Agent skill

Eli5

by try-works in try-works/role-model

Transform technical jargon into clear explanations using before/after comparisons, metaphors, and practical context

MITAuto-check passed

Install Eli5

skills CLI
$ npx skills add try-works/role-model --skill eli5 -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install try-works/role-model eli5 --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/try-works/role-model.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/eli5 .claude/skills/eli5 && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
eli5
GitHub stars
118
Used in
1 other repo
Token cost
~7.1k tokens
SKILL.md length
3,578 words
Files
6 (incl. references)
Skills in repo
19
Repo updated
First seen
Licence
MIT

At a glance

Transform technical jargon into clear explanations using before/after comparisons, metaphors, and practical context

  • Works in 6 steps: Context before details — Start with… → Tech-adjacent metaphors — Analogies… → Layered explanations — Multiple entry… → …
  • SKILL.md covers What I Do, Philosophy, When to Use Me and How I Work, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Eli5 is an agent skill from try-works/role-model. Transform technical jargon into clear explanations using before/after comparisons, metaphors, and practical context

Its SKILL.md is about 7.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `README.md`, `references/EXAMPLES_REFERENCE.md` and `references/content-type-guide.md`). Compatibility notes: opencode

The repository describes itself as: role-model is a protocol for assigning the right model for the right job. Use local and cloud AI together, or route between several cloud providers. The licence is MIT.

Example prompts

  • “/eli5”

Requirements

  • Compatibility (from SKILL.md): opencode

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Context before details — Start with "why" and "when" before "what" and "how"
  2. Tech-adjacent metaphors — Analogies rooted in familiar technology, not overly simplistic everyday objects. Acknowledge where metaphors…
  3. Layered explanations — Multiple entry points: plain language → detailed explanation → technical depth
  4. Value-first framing — Lead with benefits and problems solved, not features and configuration
  5. Explicit pitfalls — Address common misunderstandings directly
  6. Familiar connections — Bridge new ideas to concepts readers already know

What it can do on your machine

Read from SKILL.md and the folder at commit de1c04a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    opencode

    From compatibility in the SKILL.md frontmatter.

Context cost

Eli5 loads about 7.1k tokens when it runs, and up to ~29k if it reads all its reference files. Until then it costs about 30 tokens; SKILL.md has 3,578 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~30
When it runs · the whole SKILL.md, loaded when a task matches
~7.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~29k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from try-works/role-model at commit de1c04a, republished under its MIT licence (© try-works). 3,578 words, ~7,080 tokens.

Download SKILL.mdSave it as .claude/skills/eli5/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
eli5
description
Transform technical jargon into clear explanations using before/after comparisons, metaphors, and practical context
compatibility
opencode
license
MIT
metadata.audience
mixed (developers, IT admins, marketers, students, hobbyists)
metadata.workflow
technical-simplification
metadata.output_format
before-after-comparison
metadata.supported_formats
.md, .mdx

What I Do

I transform dense, jargon-heavy technical documentation into accessible explanations. Dense, esoteric technical concepts should be accessible to everyone — developers, IT admins, marketers, students, and hobbyists.

Key capabilities:

  • Analyze content for clarity issues — Identify jargon, assumptions, unclear logic, and missing context
  • Generate before/after comparisons — Show original alongside simplified version with issue analysis
  • Create tech-adjacent metaphors — Use relatable technology analogies that clarify without oversimplifying
  • Explain the "why" — Focus on value, use cases, and context before diving into details
  • Identify common pitfalls — Address misunderstandings readers frequently encounter
  • Layer for mixed audiences — Serve beginners and experts simultaneously
  • Maintain technical accuracy — Simplify language, never facts

Philosophy

Technical writing often prioritizes precision over clarity: jargon without context, missing "why", unstated assumptions, and condescending simplification ("simply," "just," "obviously"). ELI5 fixes this through:

  1. Context before details — Start with "why" and "when" before "what" and "how"
  2. Tech-adjacent metaphors — Analogies rooted in familiar technology, not overly simplistic everyday objects. Acknowledge where metaphors break down.
  3. Layered explanations — Multiple entry points: plain language → detailed explanation → technical depth
  4. Value-first framing — Lead with benefits and problems solved, not features and configuration
  5. Explicit pitfalls — Address common misunderstandings directly
  6. Familiar connections — Bridge new ideas to concepts readers already know

Audience: Readers are intelligent but lack specific context. Never write for the "lowest common denominator." Assume smart people who are unfamiliar with this particular domain.

