent configs before they break

155 validation rules across 28 categories
10+ AI tools: Claude Code, Cursor, Copilot, Codex, OpenCode, Gemini CLI
57 auto-fixable rules with --fix flag
SARIF output for GitHub Code Scanning

$ /agnix                # Validate current project
$ /agnix --fix          # Auto-fix fixable issues

/ship

Branch to merged PR in one command

Commits, pushes, creates PR, monitors CI
Waits for auto-reviewers, addresses every comment
Platform auto-detection (GitHub Actions, Railway, Vercel)
Merges, deploys, and cleans up

$ /ship                  # Full workflow
$ /ship --dry-run        # Preview without executing

/deslop

Kill AI slop before it ships

3-phase detection: regex, multi-pass analyzers, CLI tools
Certainty-graded findings (HIGH / MEDIUM / LOW)
JS/TS, Python, Rust, Go, Java
Auto-fix HIGH certainty issues

$ /deslop              # Report only (safe)
$ /deslop apply        # Fix HIGH certainty issues

/perf

Evidence-backed performance investigation

10-phase methodology with baselines and profiling
Hypothesis generation and controlled experiments
Breaking point analysis via binary search
Based on recorded real investigation sessions

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

/drift-detect

Find what's documented but not built

AST-based plan vs code semantic analysis
JavaScript collectors + single Opus call
77% token reduction vs multi-agent approaches
Tested on 1,000+ repositories

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

/audit-project

Multi-agent code review that iterates until clean

Up to 10 specialized agents per project
Security, performance, architecture, DB, API, frontend
Iterates until zero open issues remain
Auto-fixes all non-false-positive findings

$ /audit-project                # Full review
$ /audit-project --domain security  # Security only

/enhance

Analyze everything that shapes agent behavior

7 parallel analyzers for prompts, agents, plugins, docs
Certainty-graded findings with auto-fix support
Auto-learns false positives over time
Hooks and skills analysis included

$ /enhance                # Run all analyzers
$ /enhance --apply        # Apply HIGH certainty fixes

/repo-map

AST symbol and import mapping

Cached file-to-symbols map via ast-grep
Exports, functions, classes, import graph
Used by drift-detect and planners automatically
Incremental updates for large repos

$ /repo-map init       # First-time map generation
$ /repo-map update     # Incremental update

/sync-docs

Keep docs in sync with code

Finds outdated references and stale examples
Detects missing CHANGELOG entries
Version mismatch detection
Auto-fixes safe issues like version numbers

$ /sync-docs           # Check what needs updates
$ /sync-docs apply     # Apply safe fixes

/learn

Research any topic, build a learning guide

Progressive discovery: broad to specific to deep
Quality-scored sources (authority, recency, depth)
Structured guide with examples and best practices
RAG index for future agent lookups

$ /learn react hooks --depth=deep   # Comprehensive
$ /learn kubernetes --depth=brief    # Quick overview

How It Works

One approval. Fully autonomous execution.

Pick a task

Select from GitHub Issues, GitLab, or a local task file. The agent explores your codebase and designs a plan.

Approve the plan

Review the implementation plan. This is the last human interaction. Everything after is automated.

Watch it ship

Code, review, cleanup, documentation, PR, CI, merge. All handled. You review the result.

Built Different

Not another AI wrapper. Engineering-grade workflow automation.

Code does code work. AI does AI work.

Static analysis, regex, and AST for detection. LLMs only for synthesis and judgment. 77% fewer tokens than multi-agent approaches.

One agent, one job, done well

40 specialized agents, each with a narrow scope and clear success criteria. No agent tries to do everything.

Pipeline with gates

Each step must pass before the next begins. Can't push before review. Can't merge before CI. Hooks enforce it.

Validate plan and results

Approve the plan. See the results. The middle is automated. One approval unlocks autonomous execution.

40 Agents. 26 Skills.

Right model for the task. Opus reasons. Sonnet validates. Haiku executes.

exploration-agentopus

Deep codebase analysis and context gathering

planning-agentopus

Step-by-step implementation design

implementation-agentopus

Autonomous code writing and modification

perf-orchestratoropus

Performance investigation coordination

perf-analyzeropus

Deep performance analysis and profiling

learn-agentopus

Web research and learning guide creation

plan-synthesizeropus

Multi-source plan synthesis and merging

agent-enhanceropus

Agent configuration quality analysis

claudemd-enhanceropus

CLAUDE.md file optimization

docs-enhanceropus

Documentation quality improvement

hooks-enhanceropus

Git hooks and automation analysis

prompt-enhanceropus

Prompt engineering best practices

skills-enhanceropus

Skill definition quality analysis

26 Skills across 11 Plugins

next-task

orchestrate-review discover-tasks validate-delivery

enhance

enhance-agents enhance-claudemd enhance-docs enhance-hooks enhance-plugins enhance-prompts enhance-skills enhance-cross-file

perf

baseline benchmark profile theory-tester code-paths theory-gatherer investigation-logger perf-analyzer

other plugins

deslop drift-analysis repo-mapping sync-docs learn-topic agnix-lint

Get Started in 30 Seconds

Recommended

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

Interactive installer for Claude Code, OpenCode, and Codex CLI

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

Clone and install from source

$ git clone https://github.com/avifenesh/awesome-slash.git
$ cd awesome-slash
$ npm install

Automate the entire dev workflow. Not just the coding.

AI models write code.That's not the hard part anymore.