CS2_VibeSignatures: Skill for Claude Code

.claude/skills/write-func-as-yaml/SKILL.md

write-func-as-yaml is a skill for Claude Code from HLND2T/CS2_VibeSignatures. It costs 61 tokens per session (1,010 once invoked), scanned A, original, MIT.

A reverse-engineering skill for saving information about a discovered program function in a YAML file. IDA Pro is a tool for examining compiled programs, and YAML is a human-readable data format.

In plain words
What is it for?
It helps record a renamed function, its location, and its unique byte pattern beside the analyzed program.
Why use it?
It keeps function names, addresses, and byte signatures in a consistent file format instead of leaving the findings only inside the analysis session.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is HLND2T/CS2_VibeSignatures's own configuration. It tells Claude Code how to work on CS2_VibeSignatures itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything CS2_VibeSignatures configures →

Reuse

Borrowing it

Nothing to install: this file belongs to HLND2T/CS2_VibeSignatures. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/HLND2T/CS2_VibeSignatures/main/.claude/skills/write-func-as-yaml/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/HLND2T/CS2_VibeSignatures

Made for: Claude Code.

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 write-func-as-yaml

README.md
[![agentmods](https://agentmods.dev/badge/skills/hlnd2t/cs2_vibesignatures/write-func-as-yaml/github.svg)](https://agentmods.dev/skills/hlnd2t/cs2_vibesignatures/write-func-as-yaml)
Your own site
<a href="https://agentmods.dev/skills/hlnd2t/cs2_vibesignatures/write-func-as-yaml"><img src="https://agentmods.dev/badge/skills/hlnd2t/cs2_vibesignatures/write-func-as-yaml/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.

agentmods 80×15 button for write-func-as-yaml

Your own site · 80×15
<a href="https://agentmods.dev/skills/hlnd2t/cs2_vibesignatures/write-func-as-yaml"><img src="https://agentmods.dev/badge/skills/hlnd2t/cs2_vibesignatures/write-func-as-yaml.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,010 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00061 $0.01010
Opus 5 $0.00030 $0.00505
Sonnet 5 $0.00012 $0.00202
Haiku 4.5 $0.00006 $0.00101

Measured 3d ago against content hash 899800df1fe2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

write-func-as-yaml 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 3d 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.

.claude/skills/write-func-as-yaml/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

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.environ.get('CS2VIBE_ARTIFACT_DIR') or 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_name': func_name,
    '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.dllCBaseModelEntity_SetModel.windows.yaml
  • libserver.so / libserver.soCBaseModelEntity_SetModel.linux.yaml

Read the full file on GitHub · 109 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. 3d ago Changed · +4 lines 899800df1fe2
  2. 9d ago First seen · 105 lines · 61 tokens per session scan A 6bd28e2292e5

Subscribe to this mod's changes

write-func-as-yaml is a skill published in the GitHub repository HLND2T/CS2_VibeSignatures (65 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 1,010 once invoked, about $0.0003 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 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens