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 agentmods add skills/chefjefff/mckinsey-problem-solving/problem-solvingnpx skills add chefjefff/mckinsey-problem-solving --skill problem-solvinggit clone --depth 1 https://github.com/chefjefff/mckinsey-problem-solvingWhat 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 | $0.00193 | $0.06454 |
| Opus 5 | $0.00097 | $0.03227 |
| Sonnet 5 | $0.00039 | $0.01291 |
| Haiku 4.5 | $0.00019 | $0.00645 |
Grade A, and why
problem-solving 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 3d 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 — 510 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bulletproof Problem Solving
Source: Charles Conn & Robert McLean, "Bulletproof Problem Solving: The One Skill That Changes Everything" (Wiley, 2019)
The McKinsey 7-step problem solving process. This skill codifies the entire framework into modular components you can pull individually. Each component is standalone -- use the one that fits your task.
Core principle: Problem solving is an iterative cycle, not a linear checklist. You "porpoise" between steps -- diving into data, surfacing to refine your hypothesis, diving again. The "one-day answer" (your best current synthesis) should be ready at any point.
The 7 Steps at a Glance
1. Define the Problem
2. Disaggregate (logic trees)
3. Prioritize (prune the tree)
4. Build a Workplan
5. Conduct Analysis
6. Synthesize Findings
7. Tell the Story
Each step feeds the next, but you iterate backwards constantly. A finding in Step 5 may send you back to redefine Step 1.
Step 1: Define the Problem
The most underrated step. A surprising number of failures originate in poor problem definition. Teams surge into data gathering without being clear about boundaries, criteria for success, time frame, or accuracy required.
Problem Statement Qualities (SMART+)
A good problem statement is:
- Outcomes-focused -- expressed as outcomes, not activities or intermediate outputs
- Specific and measurable wherever possible
- Time-bound -- clear deadline
- Decision-maker aligned -- explicitly addresses their values, boundaries, aspirations, accuracy needs
- Sufficiently scoped for creativity -- too narrow kills novel solutions
- Solved at the highest level possible -- for the whole org, not just one unit
Mnemonic: SMART = Specific, Measurable, Action-oriented, Relevant, Timely. But also add: outcomes focus + highest level.
Problem Definition Worksheet
Don't try to fit everything into one sentence. Use a worksheet:
| Element | Content |
|---|---|
| Problem statement | What are we solving? (outcome-focused) |
| Context & background | Why does this matter now? |
| Success criteria | How will we know we've solved it? |
| Boundaries/constraints | What's in and out of scope? |
| Decision maker | Who decides, and what are their values? |
| Time frame | When is the answer needed? |
| Accuracy required | Rough-cut or precise? |
| Key stakeholders | Who else is affected? |
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.
- 3d ago First seen · 510 lines · 193 tokens per session scan A a695c507e1a9
problem-solving is a skill published in the GitHub repository chefjefff/mckinsey-problem-solving (10 stars, last pushed 4mo ago), licensed MIT. It adds 193 tokens to every session and 6,454 once invoked, about $0.0010 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.
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