New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Skill Meta Prompt

by nyldn in nyldn/claude-octopus

Craft better prompts using proven optimization techniques — use when your prompt needs refinement

MITAuto-check passedAgent Workflows

Install Skill Meta Prompt

skills CLI
$ npx skills add nyldn/claude-octopus --skill skill-meta-prompt -a claude-code

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

GitHub CLI
$ gh skill install nyldn/claude-octopus skill-meta-prompt --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/nyldn/claude-octopus.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/skill-meta-prompt .claude/skills/skill-meta-prompt && 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
skill-meta-prompt
GitHub stars
4.2k
Used in
1 other repo
Token cost
~3.9k tokens
SKILL.md length
802 words
Files
2
Skills in repo
62
Repo updated
First seen
Licence
MIT

At a glance

Craft better prompts using proven optimization techniques — use when your prompt needs refinement

  • Works in 4 steps: Requirement Gathering → 4: Analysis & Design → Prompt Assembly → …
  • Your prompt needs refinement
  • SKILL.md covers Overview, The Five Techniques, Phase 1: Requirement Gathering and Phase 2-4: Analysis & Design, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Skill Meta Prompt is an agent skill from nyldn/claude-octopus. Craft better prompts using proven optimization techniques — use when your prompt needs refinement

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Agent Workflows. The repository describes itself as: Run multiple AI models against the same research, design, or coding task. Surface disagreements before you ship. The licence is MIT.

When your agent uses it

  • Your prompt needs refinement

Example prompts

  • “/skill-meta-prompt”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Requirement Gathering
  2. 4: Analysis & Design
  3. Prompt Assembly
  4. Output & Iteration

What it can do on your machine

Read from SKILL.md and the folder at commit c812f5e. 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 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.

Context cost

Skill Meta Prompt loads about 3.9k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 802 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

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 nyldn/claude-octopus at commit c812f5e, republished under its MIT licence (© nyldn). 802 words, ~3,902 tokens.

Download SKILL.mdSave it as .claude/skills/skill-meta-prompt/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
skill-meta-prompt
description
Craft better prompts using proven optimization techniques — use when your prompt needs refinement
disable-model-invocation
true

Host: Codex CLI — This skill was designed for Claude Code and adapted for Codex. Cross-reference commands use installed skill names in Codex rather than /octo:* slash commands. Use the active Codex shell and subagent tools. Do not claim a provider, model, or host subagent is available until the current session exposes it. For host tool equivalents, see skills/blocks/codex-host-adapter.md.

Meta-Prompt Generator Skill

Overview

Generate well-structured, verifiable prompts for any use case. Applies proven meta-prompting techniques to minimize hallucination and maximize effectiveness.

┌─────────────────────────────────────────────────────────────────────────────┐
│                       META-PROMPT GENERATION                                 │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  Phase 1: Requirement Gathering                                             │
│       → Understand the primary goal/role                                    │
│       → Clarify expected outputs                                            │
│       → Identify accuracy requirements                                      │
│       ↓                                                                     │
│  Phase 2: Task Analysis                                                     │
│       → Apply Technique 1: Task Decomposition                               │
│       → Identify if complex enough for subtasks                             │
│       → Map dependencies between subtasks                                   │
│       ↓                                                                     │
│  Phase 3: Expert Assignment                                                 │
│       → Apply Technique 5: Specialized Experts                              │
│       → Assign personas to subtasks                                         │
│       → Apply Technique 2: Fresh Eyes Review                                │
│       ↓                                                                     │
│  Phase 4: Verification Design                                               │
│       → Apply Technique 3: Iterative Verification                           │
│       → Build in checking steps                                             │
│       → Apply Technique 4: No Guessing                                      │
│       ↓                                                                     │
│  Phase 5: Prompt Assembly                                                   │
│       → Structure: Role, Context, Instructions, Constraints, Format         │
│       → Add verification hooks                                              │
│       → Include uncertainty disclaimers                                     │
│       ↓                                                                     │
│  Phase 6: Output & Iteration                                                │
│       → Present generated prompt                                            │
│       → Offer refinement                                                    │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

The Five Techniques

Technique 1: Task Decomposition

What: Break complex tasks into smaller, manageable subtasks.