Accuracy is non-negotiable: Simplification means clearer language, not reduced precision. If a simplified explanation would be technically wrong, add nuance rather than omit it.

Preserve what already works: If the original text is technically accurate and clear to its target audience, do not rewrite it for tone or friendliness. Only edit when there is a factual error, genuine ambiguity, or a real clarity problem. Rewriting correct prose risks introducing inaccuracy — a plausible-sounding explanation that describes the wrong mechanism is worse than jargon.

Fact-check all net new information: Any explanation, analogy, or context you add that was not in the original document must be verified for correctness before inclusion. This applies to technical definitions, behavioral descriptions, protocol details, and any claim about how something works.

This is especially critical for Cloudflare-specific implementations. Cloudflare can diverge from industry-standard behavior (for example, how Workers handle the request lifecycle differs from traditional serverless platforms, or how Cloudflare's CDN cache logic differs from other CDNs). Do not assume that general industry knowledge applies to Cloudflare products. When adding commentary about Cloudflare-specific behavior:

  1. Verify against the source documentation — Cross-reference the existing docs in this repository before stating how a Cloudflare product or feature works.
  2. Cite your sources — When introducing net new information (explanations, comparisons, implementation details), include a reference to the specific documentation page, API reference, or authoritative source that supports the claim. Use inline links or footnotes.
  3. Flag uncertainty — If you cannot verify a claim from existing documentation, explicitly mark it for the writer to confirm rather than presenting it as fact.
  4. Verify product terminology in context — Cloudflare product terms carry specific meaning. "Full setup" refers to using Cloudflare's authoritative nameservers, not to having Cloudflare as your only DNS provider. "Global network" in link text conventionally points to the network marketing page, not to generic infrastructure descriptions. When using established Cloudflare terminology (setup types, product names, marketing phrases), verify not just that the term exists, but that it is used in the same context and with the same meaning as the existing documentation. A real term applied in the wrong context is as misleading as a fabricated one.

Tone: Clear, direct, professional. Not condescending, not overly casual, not hyperbolic. Never use "simply," "just," "obviously," "clearly," "as everyone knows," or "it's easy to."

When to Use Me

Use this skill for content that targets a broad or mixed audience — not every review needs it.

Good candidates:

  • Security and networking docs (e.g. DDoS protection, WAF, Magic Transit, Tunnel) — readers often include IT admins, marketers, or decision-makers who lack deep networking background
  • Getting started and overview pages — first-touch content where readers have not yet built domain context
  • Concept pages aimed at non-developers — pages explaining "what" and "why" to audiences beyond software engineers
  • Cross-product docs (Zero Trust, SASE) — these span multiple domains and attract diverse readers

Skip or deprioritize for:

  • Developer-focused API and SDK references (e.g. Workers, D1, R2, Durable Objects, KV) — the audience is developers who are expected to know programming concepts, database terminology, and API conventions
  • Code-heavy tutorials targeting developers — readers self-select into these and already have the prerequisite knowledge
  • Configuration references with purely technical audiences — parameter tables, CLI references, and schema docs where jargon is the content

Use your judgment for everything else. Ask: "Would a reasonable reader of this page already know these terms?" If yes, this skill adds little value. On the other hand, if the following are true, this skill could provide significant value.

  • Content assumes too much prior knowledge
  • Jargon and acronyms are not explained
  • Documentation jumps to "how" without explaining "why"
  • Readers struggle to understand when/where to use something
  • You want feedback on what makes content confusing

How I Work

Workflow

1. Accept File Path

bash
/eli5 path/to/documentation.md

Supported: .md, .mdx

2. Read and Parse Content

I read the file, detect sections, analyze organization, and identify the content type.

