ELI5 for Claude Code: Audience-Calibrated Explanations as a Skill File
ELI5 — A Claude Code skill that explains anything to anyone: kids, managers, engineers, parents. Adapts tone, vocabulary, and analogies to match the audience.
At a glance
- What is it?
- ELI5 is a Claude Code skill that rewrites explanations for a named audience, from a five-year-old to a director. The interesting part is not the prompt, it is the eval harness that grades skill output against baseline output.
- Who is it for?
- Adopt ELI5 if you regularly turn the same technical material into different registers for different readers, and if you want a worked example of a skill-plus-eval-repo layout you can copy. Skip it if you only need one register, since a single well-written prompt in your own CLAUDE.md covers that case without a second directory to maintain.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Activity is slowing. The repository last received commits 6 months ago.
- What is it written in?
- Mainly Python, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The Problem Is Register, Not Comprehension
Ask a language model to explain a database index and you get a competent answer pitched at roughly one reader: an adult with some technical tolerance. That answer is wrong for a five-year-old and also wrong for a director who wants to know what it costs and what breaks. The gap is not knowledge, it is register. ELI5 takes the position that register is predictable enough to encode as a skill, and that the audience can be inferred from the phrasing of the request rather than set by a flag.
The README lists the audiences it claims to cover: ages from 5 to 40+, grade levels from 5th grade to graduate school, job roles including manager, engineer, designer, director and product manager, and relationships including wife, husband, parents, kids and friend. The intended user is someone who explains things for a living or as part of a job: an engineer writing a status update for a director, a parent answering a question at dinner, a teacher preparing material for a class. The repository is a Claude Code skill, so the practical audience is narrower than that list suggests. You need Claude Code installed to use it at all.
How Audience Detection and Calibration Actually Work
The skill is a directory copied into Claude Code's skills folder, and the README describes the mechanism in terms of five axes it calibrates once an audience is detected: vocabulary, analogies, tone, depth and framing. The examples are concrete enough to be testable. Analogies become toys and playground for age 5 and business outcomes for managers. Framing becomes impact and risk for managers, UX for designers, architecture for engineers. Tone is playful for children, professional for directors, warm for family.
What the README does not describe is the detection logic itself. There is no stated list of trigger phrases beyond the usage examples, and no fallback behaviour documented for a prompt that names no audience. That is the weakest part of the design as published. A request like "ELI5 what a database index is" has no audience in it at all, which means either the skill picks a default or the model guesses. The README does not say which, and that ambiguity is worth knowing about before you rely on it in a workflow where the wrong register is worse than no explanation.
The repository layout also carries a second, separate artifact: an evaluation workspace. The eval cases live in eli5-workspace/evals.json, and the README shows the schema for a test case with four fields: an id, a name used as a directory-friendly identifier for storing results, a prompt, an audience label, and a list of assertions. The example case, explain-recursion-teenager, asserts that the output uses social media, gaming or phone references as analogies, that the tone is casual without what the README calls "fellow kids" energy, that the concept of a function calling itself is explained correctly, and that a base case or stopping condition is mentioned. The README notes that four assertions per test works well.
Installation Is Two Commands and One Directory Convention
Installation is a clone followed by a copy into the skills directory:
git clone https://github.com/DreambigOu/ELI5.git cp -r ELI5/skills/eli5 ~/.claude/skills/eli5
That is the whole setup. The path matters: Claude Code reads skills from ~/.claude/skills/, and the copied folder must be named eli5 to match. There is no package to install, no dependency file mentioned, and no build step. Once the directory is in place, the README says you invoke it conversationally, with phrases like "ELI5 this" or "explain this to my manager", rather than through a slash command or a configuration key.
Running the evaluations is a separate setup. The README lists two prerequisites: the Claude Code CLI installed, and the skill installed at ~/.claude/skills/eli5/. Then the runner is invoked with Python:
python eli5-workspace/run-evals.py python eli5-workspace/run-evals.py --test=1 python eli5-workspace/run-evals.py --with-skill-only python eli5-workspace/run-evals.py --grade-only
The flags divide the work cleanly. --test=N runs one case, --with-skill-only drops the baseline comparison, and --grade-only re-scores outputs that already exist without re-running the prompts. That last flag is the one that matters in practice, because grading costs a model call per assertion and re-running the prompt costs another. The README says the script runs each prompt twice, once with the skill and once without, then auto-grades every output against its assertions using Claude, then prints a pass rate summary. Results land in eli5-workspace/iteration-N/, with the directory number auto-incrementing on each run, and each test case writes a grading.txt with what the README calls detailed evidence.
The Eval Harness Is the More Reusable Half
Most skill repositories ship a prompt and stop. This one ships a comparison methodology, and that is the part worth reading even if you never use ELI5 itself. The runner's design encodes a specific claim: that a skill's value is the difference between output with the skill and output without it, graded against assertions written in advance. The README's example output shows the shape of that comparison, with a per-assertion pass and fail list under each condition and a summary block reporting pass rate with the skill, pass rate without it, and the delta.