When to use:

  • Task has multiple distinct steps
  • Different expertise needed for different parts
  • Risk of getting lost in complexity

How to apply:

  1. List all components of the task
  2. Identify dependencies (what must happen first)
  3. Group related components
  4. Order by logical sequence

Example:

Task: "Create a technical blog post about OAuth 2.0"

Decomposition:
1. Research Phase
   - Gather OAuth 2.0 specifications
   - Find common implementation examples
   - Identify security best practices
   
2. Structure Phase
   - Outline main sections
   - Plan code examples
   - Design diagrams/visuals
   
3. Writing Phase
   - Write introduction
   - Write technical sections
   - Write conclusion/CTA
   
4. Review Phase
   - Technical accuracy check
   - Code example testing
   - Readability review
Technique 2: Fresh Eyes Review

What: Use different "experts" for creation vs. validation. Never use the same expert to both create and verify.

When to use:

  • Output needs to be accurate
  • Risk of blind spots from creator
  • Quality assurance is critical

How to apply:

  1. Assign Creator Expert for initial work
  2. Assign different Reviewer Expert for validation
  3. Reviewer should not have seen creation process
  4. Loop back to Creator if issues found

Example:

Creator: "Expert Technical Writer" produces article
Reviewer: "Expert Security Engineer" verifies OAuth claims
Reviewer: "Expert Developer" tests code examples

NOT: Same expert writes AND reviews their own work
Technique 3: Iterative Verification

What: Build explicit verification steps into the task, especially for error-prone outputs.

When to use:

  • Mathematical calculations
  • Code generation
  • Factual claims
  • Multi-step reasoning

How to apply:

  1. After each significant output, add verification step
  2. For calculations: "Now verify this by [alternative method]"
  3. For code: "Test this code against [test cases]"
  4. For claims: "Confirm this by [citing source]"

Example:

Step 1: Calculate discount price
Step 2: VERIFY - recalculate from opposite direction
Step 3: If mismatch, identify error and recalculate
Step 4: Only proceed when both methods match
Technique 4: No Guessing

What: Never assume unverified facts. Disclaim uncertainty explicitly.

When to use:

  • ALWAYS (this is a default behavior)
  • Especially for: dates, statistics, quotes, technical specifications

How to apply:

  1. If uncertain, say "I'm not certain about..."
  2. If no data, say "I don't have information on..."
  3. Ask for sources rather than inventing
  4. Distinguish between "likely" and "confirmed"

Disclaimer templates:

"Note: This figure is approximate and should be verified."
"I don't have access to [specific data]. Please provide or verify."
"This is based on general patterns; your specific case may differ."
Technique 5: Specialized Experts

What: Spawn domain-specific personas for complex subtasks.

When to use:

  • Task requires specialized knowledge
  • Different perspectives would improve quality
  • Cross-functional work needed

Available expert archetypes:

ExpertUse For
Expert WriterContent, copy, documentation
Expert MathematicianCalculations, proofs, statistics
Expert PythonPython code, data analysis
Expert SecuritySecurity review, threat modeling
Expert ArchitectSystem design, trade-offs
Expert ReviewerQuality assurance, error-finding
Expert StrategistPlanning, prioritization

How to apply:

"For this subtask, adopt the persona of Expert [X].
Your expertise includes [specific areas].
Focus exclusively on [your assigned task].
You have no memory of previous context—all needed information is below."

Phase 1: Requirement Gathering

Initial Prompt
markdown
**Meta-Prompt Generator**

I'll help you create an effective, verifiable prompt.

**Questions:**

1. **What is the main goal?**
   What should this prompt help someone accomplish?

2. **What's the expected output?**
   (e.g., document, code, analysis, decision)

3. **How important is accuracy?**
   - Critical (factual, technical, or high-stakes)
   - Moderate (useful but not mission-critical)
   - Flexible (creative, exploratory)

4. **Any specific constraints?**
   (length, format, tone, tools available)
Minimum Information Needed
  • Primary goal (REQUIRED)
  • Output type (REQUIRED)
  • Accuracy requirements (can assume moderate)
  • Constraints (optional, will use sensible defaults)

If information is missing, ask ONE clarifying question at a time.

Phase 2-4: Analysis & Design

After gathering requirements, analyze internally:

Task Complexity Assessment
ComplexityIndicatorsApproach
SimpleSingle step, one outputDirect prompt, no decomposition
Moderate2-3 steps, clear sequenceLight decomposition, one expert
Complex4+ steps, dependenciesFull decomposition, multiple experts
Show full SKILL.md (307 more words)Show less
Expert Assignment Matrix
Task TypeCreator ExpertReviewer Expert
Technical writingExpert WriterExpert Engineer
Code generationExpert DeveloperExpert Reviewer
AnalysisExpert AnalystExpert Strategist
CreativeExpert CreativeExpert Editor
Verification Points

For the task, identify where verification is needed:

StepRiskVerification Method
[step][what could go wrong][how to verify]

Phase 5: Prompt Assembly

Output Format

You MUST return the generated prompt in this exact format:

markdown
# [Prompt Title]

## Role
[Short, direct role definition]
[Emphasize verification and uncertainty disclaimers]

## Context
[User's task and goals]
[Background information provided]
[Clarifications gathered]

## Instructions

### Phase 1: [First Phase Name]
1. [Step 1]
2. [Step 2]
3. **Verification:** [How to verify this phase]

### Phase 2: [Second Phase Name]
1. [Step 1]
2. [Step 2]
3. **Verification:** [How to verify this phase]

[Continue phases as needed...]