Content types: Overview, Concept, How To, Reference, Tutorial

Detection signals:

  • Overview: Product name in title, feature lists, benefit statements, "Perfect for..." sections
  • Concept: "What is...", "How it works", conceptual explanations, "Why it matters"
  • How To: Numbered steps, "Prerequisites", action verbs in headings, verification sections
  • Reference: Tables, parameter lists, technical specifications, data types
  • Tutorial: "What you'll build", progressive code examples, "Time required"

After detection, I ask you to confirm the content type. Different types require different strategies:

TypeStrategy
OverviewProblem → Solution → Benefit
ConceptAnalogy → Plain explanation → Technical details
How ToContext → Multi-path steps (Dashboard + API)
ReferenceUse-case organization with two-tier descriptions
TutorialProgressive complexity with code explanations

3. Apply Enhancement Constraints

Before enhancing, enforce these limits. Target 1.5-2x expansion (not 5-10x). Enhance existing content with context, not replace it.

Maximum additions per document:

  • Problem/value statement: 2-4 sentences inline (not a separate section)
  • Use case examples: 1-2 per major concept, 5-15 lines each
  • Inline "why": 1-2 sentences when introducing features
  • Jargon definitions: Brief inline on first use
  • Troubleshooting: 1-2 critical issues only
  • Testing: 3-5 verification commands max

Preserve: All existing content, structure, diagrams, code examples, component usage, and flow.

Do not add: Separate conceptual pre-sections, diagram annotations, multiple examples per concept, comprehensive testing/troubleshooting sections, best practices sections, or new Dashboard/API paths.

Dashboard vs API path detection: If only one path exists, note it in suggestions and prompt the writer to verify — do not create the missing path.

4. Ask Which Sections to Simplify

Present these options and wait for a response:

  • All sections — Process the entire document
  • Specific sections — Choose from detected sections with line numbers
  • Auto-detect most complex — Prioritize by jargon density and assumption frequency
  • Custom range — Specify line numbers or section names

5. Analyze Selected Sections

For each section, I identify:

  • Jargon — Unexplained terms, undefined acronyms, terms with dual meanings
  • Assumptions — Unstated prerequisites, referenced concepts without explanation, skipped foundational steps
  • Unclear logic — Flow problems, missing transitions, dense paragraphs, unclear hierarchy
  • Context gaps — Missing "why", absent use cases, no "when to use this"

6. Extract Terminology

I compile a deduplicated list of all terms that may need glossary definitions or cross-links:

  • Undefined technical terms — Domain-specific words used without explanation
  • Acronyms — Initialisms not expanded on first use
  • Product/feature names — References to specific products, services, or features that lack links to their documentation
  • Concepts worth linking — Terms that have dedicated documentation pages elsewhere but are not linked

For each term I report: the term, where it appears (line number), whether it is defined in-context, and a suggested action (add glossary tooltip, add cross-link, or add inline definition).

GlossaryTooltip quality gate: Before suggesting a GlossaryTooltip for any term, read the actual glossary definition (in src/content/glossary/). Evaluate it against these criteria:

  • Is the definition accurate? If the glossary entry is vague, outdated, or technically imprecise, flag it for improvement rather than linking to it. A bad tooltip is worse than no tooltip.
  • Is the definition redundant with the surrounding sentence? If the tooltip would repeat nearly the same words as the prose it is attached to, skip it — the tooltip adds visual clutter without new understanding.
  • Does the definition stand alone? The reader sees the tooltip in isolation. If the glossary entry only makes sense in a different context or uses jargon of its own, flag it rather than linking.

When a glossary entry fails any of these checks, report it in the Terminology Index with the action "Flag glossary entry for review — [reason]" instead of "Add glossary tooltip."

Always include the Terminology Index in the output. If no terms need action, state that explicitly.

