system-prompt-lookup
Checks an AI agent's exact instructions by consulting a historical archive. This ensures the agent follows the correct rules. Useful before trusting the agent's actions.
What it does
This tool checks what an AI program was actually told in its instructions (called a 'system prompt') and what tools it can use. It does this by looking at a historical archive of instructions from AI programs that have already been released, instead of trying to remember.
When to use it
- ✓When someone asks for an AI program's instructions.
- ✓Before stating what an AI program was instructed to do.
- ✓When comparing different AI programs, like the size of their instructions or number of tools.
Example of what to ask your AI
“Check the instructions for the 'Claude-Code' program.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool searches a historical archive for the system prompts and tool schemas of released AI programs. It reads the files directly to ensure the information is exact and not an inaccurate recollection.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill system-prompt-lookupOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/system-prompt-lookup/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Checking what an agent was actually told
Overview
OrcaPromptVault is a dated archive of the system prompts and tool-call schemas that shipped AI products send — one directory per product, schemas stored as JSON. Each artifact records how it was obtained: captured off the wire while the harness ran unmodified, or reported by the vendor.
This skill is the judgement layer over that archive. It exists because models, including the one reading this, will happily produce a confident paraphrase of a product's system prompt that is a reconstruction rather than a quotation. The archive is a primary source; your recollection of it is not.
Read-only. Nothing here installs, runs, or mutates anything; the only external access is fetching public files from github.com.
When to Use This Skill
- Use when the user asks what a product's system prompt says, or quotes one and asks whether it is real.
- Use when you are about to state that some agent is instructed to do something — check first.
- Use when comparing products: how large a prompt is, how many tools it ships, how it phrases a refusal or a safety rule.
- Use when someone shows you an extracted or leaked prompt and wants it verified.
- Use when writing a harness and you want to see how shipped ones solve the same problem.
Do not use it to conclude that a product behaves a certain way today. Every artifact is a dated snapshot of one version on one day.
How It Works
Step 1: Find the product directory
Top-level directories are named after the product: Claude-Code/, Cursor/, Codex/, Cline/,
Devin/, Windsurf/, Goose/, Crush/, OpenClaw/, Manus/, Perplexity/ and others. Each has
a README.md listing its files with the model, the mode, the character count and the tool count.
# What products exist
curl -s https://api.github.com/repos/Continuum-AI-Corp/OrcaPromptVault/contents | grep '"name"'
# What is inside one of them
curl -s https://api.github.com/repos/Continuum-AI-Corp/OrcaPromptVault/contents/Claude-Code | grep '"name"'
Step 2: Read the file, not a summary of it
File names carry the facts: <product>-<model>-system-prompt-<date>.md for the prompt,
<product>-<model>-tools.json for the schema that travelled with it. A -print- segment marks the
non-interactive mode rather than the interactive one.
BASE=https://raw.githubusercontent.com/Continuum-AI-Corp/OrcaPromptVault/main
curl -s "$BASE/Claude-Code/claude-code-opus-5-system-prompt-2026-09-03.md" | head -40
curl -s "$BASE/Claude-Code/claude-code-opus-5-tools.json" | grep -o '"name": *"[^"]*"'
Quote from what you fetched. If you did not fetch it, say so rather than reconstructing it.
Step 3: Check the provenance label before relying on it
docs/CAPTURES.md lists every artifact that was pulled off the wire, with the date, the character
count, and the command that reproduces it. Anything absent from that table came from a vendor
publication or an upstream collection — still useful, but it is the vendor's account of its own
prompt, which is a different kind of evidence.
curl -s "$BASE/docs/CAPTURES.md" | grep -i "claude code"
Say which kind you are citing. Captured on a given date and published by the vendor are not interchangeable claims.
Step 4: Diff, do not eyeball
Two artifacts of the same product differ for a reason worth reporting.
curl -s "$BASE/Claude-Code/claude-code-fable-5.1-system-prompt-2026-09-02.md" -o /tmp/a.md
curl -s "$BASE/Claude-Code/claude-code-fable-5.1-print-system-prompt-2026-09-02.md" -o /tmp/b.md
diff /tmp/a.md /tmp/b.md | head -60
Same model, same day, interactive versus headless: the identity line itself changes and the tool list shrinks. A claim about the prompt of a product that ships several modes is under-specified.
Examples
Example 1: The user quotes a prompt and asks whether it is genuine
User: Is this really in Claude Code's system prompt? "You are a Claude agent, built on
Anthropic's Claude Agent SDK."
Fetch both modes for that model, grep for the line, and answer with the mode it belongs to — here the headless/SDK capture rather than the interactive one. Give the file name and its capture date, not a summary.
Example 2: Comparing tool surfaces across products
BASE=https://raw.githubusercontent.com/Continuum-AI-Corp/OrcaPromptVault/main
…
FAQ
What is a 'system prompt'?
It's the initial set of instructions an AI program receives to know how to behave and what to do.
What are 'tool schemas'?
These are descriptions of the tools or functions an AI program can use to perform tasks.
Does this tool change an AI program's instructions?
No, this tool only reads information from a historical archive. It does not install, run, or modify anything.
Details
Original description by the author
Checks what a shipped AI product's system prompt and tool schema actually say, by reading a dated archive of captured prompts instead of recalling them. Use before asserting or accepting any claim about an agent's instructions.
Popularity
- GitHub stars
- 48k
- Ranking position
- #4303
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
00-andruia-consultant
Helps your agent start or improve AI projects. It analyzes code, suggests tools, and creates action plans in Spanish.
sickn33/agentic-awesome-skills
007
Lets your agent perform security audits and threat modeling. It's useful for protecting projects against attacks and vulnerabilities.
sickn33/agentic-awesome-skills
10-andruia-skill-smith
Helps your agent create or improve AI tools. It defines how tools work, writes documentation, and validates the structure.
sickn33/agentic-awesome-skills
20-andruia-niche-intelligence
Lets your agent create a summary about a specific industry. It's useful for understanding the context, rules, usage patterns, and risks of a project, using reliable sources.
sickn33/agentic-awesome-skills
Similar skills
bengali-nlp
Has your agent process Bengali text. It helps understand and work with the language, normalizing text and handling its unique characteristics.
affaan-m/ECC
prompt-caching-strategy
Helps your agent optimize how it uses prompts for language models. It analyzes how prompts are made to find ways to cut costs and make responses faster by reusing information when possible.
affaan-m/ECC
prompt-optimizer
Has your agent analyze and improve the prompts you write. It suggests how to add information or change the wording so the AI model understands better and gives the desired answer.
affaan-m/ECC