Prompt Lab
Mathews-Tom/armory
LLM prompt engineering: analyzes failure modes, generates variants (direct, few-shot, CoT), designs rubrics, produces test suites.
Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt…
Install Prompt Engineer Toolkit
$ npx skills add alirezarezvani/claude-skills --skill prompt-engineer-toolkit -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alirezarezvani/claude-skills prompt-engineer-toolkit --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/marketing-skill/skills/prompt-engineer-toolkit .claude/skills/prompt-engineer-toolkit && rm -rf skills-srcUse ~/.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/
Install the "prompt-engineer-toolkit" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/marketing-skill/skills/prompt-engineer-toolkit into .claude/skills/prompt-engineer-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer-toolkit", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/alirezarezvani/claude-skills/tree/main/marketing-skill/skills/prompt-engineer-toolkitType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add alirezarezvani/claude-skills --skill prompt-engineer-toolkit -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alirezarezvani/claude-skills prompt-engineer-toolkit --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/marketing-skill/skills/prompt-engineer-toolkit .agents/skills/prompt-engineer-toolkit && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "prompt-engineer-toolkit" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/marketing-skill/skills/prompt-engineer-toolkit into .agents/skills/prompt-engineer-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer-toolkit", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill prompt-engineer-toolkit -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alirezarezvani/claude-skills prompt-engineer-toolkit --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/marketing-skill/skills/prompt-engineer-toolkit .cursor/skills/prompt-engineer-toolkit && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "prompt-engineer-toolkit" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/marketing-skill/skills/prompt-engineer-toolkit into .cursor/skills/prompt-engineer-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer-toolkit", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/alirezarezvani/claude-skills.git --path marketing-skill/skills/prompt-engineer-toolkit--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add alirezarezvani/claude-skills --skill prompt-engineer-toolkit -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alirezarezvani/claude-skills prompt-engineer-toolkit --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/marketing-skill/skills/prompt-engineer-toolkit .gemini/skills/prompt-engineer-toolkit && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "prompt-engineer-toolkit" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/marketing-skill/skills/prompt-engineer-toolkit into .gemini/skills/prompt-engineer-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer-toolkit", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install alirezarezvani/claude-skills prompt-engineer-toolkitInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add alirezarezvani/claude-skills --skill prompt-engineer-toolkit -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/marketing-skill/skills/prompt-engineer-toolkit .github/skills/prompt-engineer-toolkit && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "prompt-engineer-toolkit" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/marketing-skill/skills/prompt-engineer-toolkit into .github/skills/prompt-engineer-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer-toolkit", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill prompt-engineer-toolkit -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install alirezarezvani/claude-skills prompt-engineer-toolkit --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/marketing-skill/skills/prompt-engineer-toolkit .opencode/skills/prompt-engineer-toolkit && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "prompt-engineer-toolkit" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/marketing-skill/skills/prompt-engineer-toolkit into .opencode/skills/prompt-engineer-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineer-toolkit", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
prompt-engineer-toolkit- GitHub stars
- 28k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 521 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 342
- Repo updated
- First seen
- Licence
- MIT
At a glance
Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt…
- Works in 4 steps: Run Prompt A/B Test → Choose Winner With Evidence → Version Prompts → …
- A marketing team relies on AI-generated content and needs prompt quality to be measurable and safe —
- SKILL.md covers Overview, Core Capabilities, Key Workflows and Script Interfaces, plus 5 more sections
- Runs Python scripts from its folder; calls python3
What it does
Prompt Engineer Toolkit is an agent skill from alirezarezvani/claude-skills. Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt templates (ad copy, email campaigns, social posts, landing pages, SEO meta), and an LLM-governance playbook for marketing teams (claim discipline, disclosure rules, human-review gates). Use when a marketing team relies on AI-generated content and needs prompt quality to be measurable and safe — or when the user mentions…
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `README.md`, `references/evaluation-rubric.md` and `references/prompt-templates.md`).
It sits in AI & LLM Engineering, covering Prompt engineering and Test generation. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.
When your agent uses it
- A marketing team relies on AI-generated content and needs prompt quality to be measurable and safe —
- The user mentions prompt engineering
- Improve my prompts
- Prompt templates
Example prompts
- “prompt engineering,”
- “improve my prompts,”
- “prompt templates,”
- “/prompt-engineer-toolkit”
Requirements
- Python 3
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 19392f7. 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
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
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.
Context cost
Prompt Engineer Toolkit loads about 1.4k tokens when it runs, and up to ~5.7k if it reads all its reference files. Until then it costs about 170 tokens; SKILL.md has 521 words of instructions outside code blocks.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 521 words, ~1,442 tokens.
Download SKILL.mdSave it as
.claude/skills/prompt-engineer-toolkit/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- prompt-engineer-toolkit
- description
- Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt templates (ad copy, email campaigns, social posts, landing pages, SEO meta), and an LLM-governance playbook for marketing teams (claim discipline, disclosure rules, human-review gates). Use when a marketing team relies on AI-generated content and needs prompt quality to be measurable and safe — or when the user mentions 'prompt engineering,' 'improve my prompts,' 'prompt templates,' 'prompt versioning,' 'AI content workflow,' or 'AI governance for marketing.'