7. Generate Comparison

I produce a comparison with:

  • Original content preserved exactly
  • Issues identified with specific examples
  • Simplified version including: plain-language summary, clear explanation building from basics, why it matters, when you would use this, tech-adjacent metaphor, common pitfalls, related concepts

8. Report

I report: summary of improvements made, what made the original confusing, and the full terminology index.

Then proceed immediately to Step 9 (Adversarial Review). Do not prompt the user for next steps until the review is complete.

9. Adversarial Review

After presenting the report in Step 8, always launch a fresh subagent (Task tool, subagent_type: "general") to perform an adversarial review before prompting the user for next steps. Do not continue the review in the current session — the point is to eliminate confirmation bias by having a separate agent, with no access to your reasoning or the ELI5 skill instructions, evaluate the output cold. Do not skip this step.

Pass the subagent the following prompt (fill in the bracketed values):


Begin adversarial review prompt

You are a skeptical reviewer. Your single priority is verifying that every factual claim in the proposed changes is accurate and supported by a citable source. You assume claims are unsupported until proven otherwise.

You are NOT a style checker or formatter. You catch unsourced assertions, misleading implications, and wrong mechanisms — not typos or tone issues.

Original file: [original file path] Proposed changes: [full ELI5 output — the simplified/enhanced content]

Read both files carefully. Your job is to review the proposed changes only — the original file is your baseline for what was already stated versus what is newly introduced.

What counts as a claim

Any statement in the proposed changes that a reader could reasonably question:

  • Technical behavior ("Workers supports up to 128 MB of memory")
  • Comparisons ("faster than alternative X")
  • Numbers, limits, defaults, or quotas
  • Statements about how a product, protocol, or standard works
  • Simplified mechanism descriptions ("how it works" explanations added during simplification)
  • Analogies and metaphors — the 1:1 mapping claims ("X works like Y" requires that the mapped behavior actually matches how X works)
  • Net-new context — any "why," "when you'd use this," or "what problem it solves" framing not present in the original
  • Any claim about Cloudflare product behavior

Opinions, definitions created by the doc itself, and procedural steps ("Select Save") are not claims.

ELI5-specific focus areas

These are the highest-risk categories when documentation has been simplified. Prioritize them:

  1. Simplified mechanism descriptions — Any "how it works" explanation added during simplification that was not in the original. These carry the highest risk: a plausible-sounding explanation that describes the wrong mechanism is worse than the original jargon. Verify the actual mechanism against the source docs in this repository.

  2. Misleading nuance — Statements that are not outright wrong but flatten important nuance, creating a wrong mental model. Example: "Cloudflare generates a robots.txt file that instructs AI crawlers to stay away from your content" is misleading — robots.txt is a per-path allow/disallow mechanism, not a blanket block. The sentence omits that it specifies where crawlers may and may not go. Flag any statement where the simplification loses a meaningful distinction.

  3. Net-new claims — Any explanation, context, or framing added during simplification that was not present in the original document. Every piece of new information requires a citation. If the original said "zones pair with resolver policies" and the simplification adds "based on source IP, user identity, or domain," verify that all three of those selectors are actually supported.

  4. Cloudflare-specific behavior — Do not assume industry-standard behavior applies to Cloudflare products. Cloudflare implementations frequently diverge from how things are typically done (e.g., Workers request lifecycle vs. traditional serverless, Cloudflare CDN cache logic vs. other CDNs, how Cloudflare Tunnel health checks work vs. generic health check patterns). Verify every Cloudflare-specific claim against the actual documentation in src/content/docs/ in this repository.

  5. Over-generalization across categories — When a simplification says "all records," "the IP address" (singular), or "every request," verify whether the claim actually applies universally. DNS record types (A, AAAA, CNAME, MX, TXT, NS) have different proxying rules. Cloudflare returns multiple anycast IPs, not one. Protocol behaviors, plan-level features, and configuration defaults frequently vary by record type, plan, or product tier. Check that quantifiers ("all," "every," "any") and articles ("the" implying singular) are accurate. A statement that is true for A records may be false for MX records; a feature available on Enterprise may not exist on Free.

