Borrowing it
Nothing to install: this file belongs to marcoemrich/EXACT-Coding-Exercises. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/marcoemrich/EXACT-Coding-Exercises/main/.claude/skills/example-mapping/SKILL.mdgit clone --depth 1 https://github.com/marcoemrich/EXACT-Coding-ExercisesWrote 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/marcoemrich/exact-coding-exercises/example-mapping)<a href="https://agentmods.dev/skills/marcoemrich/exact-coding-exercises/example-mapping"><img src="https://agentmods.dev/badge/skills/marcoemrich/exact-coding-exercises/example-mapping/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/marcoemrich/exact-coding-exercises/example-mapping"><img src="https://agentmods.dev/badge/skills/marcoemrich/exact-coding-exercises/example-mapping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 269 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00080 | $0.03094 |
| Opus 5.5 | $0.00032 | $0.01238 |
| Sonnet 5.5 | $0.00016 | $0.00619 |
| Haiku 4.5 | $0.00008 | $0.00309 |
Grade A, and why
example-mapping 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 2mo 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Example Mapping
Conduct an interactive Example Mapping session with the user.
What is Example Mapping?
Example Mapping (by Matt Wynne) is a collaborative technique to explore a feature BEFORE implementation. It uses these categories of cards to structure the discovery:
- Story (yellow): The feature or user story being explored
- Rules (blue): Business rules and acceptance criteria discovered during discussion
- Examples (green): Concrete examples that illustrate a rule - these become tests later
- Questions (red): Open questions and uncertainties that need clarification
- New Stories: Behavior discovered during the session that belongs to a DIFFERENT story - sliced out of the current scope rather than explored here
Why Example Mapping Before TDD?
Example Mapping is the bridge between requirements and test lists:
- Example Mapping discovers rules and concrete examples through conversation
- Test List (
/test-list) converts those examples intoit.todo()test cases - Red-Green-Refactor implements them one by one
Without Example Mapping, developers often:
- Miss important business rules
- Start coding before understanding the domain
- Write tests for the wrong things
- Discover edge cases too late
How to Conduct the Session
Session Context: This is an INTERVIEW, not a group workshop
Example Mapping was designed for the Three Amigos around a table. This command runs a different format: a 1:1 interview between you and a single domain expert. That difference changes how you handle open questions.
In the original group workshop, open questions get parked on a red card and the session moves on. The reason is social: stopping to research would stall three or more people, and the person who could answer is usually not in the room.
In this interview, the person who can answer IS in the room - that's the entire point of the format. So the default is inverted: ask immediately instead of parking. A question you park here is a question you could have resolved in ten seconds.
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.
- 2mo ago First seen · 273 lines · 80 tokens per session scan A 2646c7f512b2
example-mapping is a skill published in the GitHub repository marcoemrich/EXACT-Coding-Exercises (18 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 3,094 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
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