- license
- MIT
- metadata.version
- 1.0.0
- metadata.author
- Alireza Rezvani
- metadata.category
- marketing
- metadata.updated
- 2026-03-06
Prompt Engineer Toolkit
Overview
Use this skill to move prompts from ad-hoc drafts to production assets with repeatable testing, versioning, and regression safety. It emphasizes measurable quality over intuition. Apply it when launching a new LLM feature that needs reliable outputs, when prompt quality degrades after model or instruction changes, when multiple team members edit prompts and need history/diffs, when you need evidence-based prompt choice for production rollout, or when you want consistent prompt governance across environments.
Core Capabilities
- A/B prompt evaluation against structured test cases
- Quantitative scoring for adherence, relevance, and safety checks
- Prompt version tracking with immutable history and changelog
- Prompt diffs to review behavior-impacting edits
- Reusable prompt templates and selection guidance
- Regression-friendly workflows for model/prompt updates
Key Workflows
1. Run Prompt A/B Test
Prepare JSON test cases and run:
python3 scripts/prompt_tester.py \
--prompt-a-file prompts/a.txt \
--prompt-b-file prompts/b.txt \
--cases-file testcases.json \
--runner-cmd 'my-llm-cli --prompt {prompt} --input {input}' \
--format textInput can also come from stdin/--input JSON payload.
2. Choose Winner With Evidence
The tester scores outputs per case and aggregates:
- expected content coverage
- forbidden content violations
- regex/format compliance
- output length sanity
Use the higher-scoring prompt as candidate baseline, then run regression suite.
3. Version Prompts
# Add version
python3 scripts/prompt_versioner.py add \
--name support_classifier \
--prompt-file prompts/support_v3.txt \
--author alice
# Diff versions
python3 scripts/prompt_versioner.py diff --name support_classifier --from-version 2 --to-version 3
# Changelog
python3 scripts/prompt_versioner.py changelog --name support_classifier4. Regression Loop
- Store baseline version.
- Propose prompt edits.
- Re-run A/B test.
- Promote only if score and safety constraints improve.
Script Interfaces
python3 scripts/prompt_tester.py --help- Reads prompts/cases from stdin or
--input - Optional external runner command
- Emits text or JSON metrics
- Reads prompts/cases from stdin or
python3 scripts/prompt_versioner.py --help- Manages prompt history (
add,list,diff,changelog) - Stores metadata and content snapshots locally
- Manages prompt history (
Pitfalls, Best Practices & Review Checklist
Avoid these mistakes:
- Picking prompts from single-case outputs — use a realistic, edge-case-rich test suite.
- Changing prompt and model simultaneously — always isolate variables.
- Missing
must_not_contain(forbidden-content) checks in evaluation criteria. - Editing prompts without version metadata, author, or change rationale.
- Skipping semantic diffs before deploying a new prompt version.
- Optimizing one benchmark while harming edge cases — track the full suite.
- Model swap without rerunning the baseline A/B suite.
Before promoting any prompt, confirm:
- Task intent is explicit and unambiguous.
- Output schema/format is explicit.
- Safety and exclusion constraints are explicit.
- No contradictory instructions.
- No unnecessary verbosity tokens.
- A/B score improves and violation count stays at zero.
Show full SKILL.md (168 more words)Show less
References
- references/prompt-templates.md — 6 production marketing templates (ad copy, email sequence, social repurposing, landing sections, SEO meta, brand-voice rewrite) plus generic building blocks; each written to be graded by
prompt_tester.py - references/technique-guide.md — technique-selection table for marketing tasks + the LLM-governance stack for marketing teams (claim discipline, disclosure rules, data boundaries, human-review gates)
- references/evaluation-rubric.md — mechanical scoring weights, acceptance gates, marketing quality dimensions, test-suite design, and eval anti-patterns
- README.md
Evaluation Design
Each test case should define:
input: realistic production-like inputexpected_contains: required markers/contentforbidden_contains: disallowed phrases or unsafe contentexpected_regex: required structural patterns
This enables deterministic grading across prompt variants.
Versioning Policy
- Use semantic prompt identifiers per feature (
support_classifier,ad_copy_shortform). - Record author + change note for every revision.
- Never overwrite historical versions.
- Diff before promoting a new prompt to production.
Rollout Strategy
- Create baseline prompt version.
- Propose candidate prompt.
- Run A/B suite against same cases.
- Promote only if winner improves average and keeps violation count at zero.
- Track post-release feedback and feed new failure cases back into test suite.
© alirezarezvani, 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 6 other files (scripts, references) in marketing-skill/skills/prompt-engineer-toolkit of alirezarezvani/claude-skills.