Show full SKILL.md (1,392 more words)Show less
Review process
  1. Extract — List every claim in the proposed changes. Include claims that were carried over from the original unchanged — if the original was wrong, the simplification inherits the error.
  2. Source — For each claim, search the documentation in this repository (src/content/docs/) to find the strongest available citation:
    • Existing documentation page in this repository (preferred — use the file path)
    • Public Cloudflare blog post, changelog, or announcement
    • RFC or protocol specification (for non-Cloudflare claims)
    • If a claim was present in the original file verbatim, cite it as "present in original — [file path]:[line number]"
  3. Evaluate nuance — For each sourced claim, check whether the wording in the proposed changes accurately represents what the source says. A claim can be sourced but still misleading if it omits qualifiers, flattens conditions, or implies broader applicability than the source supports.
  4. Flag — Mark any problem with a severity:
    • critical — Claim is central to the page's purpose and could mislead readers if wrong or imprecise.
    • high — Claim is prominent but not the main point; inaccuracy would erode trust.
    • medium — Claim is peripheral but still verifiable.
    • low — Claim is minor or widely accepted common knowledge.
  5. Report — Present findings in this format:
#Claim (exact text)SourceStatus
1"Workers KV supports keys up to 512 bytes"src/content/docs/kv/api/write-key-value-pairs.mdx✅ sourced
2"Latency is under 50 ms globally"—❌ unsourced (high)
3"instructs crawlers to stay away from your content"src/content/docs/bots/robots-txt.mdx — source says per-path allow/disallow, not blanket block⚠️ misleading (critical)
4"zones pair with resolver policies"present in original — path/to/file.mdx:34✅ sourced (original)
Rules
  • Never fix or rewrite content. Report only.
  • Every issue must include the exact text of the claim, not a vague summary.
  • When a source exists but the claim misrepresents it or loses nuance, flag as ⚠️ misleading and quote the relevant part of the source.
  • Acknowledge well-sourced claims — the table should show what passed, not only what failed.
  • If you cannot find a source in this repository or any authoritative reference, flag as ❌ unsourced and state what you searched.

End adversarial review prompt


When the subagent returns its findings, present the full claim table to the user. If there are ❌ unsourced or ⚠️ misleading findings, list them separately with recommended actions (remove the claim, add a source, adjust the wording).

Then ask: What would you like to do next?

  1. Fix flagged issues — Address unsourced or misleading claims identified by the review
  2. Suggest additional improvements
  3. Create a PR with changes
  4. Refine specific sections
  5. Apply changes to original file
  6. Keep as reference

Decision Framework

Should I simplify a term?

  • Replace or explain if: domain-specific jargon, most readers will not know it, a simpler term is equally accurate
  • Keep but define if: industry standard readers should learn, no simpler term is accurate, term appears frequently

Should I add content?

  • Yes if: "why" is missing, use cases are absent, common misunderstandings are not addressed
  • No if: original is already clear, addition would pad without value, reader can infer from context

Should I spell out a consequence or implication?

  • No if the target audience can infer the consequence from the stated cause. For example, "blocking health checks" does not need "which means Cloudflare may consider your tunnels unhealthy" for a networking audience. Trust domain expertise.
  • Yes only if the consequence is non-obvious, counterintuitive, or the audience genuinely lacks the domain knowledge to connect the dots.

Should I add a GlossaryTooltip?

  • Yes if: the glossary definition is accurate, adds information beyond what the sentence already says, and stands alone without additional context
  • No if: the glossary definition is vague, technically imprecise, or nearly identical to the surrounding sentence. Flag the glossary entry for review instead.
  • No if: the term is already clearly defined inline in the same paragraph

Should I add synonyms or aliases for a term?

  • No. One inline definition is enough. Do not pile on "also called X" aliases when the definition already explains the concept through its behavior. Define terms by what they do, not by listing alternative names.

Should I remove content?

