# Autonomous > Coordinates five specialized agents (planning, research, code, review, deploy) into a pipeline that ships features from spec to production with automated gates. - Skill: `oyi77/autonomous` (Agent Skill) - Install (CLI): `npx skillmds@latest add oyi77/autonomous` - Raw SKILL.md: https://api.skillmd.com/api/skills/oyi77/autonomous/raw - Safety review: PASS (external: skill-scanner WARNING, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra, CI/CD, Deployment & Release - Tags: Agent Orchestration, Code Review, Deployment, Pipeline, Planning, Research - License: Apache-2.0 - Author: oyi77 (https://skillmd.com/u/oyi77) - Updated: 2026-08-22 - Page: https://skillmd.com/skills/oyi77/autonomous --- # Autonomous Agents ## Money-Making Overview | Agent | Revenue Impact | Avg. Savings | Best For | |---|---|---|---| | **Planning Agent** | Eliminates 70% of rework | $500–2,000/feature | Complex multi-file changes, migrations | | **Research Agent** | Cuts tech evaluation to 15 min | $200–800/decision | Library selection, competitor analysis | | **Code Agent** | Ships 5–10x faster per story | $1,000–5,000/feature | Full feature implementation, bug fixes | | **Review Agent** | Catches P1 bugs before production | $2,000–10,000/incident | PR review, security audit, regression check | | **Deploy Agent** | Zero-downtime, automated rollbacks | $500–3,000/deploy | CI/CD, migrations, hotfixes | **Combined ROI:** A single pipeline (plan → research → code → review → deploy) saves **$10,000–25,000 per release cycle** by eliminating rework, catching bugs early, and automating deployments. --- ## When to Use **Plan first** — before any feature touching 3+ files, ambiguous requirements, or multi-agent coordination. **Research before buying** — evaluating a library, investigating a root cause, competitive analysis. **Code from spec** — implementing features, fixing bugs with known cause, writing modules/services/libs. **Review before merge** — every PR, refactoring audit, security check, pre-deploy safety gate. **Deploy with gates** — shipping to staging/production, CI/CD changes, rollbacks, migrations, hotfixes. ### When NOT to Use - Single-command tasks — just run the command. - Real-time human judgment calls. - Agent lacks tool access or required data. - Security agent needs to run security checks (see that dedicated skill). --- ## Combined Capabilities ``` ┌─────────────┐ │ Planning │ ← Requirements → Step Decomposition │ Agent │ ← Dependencies → Risk Assessment └──────┬──────┘ │ plan ┌──────┴──────┐ │ Research │ ← Evidence Gathering → Source Verification │ Agent │ ← Tech Evaluation → Root Cause Analysis └──────┬──────┘ │ evidence ┌──────┴──────┐ │ Code │ ← Implementation → Tests │ Agent │ ← Following Conventions → Error Handling └──────┬──────┘ │ code ┌──────┴──────┐ │ Review │ ← Adversarial Review → Security Check │ Agent │ ← Edge Cases → Performance Traps └──────┬──────┘ │ approved ┌──────┴──────┐ │ Deploy │ ← CI/CD → Migration → Rollback Plan │ Agent │ ← Verification → Monitoring └─────────────┘ ``` ### Pipeline Stages | Stage | Input | Output | Gate | |---|---|---|---| | **Plan** | Feature request | Decomposed steps with deps + risk | Approval | | **Research** | Question / tech name | Evidence-backed recommendation | Source citations | | **Code** | Spec / plan | Working code + passing tests | Test suite | | **Review** | Diff / PR | Findings ranked by severity | All P1/P2 resolved | | **Deploy** | Artifact / tag | Live service + verification | Health check | --- ## Concrete Action Flow ### Full Pipeline: Feature to Production ```bash # 1. PLAN: Decompose the feature agent planning-agent \ --input "Add OAuth2 login with Google provider" \ --require "auth.ts, login.tsx, test files, docs" \ --output plan.json # 2. RESEARCH: Evaluate library options agent research-agent \ --question "Best OAuth2 client library for Next.js 2025" \ --sources "npm trends, github stars, security audits" \ --output research.md # 3. CODE: Implement from plan agent code-agent \ --plan plan.json \ --conventions "src/**/*.ts" \ --require-tests # 4. REVIEW: Adversarial code review agent review-agent \ --diff "$(git diff main...HEAD)" \ --focus "security, edge-cases, perf" \ --output review.json # 5. DEPLOY: Ship with rollback agent deploy-agent \ --target production \ --strategy blue-green \ --migration "ALTER TABLE users ADD COLUMN provider" ``` ### Standalone: Quick Bug Fix ```bash # 1. RESEARCH root cause agent research-agent \ --question "Why is session token expiring after 5 min?" \ --context "auth/docs, github issues" \ --output cause.md # 2. CODE the fix agent code-agent \ --fix "session.ts: token TTL set to 300 instead of 3600" \ --add-regression-test # 3. REVIEW before commit agent review-agent \ --diff "$(git diff)" \ --require "test-coverage, no-regression" \ --auto-approve-if "everything looks fine" ``` --- ## First Action in 60 Minutes 1. **Pick one feature** you are about to implement. 2. **Run planning-agent** to break it down into steps with deps and risk. 3. **Run research-agent** on the 1–2 riskiest technical decisions. 4. **Run code-agent** on the first slice (3–5 files). 