- SKILL.md
- README.md
- references/evaluation-rubric.md
- references/prompt-templates.md
- references/technique-guide.md
- scripts/prompt_tester.py
- scripts/prompt_versioner.py
Open the folder on GitHubat commit 19392f7
Compare with similar skills
Prompt Engineer Toolkit 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Prompt Engineer Toolkit this skillalirezarezvani/claude-skills | 28k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Prompt LabMathews-Tom/armory | 328 | — | ~2.1k | Automated safety check: Pass | MIT | |
| Prompt Engineering UIHermeticOrmus/LibreUIUX-Claude-Code | 113 | — | ~3.6k | Automated safety check: Pass | MIT | |
| Agento11y Eval Startergrafana/agento11y | 127 | — | ~6.4k | Automated safety check: Pass | Apache-2.0 | |
| Prompt Improverseverity1/claude-code-prompt-improver | 1.9k | 1 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Prompt Engineering Patternsynulihao/AgentSkillOS | 617 | 14 repos | ~1.7k | Automated safety check: Pass | None |
Similar skills
Prompt Engineering UI
HermeticOrmus/LibreUIUX-Claude-Code
Prompt patterns for consistent UI generation: stating design intent precisely, component specification formats, few-shot examples, and structured refinement loops.
- Official
Agento11y Eval Starter
grafana/agento11y
Use early in an AI-agent project — before ship, before real traffic — to decide which evaluations to set up and to scaffold a starter experiment.
Prompt Improver
severity1/claude-code-prompt-improver
This skill enriches vague prompts with targeted research and clarification before execution.
Prompt Engineering Patterns
ynulihao/AgentSkillOS
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production.
Patch Creation
Piebald-AI/tweakcc
Create and register new patches for tweakcc. An agent skill from Piebald-AI/tweakcc.
More from alirezarezvani/claude-skills
All 342 skills in this repoAgile Product Owner
alirezarezvani/claude-skills
Writes INVEST-checked user stories with acceptance criteria, splits epics, plans sprints from velocity and ranks the backlog with a weighted score.
Product Strategist
alirezarezvani/claude-skills
OKR cascade toolkit for product leaders: generates aligned company-to-team OKRs from five strategy types and scores how well they line up.
App Store Optimization
alirezarezvani/claude-skills
App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store.
AWS Solution Architect
alirezarezvani/claude-skills
Design AWS architectures for startups using serverless patterns and IaC templates.
Campaign Analytics
alirezarezvani/claude-skills
Calculates attribution, funnel and ROI figures for marketing campaigns with three Python scripts that need only the standard library.
Code to PRD
alirezarezvani/claude-skills
Reverse-engineers a frontend, backend or fullstack codebase into a product requirements document with per-page docs, an enum dictionary and an API inventory.
Related
Questions about Prompt Engineer Toolkit
What does Prompt Engineer Toolkit do?
Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt…. Prompt Engineer Toolkit is an agent skill from alirezarezvani/claude-skills. Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt templates (ad copy, email campaigns, social posts, landing pages, SEO meta), and an LLM-governance playbook for marketing teams (claim discipline, disclosure rules, human-review gates).
When should I use Prompt Engineer Toolkit?
Prompt Engineer Toolkit fits situations like: A marketing team relies on AI-generated content and needs prompt quality to be measurable and safe —; the user mentions prompt engineering; improve my prompts; prompt templates.
How do I install Prompt Engineer Toolkit in Claude Code?
Run `npx skills add alirezarezvani/claude-skills --skill prompt-engineer-toolkit -a claude-code`. Or copy the skill folder (marketing-skill/skills/prompt-engineer-toolkit in alirezarezvani/claude-skills) into .claude/skills/prompt-engineer-toolkit in your project. Claude Code loads it when a task matches its description.
How do I install Prompt Engineer Toolkit in Codex?
Run `npx skills add alirezarezvani/claude-skills --skill prompt-engineer-toolkit -a codex`. Or copy the skill folder (marketing-skill/skills/prompt-engineer-toolkit in alirezarezvani/claude-skills) into .agents/skills/prompt-engineer-toolkit in your project. Codex loads it when a task matches its description.
Can I use Prompt Engineer Toolkit 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 alirezarezvani/claude-skills --skill prompt-engineer-toolkit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-engineer-toolkit, .gemini/skills/prompt-engineer-toolkit, .github/skills/prompt-engineer-toolkit and .opencode/skills/prompt-engineer-toolkit in your project.
What does Prompt Engineer Toolkit need to run?
Going by SKILL.md and its folder, Prompt Engineer Toolkit needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Prompt Engineer Toolkit 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 Prompt Engineer Toolkit 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Prompt Engineer Toolkit use?
Prompt Engineer Toolkit 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 Prompt Engineer Toolkit use?
About 1.4k tokens (SKILL.md is roughly 5.8k 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 4.2k tokens, read only when the agent opens those files.
What are the alternatives to Prompt Engineer Toolkit?
Skills that share tags, products or a category with Prompt Engineer Toolkit: Prompt Lab (Mathews-Tom/armory, 328 stars), Prompt Engineering UI (HermeticOrmus/LibreUIUX-Claude-Code, 113 stars), Agento11y Eval Starter (grafana/agento11y, 127 stars) and Prompt Improver (severity1/claude-code-prompt-improver, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Prompt Engineer Toolkit?
alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,891 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.
Source: alirezarezvani/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.