# Write Func As YAML

> Write function analysis results as YAML file beside the binary using IDA Pro MCP. Use this skill after completing function identification and signature generation to persist the results in a standardized YAML format. For virtual functions with known vtable index, use write-vfunc-as-yaml instead.

- Skill: `tools-only/write-func-as-yaml` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds add tools-only/write-func-as-yaml`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/write-func-as-yaml/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/tools-only/write-func-as-yaml

---


# Write Function IDA Analysis Output as YAML

Persist function analysis results to a YAML file beside the binary using IDA Pro MCP.

## Prerequisites

Before using this skill, you should have:
1. Identified and renamed the target function
2. Generated a unique signature using `/generate-signature-for-function`

## Required Parameters

| Parameter | Description | Example |
|-----------|-------------|---------|
| `func_name` | Name of the function | `CBaseModelEntity_SetModel` |
| `func_addr` | Virtual address of the function | `0x180A8CA10` |
| `func_sig` | Unique byte signature | `41 B8 80 00 00 00 48 8D 99 10 05 00 00` |

## Method

```python
mcp__ida-pro-mcp__py_eval code="""
import idaapi
import os
import yaml

# === REQUIRED: Replace these values ===
func_name = "<func_name>"           # e.g., "CBaseModelEntity_SetModel"
func_addr = <func_addr>             # e.g., 0x180A8CA10
func_sig = "<func_sig>"             # e.g., "41 B8 80 00 00 00"
# ======================================

# Get function size
func = idaapi.get_func(func_addr)
func_size = func.size() if func else 0

# Get binary path and determine platform
input_file = idaapi.get_input_file_path()
dir_path = os.path.dirname(input_file)

if input_file.endswith('.dll'):
    platform = 'windows'
    image_base = idaapi.get_imagebase()
else:
    platform = 'linux'
    image_base = 0x0

func_rva = func_addr - image_base

data = {
    'func_va': hex(func_addr),
    'func_rva': hex(func_rva),
    'func_size': hex(func_size),
    'func_sig': func_sig,
}

yaml_path = os.path.join(dir_path, f"{func_name}.{platform}.yaml")
with open(yaml_path, 'w', encoding='utf-8') as f:
    yaml.dump(data, f, default_flow_style=False, sort_keys=False)
print(f"Written to: {yaml_path}")
"""
```

## Output File Naming Convention

The output YAML filename follows this pattern:
- `<func_name>.<platform>.yaml`

Examples:
- `server.dll` → `CBaseModelEntity_SetModel.windows.yaml`
- `server.so` / `libserver.so` → `CBaseModelEntity_SetModel.linux.yaml`

- `engine2.dll` → `CServerSideClient_IsHearingClient.windows.yaml`
- `libengine2.so` → `CServerSideClient_IsHearingClient.linux.yaml`

## Output YAML Format

```yaml
func_va: 0x180A8CA10   # Virtual address - changes with game updates
func_rva: 0xA8CA10     # Relative virtual address (VA - image base) - changes with game updates
func_size: 0x3F        # Function size in bytes - changes with game updates
func_sig: 41 B8 80 00 00 00 48 8D 99 10 05 00 00  # Unique byte signature
```

## Platform Detection

The skill automatically detects the platform based on file extension:
- `.dll` → Windows (uses `idaapi.get_imagebase()` for image base)
- `.so` → Linux (uses `0x0` as image base)

## Notes

- All values marked "changes with game updates" should be regenerated when analyzing new binary versions
- The YAML file is written to the same directory as the input binary
- func_size is automatically calculated from IDA's function analysis
- func_rva is automatically calculated as `func_va - image_base`
- For virtual functions with vtable information, use `/write-vfunc-as-yaml` instead