What does this skill do, and when should you use it?
ELI5 is a Claude Code skill that explains complex topics, code, or errors to a specific audience. It first identifies who the listener is — by age, education level, job role, or family relationship — then calibrates vocabulary, analogies, tone, and depth accordingly. The repo ships with an evaluation harness showing a measured pass rate of 83.3% with the skill versus a 41.6% baseline. The skill itself is pure prompting (a SKILL.md file) with no script dependencies, so installation is trivial.
The skill runs as a structured SKILL.md instruction set. Step one parses the user's request to identify the target audience using four lookup tables: ages (5, 10, 15, 20-30, 40+), education levels (5th grade through graduate school), job roles (manager, engineer, designer, director, product manager), and relationships (partner, parents, kids, friend) — each mapped to specific tone and analogy guidance. Step two requires the model to fully understand the source material (code, concept, error, or document) before explaining. Step three produces output following a what-first, analogy, details, why-it-matters-to-you structure, with rules like zero jargon for simple audiences, trade-offs and edge cases for technical ones, and impact-and-decisions framing for business ones. Unstated audiences default to age-5 style.
- An engineer reporting a technical issue (e.g., API rate limiting) to a manager wants the explanation framed around impact, cost, and decision options rather than implementation details
- A developer explaining a framework concept like React's useEffect to a college student needs moderate academic framing plus practical examples
- A technical person explaining an error or code to non-technical parents or a partner wants a patient, non-condescending tone
- Educators or science communicators explaining concepts like database indexes to a 5th grader or a 5-year-old need concrete everyday analogies with zero jargon
- Skill authors looking for a ready-made evaluation methodology: the repo provides the evals. test format, a run-evals.py script, and baseline comparison results
How do you install this skill?
- Static review only; no code or evals were executed. All effectiveness data comes from author claims.
- Eval scripts (run-evals.py) and results files were not included in the evidence; run the evals yourself before relying on the claimed pass rates.
- No Chinese-language support is declared; analogy and tone calibration for Chinese audiences is untested.
- When no audience is identifiable, the skill defaults to 'Age 5', which may not match expectations — specify the audience explicitly.
- Publisher is unverified with unclear maintenance and update path; track repository activity yourself.
Copy the skill into your Claude Code skills directory: git clone https://github.com/DreambigOu/ELI5.git, then cp -r ELI5/skills/eli5 ~/.claude/skills/eli5. Prerequisite: Claude Code CLI installed. No other dependencies.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/DreambigOu/ELI5.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/eli5" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- ELI5 what a database index is
- explain this code to my manager
- break down how git merge conflicts work for a 5th grader
- explain this error to my mom
In a Claude Code conversation, use natural trigger phrases such as: "ELI5 what a database index is", "explain this code to my manager", "break down how git merge conflicts work for a 5th grader", "explain this error to my mom", or "simplify this for a designer". The skill adjusts output style to the audience automatically. To run evaluations, execute python eli5-workspace/run-evals.py (skill must be installed first); flags include --test=1, --with-skill-only, and --grade-only.
What are this skill's strengths and limitations?
- Pure prompt skill — no scripts, network, or filesystem access; works immediately after install
- Fine-grained audience taxonomy covering ages, education levels, job roles, and relationships with concrete tone and analogy guidance per category
- Ships with a reproducible evaluation harness showing a quantified gain: pass rate from 41.6% to 83.3% (+41.7%)
- Clear behavioral rules: define jargon instantly, explain purpose before mechanism, prefer 80%-accurate clarity over 100%-accurate confusion
- Evaluations cover English prompts and a small test set (examples suggest ~12 assertions); no measured results for other languages
- Audience detection relies entirely on the model's parsing of the prompt; accuracy on ambiguous phrases like 'explain to my wife' is not separately verified
- The skill shapes presentation only — it does not verify the technical accuracy of the explanation itself
- Extending it (e.g., adding new audiences like CEO or journalist) requires manually editing SKILL.md
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| ELI5 Audience-Adaptive Explanation Skill this page | 64 · Recommended | ★ 1.9k | 7mo ago | MIT |
| i-have-adhd: ADHD-Friendly Output Mode | 65 · Recommended | ★ 60k | 17d ago | MIT |
| Claude Council | 47 · Use with care | ★ 98 | 3mo ago | — |
| PDA Task Reframing Skill | 63 · Recommended | ★ 59 | 10mo ago | MIT |
| Job Application Assistant | 60 · Recommended | ★ 70k | today | MIT |
The repo names no competing skills or alternatives; it compares only against a no-skill baseline, showing a +41.7% pass-rate improvement when the skill is active.
How did FollowSkills review this skill?
Pure prompt-based skill: no scripts, no external calls, no network access, no filesystem or credential operations; minimal attack surface with no red-line risks found. Deducted for: no explicit rollback guidance, no verifiable source attribution, and unverified publisher identity.
Instructions are self-consistent and well-structured (audience identification → source understanding → explanation), no runtime dependencies, happy path plausible. But static review cannot reproduce key paths; examples are illustrative, not reproducible tests; behavior on abnormal input (unidentifiable audience) is undefined — capped at 10 per anchors.
Audience taxonomy (ages/grades/roles/relationships) is thorough, trigger phrases are explicit in frontmatter, semantic invocation is clear. Deducted for: undeclared non-fit boundaries (non-English, non-conversational content), no Chinese-language support; README lists 'non-English support' as future work, suggesting degraded experience for Chinese users.
Good information layering (README + SKILL.md), complete MIT license, rich examples, clear installation. Deducted for: no version number, no changelog, no known-limitation disclosure (e.g., side effects of the '80% accuracy' simplification policy), unclear maintenance ownership and update path.
The task (audience-tailored explanations) is direct and completable; example outputs are usable; README claims 83.3% eval pass rate vs 41.6% baseline, indicating clear marginal value. However, the eval results file was not part of the provided evidence and static review cannot verify representative outputs; the benefit claim rests on author assertion — capped at 6.
README describes a reproducible eval methodology (run-evals.py, assertion design, baseline comparison) with an external blog post, but the eval scripts, evals. and eval-results.md were not provided in the evidence; actual results cannot be independently corroborated and remain author claims.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →