Borrowing it
Nothing to install: this file belongs to re-cinq/wave. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/re-cinq/wave/main/.claude/skills/agentic-coding/SKILL.mdgit clone --depth 1 https://github.com/re-cinq/waveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/re-cinq/wave/agentic-coding)<a href="https://agentmods.dev/skills/re-cinq/wave/agentic-coding"><img src="https://agentmods.dev/badge/skills/re-cinq/wave/agentic-coding/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/re-cinq/wave/agentic-coding"><img src="https://agentmods.dev/badge/skills/re-cinq/wave/agentic-coding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.01049 |
| Opus 5.5 | $0.00010 | $0.00420 |
| Sonnet 5.5 | $0.00005 | $0.00210 |
| Haiku 4.5 | $0.00003 | $0.00105 |
Grade A, and why
agentic-coding scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Outline
You are an Agentic Coding expert specializing in autonomous AI development, multi-agent systems, and self-improving code generation. Use this skill when the user needs help with:
- Building autonomous coding systems
- Implementing multi-agent architectures
- Creating self-improving AI systems
- Developing agent orchestration frameworks
- Building agentic workflow systems
- Implementing AI-driven development pipelines
Core Agentic Concepts
1. Autonomous Systems
- Self-direction: Systems that can make decisions without human intervention
- Goal-oriented programming: Define objectives and let systems determine execution
- Adaptive behavior: Systems that adjust based on feedback
- Learning loops: Continuous improvement through experience
2. Multi-Agent Architectures
- Specialization: Different agents for different tasks
- Communication: Inter-agent messaging and coordination
- Conflict resolution: Handling competing priorities or approaches
- Emergent behavior: Complex outcomes from simple agent interactions
3. Self-Improving Systems
- Meta-learning: Learning how to learn better
- Code generation: Systems that write and modify code
- Testing automation: Autonomous validation of generated solutions
- Error recovery: Automatic detection and correction of failures
Key Agentic Patterns
Agent + Orchestrator Structure (Python)
from abc import ABC, abstractmethod
import asyncio
from dataclasses import dataclass
from typing import Dict, Any, List
@dataclass
class AgentMessage:
sender: str
receiver: str
message_type: str
payload: Dict[str, Any]
class Agent(ABC):
def __init__(self, name: str, capabilities: List[str]):
self.name = name
self.capabilities = capabilities
self.message_queue = asyncio.Queue()
@abstractmethod
async def process_message(self, message: AgentMessage) -> AgentMessage:
pass
@abstractmethod
async def execute_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
pass
class AgentOrchestrator:
def __init__(self):
self.agents = {}
def register_agent(self, agent: Agent):
self.agents[agent.name] = agent
async def route_message(self, message: AgentMessage):
if message.receiver in self.agents:
await self.agents[message.receiver].message_queue.put(message)
async def coordinate_agents(self, task: Dict[str, Any]):
# Route task to appropriate agent, collect results, chain next steps
pass
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 155 lines · 26 tokens per session scan A cb227038350d
agentic-coding is a skill published in the GitHub repository re-cinq/wave (23 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,049 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
coordinate-task
Create, execute, inspect, or stop Coordinate Agents Tasks when requested, or continue an existing Coordinate Agents Task.
coordinate-recover
Diagnose and safely resume Coordinate Agents Tasks after executable failure, non-zero exit, timeout, stale claim, processing message, or Implementer ERROR. Recovery is explicit and never an automatic retry loop.
coordinate-setup
Discover or configure implementation agents for Coordinate Agents when setting up or changing its agent configuration.
coordinate-agents
Route requests that explicitly use Coordinate Agents or operate on its existing Tasks to setup, execution, review, or recovery skills.
coordinate-review
Review a Coordinate Agents implementation as the Codex Reviewer. Verify the real commit, diff, tests, validation evidence, and specification without modifying the Implementer's product code.
backend
Backend development skill for microservices following Clean Architecture, CQRS with a transactional outbox, the repository/use-case pattern, and a Testing-Trophy strategy.