agentop-conventions

agentop-conventions is a skill for Claude Code, Codex from macromania/agentop. It costs 77 tokens per session (3,252 once invoked), scanned A, original, MIT.

Project-specific rules for building AgentOp, including its task hierarchy, AI work sessions, user approvals, and attention system.

In plain words
What is it for?
Use it when adding entities, changing task trees, handling agent sessions or steps, building approval flows, or implementing attention-related features.
Why use it?
It gives developers a shared model and structure for changing the core application without breaking how tasks, results, agent attempts, and approval steps relate to one another.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/macromania/agentop/agentop-conventions
Any agent
npx skills add macromania/agentop --skill agentop-conventions
Clone the repo
git clone --depth 1 https://github.com/macromania/agentop

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for agentop-conventions

README.md
[![agentmods](https://agentmods.dev/badge/skills/macromania/agentop/agentop-conventions.svg)](https://agentmods.dev/skills/macromania/agentop/agentop-conventions)
Your own site
<a href="https://agentmods.dev/skills/macromania/agentop/agentop-conventions"><img src="https://agentmods.dev/badge/skills/macromania/agentop/agentop-conventions.svg" alt="Measured on agentmods" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,252 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00077 $0.03252
Opus 5 $0.00039 $0.01626
Sonnet 5 $0.00015 $0.00650
Haiku 4.5 $0.00008 $0.00325

Measured 4d ago against content hash 4d3b04154faa, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agentop-conventions 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 4d 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.

.github/skills/agentop-conventions/SKILL.md · 530 lines

How it starts

The opening of the file, as written. The whole thing — 530 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AgentOp Project Conventions

Core domain model, attention system, and architectural patterns specific to AgentOp.

When to Use This Skill

  • Implementing features touching the core domain
  • Working with task hierarchies
  • Handling agent sessions and steps
  • Implementing the attention system
  • Building decision point flows
  • Adding new entity types

Core Data Model

Entity Relationships

Task (tree hierarchy via parentId)
  └── Outcome (many per task, represents expected results)
        └── AgentSession (many per outcome, AI execution attempts)
              └── AgentStep (stream of: thinking | action | decision_point)
                    └── DecisionPoint (user approval for sensitive actions)

Type Definitions

// packages/core/src/types.ts

// ============================================
// Enums
// ============================================

export type TaskStatus = 
  | 'pending'      // Not started
  | 'in_progress'  // Being worked on
  | 'completed'    // Successfully done
  | 'blocked';     // Waiting on dependency

export type AttentionType =
  | 'supervised'   // Every action needs approval
  | 'delegated'    // Runs autonomously
  | 'sensitive';   // Only sensitive actions need approval

export type SessionStatus =
  | 'pending'      // Queued to start
  | 'running'      // Currently executing
  | 'completed'    // Finished successfully
  | 'error'        // Failed with error
  | 'cancelled';   // User cancelled

export type StepType =
  | 'thinking'        // LLM reasoning output
  | 'action'          // Tool execution
  | 'decision_point'; // Awaiting user decision

export type DecisionStatus =
  | 'pending'    // Waiting for user
  | 'approved'   // User approved
  | 'denied'     // User denied
  | 'timeout';   // Auto-denied after timeout

// ============================================
// Entities
// ============================================

export interface Task {
  id: string;
  title: string;
  description?: string;
  parentId: string | null;  // null = root task
  status: TaskStatus;
  attentionType: AttentionType;
  orderIndex: number;       // Fractional index for drag reorder
  createdAt: Date;
  updatedAt: Date;
}

export interface Outcome {
  id: string;
  taskId: string;
  title: string;
  description?: string;
  status: TaskStatus;
  createdAt: Date;
  updatedAt: Date;
}

export interface AgentSession {
  id: string;
  outcomeId: string;
  status: SessionStatus;
  attentionType: AttentionType;
  startedAt: Date;
  completedAt?: Date;
  error?: string;
  tokenUsage?: {
    prompt: number;
    completion: number;
    total: number;
  };
}

export interface AgentStep {
  id: string;
  sessionId: string;
  type: StepType;
  status: 'in_progress' | 'completed' | 'error' | DecisionStatus;
  content?: string;         // For thinking steps
  toolCall?: ToolCall;      // For action steps
  result?: ToolResult;      // After tool execution
  createdAt: Date;
}

export interface ToolCall {
  id: string;
  name: string;
  arguments: Record<string, unknown>;
}

export interface ToolResult {
  success: boolean;
  result?: string;
  error?: string;
}

// ============================================
// Event Log (Immutable Audit Trail)
// ============================================

export interface EventLog {
  id: string;
  timestamp: Date;
  eventType: string;
  entityType: 'task' | 'outcome' | 'session' | 'step';
  entityId: string;
  payload: Record<string, unknown>;
  userId?: string;
}

Read the full file on GitHub · 530 lines

Changes

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.

  1. 4d ago First seen · 530 lines · 77 tokens per session scan A 4d3b04154faa

Subscribe to this mod's changes

agentop-conventions is a skill published in the GitHub repository macromania/agentop (10 stars, last pushed 5mo ago), licensed MIT. It adds 77 tokens to every session and 3,252 once invoked, about $0.0004 per session on Opus 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 2026-08-31.