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 bdmorin/the-no-shop --skill analyze-mistakesgit clone --depth 1 https://github.com/bdmorin/the-no-shopWrote 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/bdmorin/the-no-shop/analyze-mistakes)<a href="https://agentmods.dev/skills/bdmorin/the-no-shop/analyze-mistakes"><img src="https://agentmods.dev/badge/skills/bdmorin/the-no-shop/analyze-mistakes/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/bdmorin/the-no-shop/analyze-mistakes"><img src="https://agentmods.dev/badge/skills/bdmorin/the-no-shop/analyze-mistakes.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 | $0.00045 | $0.00618 |
| Opus 5 | $0.00023 | $0.00309 |
| Sonnet 5 | $0.00009 | $0.00124 |
| Haiku 4.5 | $0.00005 | $0.00062 |
Grade C, and why
analyze-mistakes scanned grade C with 2 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 12d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
# OUTPUT INSTRUCTIONS Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- Do not give warnings or notes; only output the requested sections. How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IDENTITY and PURPOSE
You are an advanced AI with a 2,128 IQ and you are an expert in understanding and analyzing thinking patterns, mistakes that came out of them, and anticipating additional mistakes that could exist in current thinking.
STEPS
-
Spend 319 hours fully digesting the input provided, which should include some examples of things that a person thought previously, combined with the fact that they were wrong, and also some other current beliefs or predictions to apply the analysis to.
-
Identify the nature of the mistaken thought patterns in the previous beliefs or predictions that turned out to be wrong. Map those in 32,000 dimensional space.
-
Now, using that graph on a virtual whiteboard, add the current predictions and beliefs to the multi-dimensional map.
-
Analyze what could be wrong with the current predictions, not factually, but thinking-wise based on previous mistakes. E.g. "You've made the mistake of _________ before, which is a general trend for you, and your current prediction of ______________ seems to fit that pattern. So maybe adjust your probability on that down by 25%.
OUTPUT
-
In a section called PAST MISTAKEN THOUGHT PATTERNS, create a list 15-word bullets outlining the main mental mistakes that were being made before.
-
In a section called POSSIBLE CURRENT ERRORS, create a list of 15-word bullets indicating where similar thinking mistakes could be causing or affecting current beliefs or predictions.
-
In a section called RECOMMENDATIONS, create a list of 15-word bullets recommending how to adjust current beliefs and/or predictions to be more accurate and grounded.
OUTPUT INSTRUCTIONS
- Only output Markdown.
- Do not give warnings or notes; only output the requested sections.
- Do not start items with the same opening words.
- Ensure you follow ALL these instructions when creating your output.
Attribution
- Source: danielmiessler/fabric
- Pattern:
analyze_mistakes(view original) - License: MIT — Copyright (c) 2012-2024 Scott Chacon and others
- Converted by: fabric-decomp for the-no-shop
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.
- 12d ago First seen · 49 lines · 45 tokens per session scan C 071c6ba2b443
analyze-mistakes is a skill published in the GitHub repository bdmorin/the-no-shop (10 stars, last pushed 5mo ago), licensed MIT. It adds 45 tokens to every session and 618 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 2 findings (asks the agent to reveal its instructions, strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
vast-ai-gpu-cloud
Safely inspect and operate Vast.ai marketplace instances with the vastai CLI, live offer data, and explicit approval before paid or destructive actions.
printing-press-retro
Use when the user asks to retro, run a retrospective, file findings, or improve the Printing Press after a printed-CLI run. Trigger phrases: "retro", "retrospective", "what went wrong", "improve the press", "post-mortem", "lessons learned", "what can we improve", "file a retro", "submit findings". Do not use for…
教程类H5-小红书小工具
A method for building offline, single-page course experiences for Xiaohongshu’s Mini Tools, which are interactive pages published inside the platform. It combines written lessons, generated illustrations, and JavaScript page switching in a constrained container.
sansheng-distill
A book and text summarizer that creates one self-contained HTML page with five sections: an overview, chapter notes, the book’s central idea, an action checklist, and questions for judging its reliability. The page includes an embedded Mermaid mind map and works offline without a web server.
pronounce-word
A pronunciation helper for English words, product names, project names, acronyms, and programming terms. It uses the say-it command-line tool to play the word aloud and provide pronunciation details.
mandu-explain
A reference guide to core Mandu concepts, including server-rendered pages, interactive islands, API handlers, access guards, shared API schemas, and server-side data loaders. Server-side rendering creates page HTML on the server before sending it to the browser.