mcp-friction-assessment

mcp-friction-assessment is a skill for Claude Code from dinglebear-ai/cortex. It costs 99 tokens per session (1,326 once invoked), scanned B, original, AGPL-3.0.

A diagnostic skill for investigating failed or confusing MCP tool calls. MCP is a way for an AI agent to connect to external tools and services.

In plain words
What is it for?
Use it after running the Cortex MCP assessment or investigation pipeline, when reviewing evidence about MCP tool reliability or diagnosing a specific failed server call.
Why use it?
It helps explain why an MCP server or tool produced an error, behaved incorrectly, or confused the agent instead of treating every failure as an unexplained outage.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the cortex plugin — 12 skills, 1 MCP server shipped together

Good fit Use it after running the Cortex MCP assessment or investigation pipeline, when reviewing evidence about MCP tool reliability or diagnosing a specific failed server call.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dinglebear-ai/cortex/mcp-friction-assessment
Install

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.

Any agent
npx skills add dinglebear-ai/cortex --skill mcp-friction-assessment
Clone the repo
git clone --depth 1 https://github.com/dinglebear-ai/cortex

Made for: Claude Code.

Or install cortex, the plugin that ships this one along with the rest of its 12 skills, 1 MCP server.

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 mcp-friction-assessment

README.md
[![agentmods](https://agentmods.dev/badge/skills/dinglebear-ai/cortex/mcp-friction-assessment/github.svg)](https://agentmods.dev/skills/dinglebear-ai/cortex/mcp-friction-assessment)
Your own site
<a href="https://agentmods.dev/skills/dinglebear-ai/cortex/mcp-friction-assessment"><img src="https://agentmods.dev/badge/skills/dinglebear-ai/cortex/mcp-friction-assessment/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 mcp-friction-assessment

Your own site · 80×15
<a href="https://agentmods.dev/skills/dinglebear-ai/cortex/mcp-friction-assessment"><img src="https://agentmods.dev/badge/skills/dinglebear-ai/cortex/mcp-friction-assessment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 99 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,326 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown 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 $0.00099 $0.01326
Opus 5 $0.00049 $0.00663
Sonnet 5 $0.00020 $0.00265
Haiku 4.5 $0.00010 $0.00133

Measured 10d ago against content hash 9888b3984108, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade B, and why

mcp-friction-assessment 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 10d 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.

Instruction-override phrasingmediumPrompt injection

Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.

at you (for example "ignore previous instructions", "you are now in

Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.

plugins/cortex/skills/mcp-friction-assessment/SKILL.md · 121 lines

The source is not reproduced here

Licensed AGPL-3.0

The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

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. 10d ago First seen · 121 lines · 99 tokens per session scan B 9888b3984108

Subscribe to this mod's changes

mcp-friction-assessment is a skill published in the GitHub repository dinglebear-ai/cortex (3 stars, last pushed today), licensed AGPL-3.0. It adds 99 tokens to every session and 1,326 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.