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 igor-elbert/ctobot --skill cto-challenge-extractiongit clone --depth 1 https://github.com/igor-elbert/ctobotWrote 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/igor-elbert/ctobot/cto-challenge-extraction)<a href="https://agentmods.dev/skills/igor-elbert/ctobot/cto-challenge-extraction"><img src="https://agentmods.dev/badge/skills/igor-elbert/ctobot/cto-challenge-extraction/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/igor-elbert/ctobot/cto-challenge-extraction"><img src="https://agentmods.dev/badge/skills/igor-elbert/ctobot/cto-challenge-extraction.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 | $0.00043 | $0.00747 |
| Opus 5 | $0.00022 | $0.00374 |
| Sonnet 5 | $0.00009 | $0.00149 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
cto-challenge-extraction 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CTO Challenge Extraction
Overview
Use this skill to convert a source article, interview, blog post, or leadership checklist into a normalized set of CTO challenges that can be scored later.
The point is to identify the real operating problems hidden inside the source language. Good extraction strips away:
- product promotion
- vendor upsell language
- repeated phrasing
- generic leadership fluff
The result should be a compact, plain-English challenge list that is ready for red-team scoring.
When to Use
Use when:
- the user asks for “the challenges” in an article or leadership post
- you need a challenge list before building a scorecard
- the user says to ignore sales pitches or vendor recommendations
- multiple sources need to be merged into one canonical challenge set
Do not use for:
- scoring the docs themselves
- rewriting operating manuals
- summarizing an article for general reading
Workflow
-
Read the source closely.
- Prefer headings, TL;DR sections, bullet lists, and “What to do” sections.
- Treat marketing copy as suspect unless it clearly names a leadership problem.
-
Extract the underlying problem statement.
- Rewrite “how to win buy-in for our platform” as a leadership challenge like “getting stakeholder buy-in for change.”
- Rewrite “use resource management software” as “maintaining visibility into capacity and skills.”
-
Normalize wording.
- Use short, plain labels.
- Prefer nouns or gerunds: roadmap misalignment, operational pull, change management, profitability.
- Remove source-specific jargon unless it is genuinely part of the challenge.
-
Merge duplicates.
- If two sources describe the same problem differently, collapse them into one canonical challenge.
- Keep a source note only if it helps disambiguate the wording.
-
Tag the challenge if useful.
- Optional tags: strategy, delegation, change, communication, talent, finance, culture, risk.
Output Shape
Return a short list like:
- Challenge: stakeholder buy-in
- Plain meaning: getting executives, managers, and teams to support and adopt a change
- Challenge: profitability pressure
- Plain meaning: protecting margin while teams stay busy
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.
- 8d ago First seen · 95 lines · 43 tokens per session scan A 87cd771d7fb2
cto-challenge-extraction is a skill published in the GitHub repository igor-elbert/ctobot (5 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 747 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…
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…
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…
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…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…