The README reports a current result of 83.3% with the skill against 41.6% without, a delta of 41.7 percentage points, and states that the largest improvement is in audience-specific framing, particularly for non-technical audiences like managers, which it reports moving from 0% baseline to 50% with the skill. Treat those figures as the maintainer's own reported numbers from the repository's evaluation, not as an independent measurement. The grader is Claude scoring Claude's output against assertions the same repository author wrote, so the harness measures agreement with a rubric, not correctness. A test can pass all four assertions and still contain a misleading analogy.
The assertion format is where the discipline lives. An assertion like "mentions a base case or stopping condition" is checkable. An assertion like "tone is casual but not cringey" is a judgement call delegated to a model, and the README's own example relies on that kind of phrasing. That mix is honest about what is being measured, but it means the pass rate is only as stable as the grader. If you extend the suite, the README's contributing note suggests adding audience types such as CEO, intern or journalist, adding non-English language support, and improving evaluation coverage with more test cases. All three are open work, not shipped features.
Where It Breaks Down
The clearest limitation is that the skill only fires when the audience is stated or inferable. The README's usage examples all name one: "to my manager", "for a 5th grader", "to my mom", "for a designer". The bare form, "ELI5 what a database index is", names none, and nothing in the supplied material explains what happens then. If your prompts are terse, you will be guessing at the register you get back.
A second constraint is that the skill is a text transformation with no memory of the reader. It cannot know that your manager already understands caching but not indexing, or that your ten-year-old has heard the word algorithm before. Every invocation starts from the audience label alone. For one-off explanations that is fine. For an ongoing relationship with a specific reader, a persistent project instruction that describes that person in detail will beat a category label every time.
The third issue is the evaluation cost. Grading is done by a model call per assertion, and the README's default flow runs every prompt twice and grades both runs. That doubles the model calls before grading even starts. The --with-skill-only and --grade-only flags exist precisely to cut that cost, which suggests the maintainer hit the same wall. There is also no release history retrieved for this repository, so there is no changelog to consult about how the eval set or the skill text has changed over time. The last push date is 2026-03-18, which tells you the repository was active then and nothing more.
Finally, the repository is MIT licensed. That permits reuse and modification with the licence and copyright notice retained, but this is a description of the licence text, not legal advice, and the skill's prompt content is the thing you would be modifying.
Against a Plain Project Instruction
The obvious alternative is not another skill. It is a few paragraphs in your CLAUDE.md or an equivalent project instruction file that tells the model how you want explanations pitched. That approach has real advantages here. It is versioned alongside the code it describes, it can name specific readers instead of categories, and it costs nothing to maintain beyond the file you already have. If you explain things to one audience most of the time, a project instruction is the better tool and ELI5 is overhead.
The difference in approach is that a project instruction is static and ELI5 is conditional. The skill's premise is that the register should be chosen per request from the prompt, so a single session can explain the same concept to a fifth grader and then to a director without you editing anything. A project instruction cannot do that unless you rewrite it between requests. That is the actual trade: ELI5 buys per-request switching at the cost of a second directory to keep in sync and a detection step you do not control.
There is a second alternative worth naming for the eval half. If what you want is a way to measure prompt changes, the pattern here, a JSON file of cases with assertions, a runner that executes with and without the change, and a grader that scores against the assertions, is portable. You can build it for your own prompts without adopting this skill at all, and the flags and directory conventions in run-evals.py are a reasonable template to copy. The README links to a separate post on how the eval system works, which is where the design rationale lives rather than in the repository itself.
Maintenance, Upgrades and What to Check First
Upgrading is a git pull in the cloned repository followed by re-running the copy command, since the skill is installed by copying rather than by linking. That means local edits to ~/.claude/skills/eli5/ are overwritten on the next copy, so any customisation you make should live in your fork rather than in the installed directory. There is no package manager and no version pinning mechanism described, so the version you have is whatever the main branch contained when you cloned it.
The eval workspace adds a second maintenance surface. Each run creates a new iteration-N directory, and the README does not describe any cleanup or retention policy for those directories. If you run the suite often, that is unbounded local growth of prompt outputs and grading files. The evals.json file is also a file you will end up owning, because the assertions encode your standard of a good explanation, not the maintainer's. Adding a test case means writing four assertions and a prompt, which is cheap, but the assertions need to be checkable enough that the grader is not just agreeing with itself.
The MIT licence keeps the upgrade path unencumbered: you can fork, modify the skill text, and redistribute, provided the licence and copyright notice stay in place. That is a statement about the licence, not a legal opinion, and if you plan to ship the skill inside a commercial product you should read the licence text yourself.
Editorial conclusion
Adopt ELI5 if you regularly turn the same technical material into different registers for different readers, and if you want a worked example of a skill-plus-eval-repo layout you can copy. Skip it if you only need one register, since a single well-written prompt in your own CLAUDE.md covers that case without a second directory to maintain. Before adopting, open eli5-workspace/evals.json and read the assertions for the audience you care about, then run python eli5-workspace/run-evals.py --test=1 and check whether the grader's evidence in grading.txt matches your own reading of the output. The skill's value to you is bounded by how well those assertions match your definition of a good explanation.
Community notes