osop

OSOP workflow authoring, validation, risk analysis, and self-optimization for AI agents

3,891 stars

Best use case

osop is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

OSOP workflow authoring, validation, risk analysis, and self-optimization for AI agents

Teams using osop should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/osop/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/archie0125/osop/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/osop/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How osop Compares

Feature / AgentosopStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

OSOP workflow authoring, validation, risk analysis, and self-optimization for AI agents

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# OSOP — Open Standard Operating Procedures

Universal protocol for defining, validating, risk-assessing, and executing process workflows. Works with any AI coding agent.

## Capabilities

When this skill is active, the agent can:

- **Create** OSOP workflow definitions from natural language
- **Validate** workflow YAML against the OSOP schema
- **Risk Assess** workflows for security issues, permission gaps, destructive commands
- **Execute** workflows with dry-run mode for safety
- **Render** workflows as Mermaid diagrams
- **Optimize** workflows using execution history — detect slow steps, failure hotspots
- **Convert** between OSOP and external formats (GitHub Actions, BPMN, Airflow)
- **Report** on workflow executions as standalone HTML

## Sub-Skills

This pack includes 4 specialized skills:

| Skill | Command | What it does |
|-------|---------|-------------|
| `osop-log` | `/osop-log` | Record a structured session log after completing work |
| `osop-report` | `/osop-report` | Convert .osop + .osoplog.yaml to HTML report |
| `osop-review` | `/osop-review` | Security & risk analysis of workflows |
| `osop-optimize` | `/osop-optimize` | Improve workflows from execution history |

## OSOP Node Types (v1.0)

16 supported node types in 4 categories:

**Actors:** `human`, `agent`, `company`, `department`
**Technical:** `api`, `cli`, `db`, `git`, `docker`, `cicd`, `infra`, `mcp`
**Flow Control:** `system`, `event`, `gateway`, `data`

Use `subtype` for domain specialization (e.g., `type: agent, subtype: llm`).

## Edge Modes

13 modes: `sequential`, `conditional`, `parallel`, `loop`, `event`, `fallback`, `error`, `timeout`, `compensation`, `message`, `dataflow`, `signal`, `weighted`, `spawn`.

## Quick Start

### 1. Define a workflow

```yaml
osop_version: "1.0"
id: "deploy-staging"
name: "Deploy to Staging"
description: "Build, test, and deploy to staging environment."
version: "1.0.0"
tags: [deploy, staging]

nodes:
  - id: "build"
    type: "cli"
    name: "Build Project"
    description: "Run the build command"
  - id: "test"
    type: "cicd"
    subtype: "test"
    name: "Run Tests"
    description: "Execute test suite"
  - id: "deploy"
    type: "infra"
    name: "Deploy to Staging"
    description: "Push to staging environment"
    security:
      risk_level: "medium"
      approval_gate: true

edges:
  - from: "build"
    to: "test"
    mode: "sequential"
  - from: "test"
    to: "deploy"
    mode: "conditional"
    when: "tests.passed == true"
```

### 2. Review for risks

Run `/osop-review deploy-staging.osop` to check for security issues before execution.

### 3. Execute and log

After running, use `/osop-log` to record what happened as a structured `.osoplog.yaml`.

### 4. Generate report

Run `/osop-report` to create a standalone HTML report with dark mode and expandable nodes.

## Session Logging

After completing multi-step tasks, this skill produces:
- **`.osop`** — workflow definition (what should happen)
- **`.osoplog.yaml`** — execution record (what actually happened)

These files can be visualized at https://osop-editor.vercel.app or converted to HTML.

## Security Metadata

Nodes can declare `security.risk_level` (low/medium/high/critical), `security.permissions`, `security.secrets`, and `approval_gate` for risk analysis.

## Self-Optimization Loop

1. Execute workflow → produce `.osoplog`
2. Aggregate stats from past runs
3. Run `/osop-optimize` → get improvement suggestions
4. Apply approved changes → execute improved workflow
5. Repeat — the SOP gets better each iteration

## Links

- **Spec:** https://github.com/Archie0125/osop-spec
- **Visual Editor:** https://osop-editor.vercel.app
- **Examples:** https://github.com/Archie0125/osop-examples
- **Website:** https://osop.ai

Related Skills

osop-review

3891
from openclaw/skills

Review .osop/.osoplog for security risks, permission gaps, and destructive commands

osop-report

3891
from openclaw/skills

Convert .osop and .osoplog.yaml into standalone HTML report with dark mode and expandable nodes

osop-optimize

3891
from openclaw/skills

Analyze .osoplog execution history to optimize workflows — finds slow steps and parallelization opportunities

osop-log

3891
from openclaw/skills

Generate OSOP session log — creates .osop workflow and .osoplog.yaml execution record

---

3891
from openclaw/skills

name: article-factory-wechat

Content & Documentation

humanizer

3891
from openclaw/skills

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Content & Documentation

find-skills

3891
from openclaw/skills

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

General Utilities

tavily-search

3891
from openclaw/skills

Use Tavily API for real-time web search and content extraction. Use when: user needs real-time web search results, research, or current information from the web. Requires Tavily API key.

Data & Research

baidu-search

3891
from openclaw/skills

Search the web using Baidu AI Search Engine (BDSE). Use for live information, documentation, or research topics.

Data & Research

agent-autonomy-kit

3891
from openclaw/skills

Stop waiting for prompts. Keep working.

Workflow & Productivity

Meeting Prep

3891
from openclaw/skills

Never walk into a meeting unprepared again. Your agent researches all attendees before calendar events—pulling LinkedIn profiles, recent company news, mutual connections, and conversation starters. Generates a briefing doc with talking points, icebreakers, and context so you show up informed and confident. Triggered automatically before meetings or on-demand. Configure research depth, advance timing, and output format. Walking into meetings blind is amateur hour—missed connections, generic small talk, zero leverage. Use when setting up meeting intelligence, researching specific attendees, generating pre-meeting briefs, or automating your prep workflow.

Workflow & Productivity

self-improvement

3891
from openclaw/skills

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.

Agent Intelligence & Learning