### Expert Assignments (if applicable)
- **[Expert Type]:** Handles [specific subtask]
- **[Reviewer Type]:** Validates [what they check]

## Constraints
- [Constraint 1]
- [Constraint 2]
- [Accuracy requirement: how to handle uncertainty]

## Output Format
[Specify exactly how the output should be structured]
[Include all required sections]

## Verification Checklist
Before considering complete:
- [ ] [Verification item 1]
- [ ] [Verification item 2]
- [ ] [Accuracy disclaimers added where needed]

## Examples (if provided)
[Context or examples from user]
Model-Specific Adjustments

Tune the assembled prompt to the model that will execute it:

  • Current frontier roster: apply skills/blocks/frontier-model-routing.md when choosing between Opus 5, GPT-5.6, Sonnet 5, Fable 5.1, Astra, or cheaper seats.
  • Claude Fable 5.1 or 5: apply skills/blocks/fable5-prompting.md to either model ID. In short: never instruct the model to reveal or transcribe its reasoning; replace step-by-step micromanagement with a boundary plus checkable acceptance criteria; drop "CRITICAL"/"MUST" emphasis unless strict compliance is required; add grounded-progress and act-when-ready language for long runs.
  • GPT-6 Astra: reserve it for a bounded pass after GPT-5.6 Sol fails a hard acceptance test. Do not treat Astra plus GPT-5.6 as independent review.
  • Codex / GPT-5.6: apply docs/GPT-5.6-PROMPTING.md; give it a concrete outcome, scoped repository constraints, checkable acceptance criteria, non-goals, and the required verification.
  • Older Claude models: the fuller template below applies as written.

Phase 6: Output & Iteration

Present the Prompt
markdown
**Generated Meta-Prompt**

[The prompt in the format above]


**Techniques Applied:**
- ✓ Task Decomposition: [Yes/No, how]
- ✓ Fresh Eyes Review: [Yes/No, which experts]
- ✓ Iterative Verification: [Yes/No, where]
- ✓ No Guessing: [Where disclaimers needed]
- ✓ Specialized Experts: [Which ones assigned]

**Would you like me to:**
1. Refine any section
2. Add more verification steps
3. Adjust the complexity level
4. Execute this prompt now

Example: Complete Meta-Prompt Generation

User Request

"Create a prompt to help me write API documentation"

Generated Prompt
markdown
# API Documentation Generator

## Role
You are an Expert Technical Writer specializing in API documentation.
Your documentation will be reviewed by an Expert Developer for accuracy.
When uncertain about behavior, note "Verify with implementation" rather than guessing.

## Context
Creating comprehensive API documentation for developers.
Documentation should be usable without reading source code.

## Instructions

### Phase 1: API Discovery
1. List all endpoints with methods (GET, POST, etc.)
2. Document request/response schemas
3. Identify authentication requirements
4. **Verification:** Cross-reference with OpenAPI spec if available

### Phase 2: Documentation Writing
1. Write endpoint descriptions (what it does, not how)
2. Create request examples with all parameters
3. Create response examples for success and error cases
4. Document rate limits and constraints
5. **Verification:** Each example should be valid JSON/code

### Phase 3: Review Cycle
Expert Developer reviews for:
- Technical accuracy of examples
- Missing edge cases
- Unclear descriptions

### Expert Assignments
- **Expert Technical Writer:** Creates documentation prose
- **Expert Developer:** Validates examples and accuracy

## Constraints
- Use consistent terminology throughout
- Examples must be syntactically valid
- Note any undocumented or unclear behaviors
- Accuracy: Mark assumptions with "Assumed behavior - verify"

## Output Format
```markdown
# [Endpoint Name]

**Method:** [HTTP method]
**Path:** [/api/path]
**Auth:** [Required/Optional/None]

## Description
[What this endpoint does]

## Request
[Parameters, body schema, headers]

## Response
[Success and error responses with examples]

## Notes
[Rate limits, deprecation, related endpoints]

Verification Checklist

  • All endpoints documented
  • All examples are valid
  • Authentication clearly specified
  • Error responses included
  • Assumptions marked for verification


## Error Handling

### Unclear Requirements

```markdown
I need a bit more clarity to create an effective prompt.