  • Rarely. Only if genuinely redundant or tangential. Never remove caveats, accuracy qualifiers, or security warnings.

Quality Checklist

Before finalizing, verify:

  • Technical accuracy maintained
  • Jargon identified and explained
  • Assumptions stated explicitly
  • "Why" comes before "what" and "how"
  • Use cases are realistic
  • Metaphors have clear 1:1 mapping with stated limitations
  • No condescending language
  • Enhanced version is 1.5-2x original (not 5-10x)
  • Original structure preserved (not reorganized)
  • 1-2 examples max per concept
  • Diagrams left untouched
  • Already-correct prose left untouched (not rewritten for tone)
  • No consequence chains the audience can infer
  • No synonym glosses when behavior-based definitions exist
  • No rhetorical questions (examples stated as examples)
  • Bold formatting follows Cloudflare style guide (bold for clickable UI elements only — not used for sporadic emphasis in explanatory prose)
  • Every simplification describes the correct mechanism
  • Register matches the existing documentation voice
  • Adversarial review completed

Anti-patterns to avoid

These are patterns that feel like improvements but consistently make documentation worse. They were identified from human review of AI-generated edits.

1. Rewriting correct prose for "friendliness"

If the original sentence is factually accurate and structurally sound, do not rewrite it to sound warmer or simpler. Rewrites introduce risk of mechanical inaccuracy. Only touch sentences that have a concrete problem (wrong fact, ambiguous referent, undefined term, broken logic).

2. Adding consequence chains the reader can infer

Do not spell out "If X happens, then Y, which causes Z" when the audience already understands the causal chain. Example: telling a network engineer that blocked health checks cause tunnels to go unhealthy is stating the obvious. Ask: "Would a reasonable reader of this page already know this consequence?" If yes, omit it.

3. Adding synonym glosses ("also called X")

Do not append "also called 'default deny'" or similar aliases when the concept is already defined by its behavior in the same sentence. One definition is enough. Synonym stacking clutters without adding understanding.

4. Using rhetorical questions in documentation

Do not convert example lists into questions ("do you run VPN, NTP, or database services?"). State examples as examples. Documentation is not a conversation.

5. Implying mutual exclusivity between complementary features

Do not add phrases like "rather than writing rules from scratch" that imply one feature replaces another when both are used together. When two features complement each other, cross-reference them instead of contrasting them.

6. Describing the wrong mechanism with a plausible simplification

When simplifying how a system works, verify the simplification describes the actual mechanism. For example, saying "a Custom rule can change a Managed rule's action" is wrong if Custom rules actually take precedence due to evaluation order. A plausible-sounding but mechanically incorrect explanation is worse than the original jargon.

7. Over-specifying precision the audience already has

Do not explain that == means "equals" to an audience writing Wireshark-syntax filter expressions. Calibrate the level of inline definition to the actual audience of the page, not to a hypothetical beginner.

8. Using casual register in formal docs

"Let you" is too casual for Cloudflare docs. Use "allow you to" or state the action directly. Match the existing voice of the documentation, not a conversational ideal.

9. Conflating related but distinct concepts in a single statement

When simplifying, do not merge two separate concepts into one sentence in a way that implies they are the same thing or that one requires the other. Example: "CNAME flattening resolves the chain and returns a Cloudflare anycast IP" conflates CNAME flattening (a DNS resolution behavior) with proxying (a traffic-routing decision) — you can have CNAME flattening with proxy off, in which case no Cloudflare IP is returned. Similarly, "Full setup means Cloudflare is your only DNS provider" conflates the setup type (using Cloudflare authoritative nameservers) with exclusivity (having no other provider). Each concept should be introduced on its own terms, even if they often appear together. If two features interact, describe them separately and then explain the relationship.