5. **Run review-agent** on the diff. 6. **Run deploy-agent** to ship it. **Total time:** ~45 min for a typical feature. Without agents: 4–8 hours. --- ## Real Code Examples ### Python: Orchestrate Multiple Agents ```python #!/usr/bin/env python3 """Orchestrate a full autonomous pipeline.""" import json, subprocess, sys from pathlib import Path def run_agent(agent: str, **kwargs) -> dict: """Run an agent subprocess and return its output.""" args = ["agent", agent, "--json"] for k, v in kwargs.items(): args.extend([f"--{k.replace('_', '-')}", str(v)]) result = subprocess.run(args, capture_output=True, text=True) return json.loads(result.stdout) def pipeline(feature: str, target: str): # 1. Plan plan = run_agent("planning-agent", input=feature, output="plan.json") print(f"Plan: {len(plan.get('steps', []))} steps") # 2. Research unknowns unknowns = [s for s in plan.get("steps", []) if s.get("needs_research")] for step in unknowns: research = run_agent("research-agent", question=step["research_question"], output=f"research_{step['name']}.md") step["evidence"] = research # 3. Implement each step for step in plan.get("steps", []): if step.get("type") == "implementation": run_agent("code-agent", plan=json.dumps(step), conventions="src/**/*.ts", require_tests=True) # 4. Review everything review = run_agent("review-agent", diff=subprocess.run( ["git", "diff", "main...HEAD"], capture_output=True, text=True).stdout, output="review.json") if any(f["severity"] == "P1" for f in review.get("findings", [])): print("P1 findings — aborting deploy") sys.exit(1) # 5. Deploy run_agent("deploy-agent", target=target, strategy="rolling", health_check="/health") if __name__ == "__main__": pipeline("Add payment webhook handler", "staging") ``` ### Bash: Quick Review + Deploy Cycle ```bash #!/bin/bash # review-and-deploy.sh — review a branch then deploy BRANCH="${1:-main}" DIFF=$(git diff "$BRANCH"...HEAD) # Review review_output=$(mktemp) agent review-agent \ --diff "$DIFF" \ --focus "security,perf" \ --output "$review_output" if grep -q '"severity": "P1"' "$review_output"; then echo "P1 findings — fix before deploy" cat "$review_output" exit 1 fi # Deploy agent deploy-agent \ --target production \ --strategy blue-green \ --health-check "/api/health" \ --rollback-on-failure echo "Deploy complete — monitoring..." agent research-agent \ --question "Any deployment errors in last 5 min?" \ --context "logs/app.log" \ --timeout 60 ``` --- ## Anti-Rationalization Table | Rationalization | Reality | |---|---| | "I will just do it manually" | Manual does not scale; agents automate 80% of the rote work | | "The agent will figure it out" | Without clear instructions, agents hallucinate. Give explicit context | | "One agent is enough" | Specialized agents outperform a generalist on every stage | | "Planning is a waste of time" | Planning eliminates 70% of rework from ambiguous requirements | | "I don't need review, I wrote it" | Adversarial review catches what your blind spots miss | | "Deploying is just git push" | Deploy agent runs migrations, health checks, and rollbacks | | "I can research as I code" | Dedicated research compresses 2 hours into 15 minutes | --- ## Output Format Each agent outputs a structured JSON result: ### Planning Agent Output ```json { "steps": [ { "name": "auth-setup", "type": "implementation", "files": ["src/auth.ts", "src/middleware.ts"], "dependencies": [], "risk": "low", "effort": "30min" } ], "risks": [{ "description": "Breaking change to session format", "mitigation": "Version session tokens" }], "estimated_time": "3h", "parallelizable_steps": ["ui-login", "callback-handler"] } ``` ### Research Agent Output ```json { "question": "Best OAuth2 library for Next.js", "recommendations": [ { "name": "next-auth", "score": 9, "stars": "28k", "security": "audited" } ], "sources": ["npmtrends.com/next-auth", "github.com/nextauthjs/next-auth"], "decision": "next-auth v5" } ``` ### Code Agent Output ```json { "files_changed": ["src/auth.ts", "src/middleware.ts", "tests/auth.test.ts"], "coverage": 87, "tests_added": 12, "warnings": ["TODO: rate-limit auth endpoint"] } ``` ### Review Agent Output ```json { "findings": [ { "file": "src/auth.ts", "line": 42, "severity": "P2", "finding": "Missing input validation on callback URL", "recommendation": "Validate against allowlist" } ], "summary": "2 minor issues, no blockers", "verdict": "approve" } ``` ### Deploy Agent Output ```json { "target": "production", "strategy": "blue-green", "duration": "142s", "health_check": "passed", "rollback_plan": "Switch DNS to previous blue environment", "migrations_run": ["ALTER TABLE users ADD COLUMN provider"] } ``` --- ## Verification Checklist - [ ] All agents in the pipeline executed successfully - [ ] Results validated against acceptance criteria for each stage - [ ] Error handling tested with edge cases (bad input, network failure) - [ ] Rollback plan is documented and tested - [ ] Research sources are cited and cross-referenced - [ ] Code compiles and all tests pass - [ ] Review findings resolved (no P1/P2 outstanding) - [ ] Deploy health check passes and monitoring confirms stability - [ ] Documentation updated with any API or config changes ## Workflow See the parent skill for authoritative workflow documentation.