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.
npx skills add RudyCity/superagent --skill mcp-agentic-toolinggit clone --depth 1 https://github.com/RudyCity/superagentWrote 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.
[](https://agentmods.dev/skills/rudycity/superagent/mcp-agentic-tooling)<a href="https://agentmods.dev/skills/rudycity/superagent/mcp-agentic-tooling"><img src="https://agentmods.dev/badge/skills/rudycity/superagent/mcp-agentic-tooling/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.
<a href="https://agentmods.dev/skills/rudycity/superagent/mcp-agentic-tooling"><img src="https://agentmods.dev/badge/skills/rudycity/superagent/mcp-agentic-tooling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00059 | $0.02670 |
| Opus 5.5 | $0.00024 | $0.01068 |
| Sonnet 5.5 | $0.00012 | $0.00534 |
| Haiku 4.5 | $0.00006 | $0.00267 |
Grade B, and why
MCP Agentic Tooling & Execution for ChatGPT and Claude scanned grade B with 1 finding 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 6d 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.
Subtle steeringmediumPrompt injection
Instructions that bias recommendations or shape behaviour without the user noticing.
- NEVER tell the user: "You can run `npm test`" or "You should update line 42 in foo.ts". How it starts
The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MCP Agentic Tooling & Execution for ChatGPT and Claude
Overview
When external Large Language Models such as ChatGPT (GPT-4o, o1, o3-mini) or Claude (Claude 3.5 Sonnet, Claude 3.7 Sonnet) interact with a workspace through the Model Context Protocol (MCP), they frequently default to passive conversational chatbot behavior. Instead of utilizing their connected tools, they tend to:
- Output speculative advice or hypotheticals instead of inspecting actual files.
- Ask the user to execute shell commands manually rather than calling execution tools.
- Stop after a single read operation and ask for confirmation before proceeding to edit or test.
- Hallucinate file paths, code implementations, or command outputs.
This skill provides an authoritative, battle-tested agentic execution protocol and copy-pasteable instructions designed to force ChatGPT, Claude, and other MCP client models to operate as autonomous, proactive software engineering agents that prioritize tool invocation (TOOL_FIRST) across every turn.
The Core Mandates
1. TOOL_FIRST Enforcement (Zero Hallucination)
- If a task involves codebase exploration, file editing, debugging, building, testing, or environment verification, you MUST call MCP tools in your very first turn.
- NEVER guess what a file contains or speculate whether a package is installed. Always inspect with
superagent_read_file,superagent_find_files, orsuperagent_exec_command. - Conversational filler, greetings, and generic preamble before tool calls are strictly forbidden.
2. Autonomous Loop (Inspect -> Plan -> Act -> Verify -> Report)
Do not pause after every atomic action to ask the user "Would you like me to proceed?". Drive the entire execution lifecycle autonomously:
- Inspect: Read relevant source files, configs, and directory trees using
superagent_read_file,superagent_list_files, orsuperagent_grep_search. - Plan: Formulate the minimal required modifications and record tasks via
superagent_manage_tasksorsuperagent_get_plan_and_tasks. - Act: Apply targeted edits using
superagent_replace_file_contentor create files withsuperagent_write_file. - Verify: Immediately run the test suite or build verification command with
superagent_exec_command. - Report: Deliver a concise summary of results, changes made, and verification outputs once all steps are completed.
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.
- 6d ago First seen · 199 lines · 59 tokens per session scan B a08877381e97
MCP Agentic Tooling & Execution for ChatGPT and Claude is a skill published in the GitHub repository RudyCity/superagent (51 stars, last pushed today), licensed MIT. It adds 59 tokens to every session and 2,670 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (subtle steering). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-14.
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