Edge Cases

  • Very long documents (>1000 lines): Ask which sections to prioritize, offer to process in chunks
  • Already-clear content: Acknowledge clarity, suggest minor improvements only
  • Highly technical content: Maintain accuracy above all, use progressive disclosure
  • Code-heavy docs: Add plain-language explanations of what code accomplishes and why it is structured that way
  • Multiple audience types: Use labeled sections ("For developers:" / "For non-technical readers:")

Output Format

Produce output following this template exactly. All sections are required.

markdown
# ELI5 Simplified: [Original Doc Name]

**Original:** `[file path]`
**Sections simplified:** [count/list]

---

## Simplification Overview

**What was confusing:**
- [Issue pattern 1]
- [Issue pattern 2]

**Approach taken:**
- [Strategy 1]
- [Strategy 2]

---

## Section: [Original Heading]

### Original Content
[Exact text from source, preserved]

### Issues Identified
**Jargon:** [terms and why problematic]
**Assumptions:** [unstated prerequisites]
**Unclear Logic:** [structural issues]

### Simplified Version
**In Plain Language:** [One-sentence distillation]
**What It Is:** [2-3 paragraphs building from basics]
**Why It Matters:** [Benefits and value]
**When You'd Use This:** [Use cases with context]
**Think of It Like:** [Tech-adjacent metaphor]
**Where this metaphor breaks down:** [Limitations]
**Common Pitfalls:** [Misunderstanding → Correction]
**Related Concepts:** [Connections to familiar ideas]

---

[Repeat for each section]

---

## Terminology Index

| Term | Line | Defined? | Suggested Action |
| ---- | ---- | -------- | ---------------- |
| [term] | [line number] | Yes/No | Add glossary tooltip / Add cross-link to [page] / Add inline definition |

---

## Summary & Recommendations

**Key improvements made:** [list]
**Patterns noticed:** [meta-analysis]

## Suggestions for Enhancement

Line-numbered recommendations for further improvements:

| Line(s) | Current Approach | Suggested Enhancement | Why | Priority |
| ------- | ---------------- | --------------------- | --- | -------- |
| [lines] | [what exists] | [what to change] | [why it improves accessibility] | High/Medium/Low |

References

  • Content type detection criteria: references/content-type-guide.md
  • Before/after pattern templates: references/pattern-library.md
  • Full examples: EXAMPLES_REFERENCE.md

© try-works, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in .agents/skills/eli5 of try-works/role-model.

  • SKILL.md
  • README.md
  • recommendations/internal-dns/index.eli5.mdx
  • references/EXAMPLES_REFERENCE.md
  • references/content-type-guide.md
  • references/pattern-library.md

Open the folder on GitHubat commit de1c04a

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in try-works/role-model, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Eli5 next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Eli5 compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Eli5 this skilltry-works/role-model1181 repos~7.1kAutomated safety check: PassMIT
TransformersK-Dense-AI/scientific-agent-skills48k1 repos~2.8kAutomated safety check: NotesApache-2.0
Hugging Face Transformers Usagedavila7/claude-code-templates32k12 repos~1.2kAutomated safety check: PassMIT
Transformers JSsickn33/agentic-awesome-skills47k1 repos~444Automated safety check: PassApache-2.0
Eli5DreambigOu/ELI51.7k—~2kAutomated safety check: PassMIT
Fp Data Transformssickn33/agentic-awesome-skills47k2 repos~2.4kAutomated safety check: PassMIT

Similar skills

  • Transformers

    K-Dense-AI/scientific-agent-skills

    Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks.

    48k GitHub starsUsed in 1 repo~2.8k tokens
    AI & LLM EngineeringAuto-check: notes
  • Hugging Face Transformers Usage

    davila7/claude-code-templates

    Loads pre-trained Hugging Face Transformers models for text, vision and audio tasks, runs inference with pipelines and fine-tunes on custom datasets.

    32k GitHub starsUsed in 12 repos~1.2k tokens
    AI & LLM EngineeringAuto-check passed
  • Transformers JS

    sickn33/agentic-awesome-skills

    Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript.

    47k GitHub starsUsed in 1 repo~444 tokens
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  • Eli5

    DreambigOu/ELI5

    Explain any topic, code, concept, or error tailored to a specific audience's level of understanding.

    1.7k GitHub stars~2k tokensUpdated 6 mo ago
    Auto-check passed
  • Fp Data Transforms

    sickn33/agentic-awesome-skills

    Everyday data transformations using functional patterns - arrays, objects, grouping, aggregation, and null-safe access

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  • Eli5

    coldteadotai/pr-lens

    WHAT: Explains a codebase, a folder, a feature, a command or a pull request to someone who knows nothing about it, as a PR Lens canvas whose walkthrough builds the picture one part at a time.

    1.9k GitHub stars~2.3k tokensUpdated 3 days ago
    DevelopmentAuto-check passed

More from try-works/role-model

All 19 skills in this repo
  • E2E Testing Patterns

    try-works/role-model

    Master end-to-end testing with Playwright and Cypress to build reliable test suites that catch bugs, improve confidence, and enable fast deployment.

    118 GitHub starsUsed in 14 repos~990 tokens
    Auto-check passed
  • UI Design System

    try-works/role-model

    React UI component systems with TailwindCSS + Radix + shadcn/ui.

    118 GitHub stars~5k tokensUpdated yesterday
    Auto-check passed
  • Swiss Design

    try-works/role-model

    Apply a Swiss International Style design system using Tailwind CSS.

    118 GitHub starsUsed in 1 repo~3.2k tokens
    Auto-check passed
  • Contributing

    try-works/role-model

    A skill your agent uses when contributing to the Cloudflare Docs repository — writing or editing documentation pages, choosing content types or components, adding changelog entries, reviewing docs…

    118 GitHub stars~1.3k tokensUpdated yesterday
    Auto-check passed
  • Effect V3 To V4

    try-works/role-model

    A skill your agent uses when migrating a codebase from Effect v3 to Effect v4, upgrading effect or any @effect/ package across the v3/v4 boundary.

    118 GitHub starsUsed in 1 repo~2k tokens
    Auto-check passed
  • Turnstile Spin

    try-works/role-model

    Set up Cloudflare Turnstile end-to-end in a project — scan the codebase, create the widget via the Cloudflare API, deploy the managed siteverify Worker, write the frontend snippets, validate, and…

    118 GitHub stars~3.8k tokensUpdated yesterday
    Auto-check passed

Questions about Eli5

What does Eli5 do?

Transform technical jargon into clear explanations using before/after comparisons, metaphors, and practical context. Eli5 is an agent skill from try-works/role-model.

How do I install Eli5 in Claude Code?

Run `npx skills add try-works/role-model --skill eli5 -a claude-code`. Or copy the skill folder (.agents/skills/eli5 in try-works/role-model) into .claude/skills/eli5 in your project. Claude Code loads it when a task matches its description.

How do I install Eli5 in Codex?

Run `npx skills add try-works/role-model --skill eli5 -a codex`. Or copy the skill folder (.agents/skills/eli5 in try-works/role-model) into .agents/skills/eli5 in your project. Codex loads it when a task matches its description.

Can I use Eli5 in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add try-works/role-model --skill eli5 -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/eli5, .gemini/skills/eli5, .github/skills/eli5 and .opencode/skills/eli5 in your project.

What does Eli5 need to run?

SKILL.md names no scripts, command-line tools or credentials: Eli5 is instructions for the agent only. Compatibility (from SKILL.md): opencode.

Does Eli5 access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Eli5 safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Eli5 use?

Eli5 is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Eli5 use?

About 7.1k tokens (SKILL.md is roughly 28k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 22k tokens, read only when the agent opens those files.

What are the alternatives to Eli5?

Skills that share tags, products or a category with Eli5: Transformers (K-Dense-AI/scientific-agent-skills, 48k stars), Hugging Face Transformers Usage (davila7/claude-code-templates, 32k stars), Transformers JS (sickn33/agentic-awesome-skills, 47k stars) and Eli5 (DreambigOu/ELI5, 1.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Eli5?

try-works (a GitHub user) maintains it in try-works/role-model, which has 118 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 7, 2026.

Source: try-works/role-model on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.