**Specifically:**
[Question about the unclear part]

[Offer 2-3 options if applicable]
Over-Complex Request
markdown
This task has [N] distinct components. I recommend:

1. **Split into multiple prompts** - One per major component
2. **Simplify scope** - Focus on [core element] first
3. **Proceed as-is** - Full complexity, longer prompt

Which approach works best for you?
Can't Apply Techniques

If techniques don't fit the task:

markdown
ℹ️ **Note on Techniques**

This task is straightforward enough that some techniques
don't apply:

- Task Decomposition: Not needed (single step)
- Fresh Eyes: [Explain why/why not]
- Specialized Experts: Not needed (single domain)

The generated prompt focuses on clarity and verification instead.

Integration

With skill-content-pipeline

Generate prompts for content creation based on anatomy guides.

With skill-thought-partner

Transform brainstorming insights into actionable prompts.

With skill-prd

Enhance PRD generation with meta-prompting techniques.

With flow-develop

Generate implementation prompts with built-in verification.

The Bottom Line

Meta-prompt → Decompose → Assign experts → Build verification → Generate
Otherwise → Vague prompts → Hallucination → Unreliable output

Structure breeds reliability. Verification breeds accuracy. Experts breed quality.

© nyldn, 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 1 other file in skills/skill-meta-prompt of nyldn/claude-octopus.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit c812f5e

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 nyldn/claude-octopus, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Skill Meta Prompt 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 Meta Prompt compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Skill Meta Prompt this skillnyldn/claude-octopus4.2k1 repos~3.9kAutomated safety check: PassMIT
MCP Server Builderanthropics/skills180k63 repos~2.3kAutomated safety check: PassApache-2.0
Hook Development for Claude Code Pluginsanthropics/claude-plugins-official38k10 repos~4.1kAutomated safety check: NotesApache-2.0
Using Superpowersfarm-fe/farm5.6k36 repos~1.4kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers297k2 repos~5.1kAutomated safety check: PassMIT
Skill CreatorAzure/azqr79689 repos~8.2kAutomated safety check: PassApache-2.0

Similar skills

  • MCP Server Builder

    anthropics/skills

    Official

    Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.

    180k GitHub starsUsed in 63 repos~2.3k tokens
    Agent WorkflowsAuto-check passed

  • Hook Development for Claude Code Plugins

    anthropics/claude-plugins-official

    Official

    Explains how to write Claude Code plugin hooks, both prompt-based checks and bash commands, for events such as PreToolUse, Stop and SessionStart.

    38k GitHub starsUsed in 10 repos~4.1k tokens
    Agent WorkflowsAuto-check: notes

  • Using Superpowers

    farm-fe/farm

    A skill your agent uses when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions

    5.6k GitHub starsUsed in 36 repos~1.4k tokens
    Agent WorkflowsAuto-check passed

  • Executing Plans Inline

    obra/superpowers

    Has the agent carry out an implementation plan itself, task by task in the current session, keeping a ledger, proving each step with a test and ending with one whole-branch review.

    297k GitHub starsUsed in 2 repos~5.1k tokens
    Agent WorkflowsAuto-check passed

  • Skill Creator

    Azure/azqr

    Official

    Create new skills, modify and improve existing skills, and measure skill performance.

    796 GitHub starsUsed in 89 repos~8.2k tokens
    Agent WorkflowsAuto-check passed

  • Claude Code Agent Development

    anthropics/claude-plugins-official

    Official

    Explains how to write agents for Claude Code plugins: the markdown file with YAML frontmatter, trigger descriptions, model and color settings, and system prompt design.

    38k GitHub starsUsed in 7 repos~2.8k tokens
    Agent WorkflowsAuto-check passed

More from nyldn/claude-octopus

All 62 skills in this repo

  • Octopus Quick

    nyldn/claude-octopus

    Quick execution for ad-hoc tasks without full workflow overhead — use for small, self-contained requests

    4.2k GitHub starsUsed in 1 repo~2.2k tokens
    Auto-check passed

  • Octopus Research

    nyldn/claude-octopus

    Thorough research across multiple sources — use for complex topics needing broad synthesis

    4.2k GitHub starsUsed in 1 repo~1.9k tokens
    Auto-check passed

  • Octopus Security Audit

    nyldn/claude-octopus

    OWASP compliance, vulnerability scanning, and adversarial red team testing — use for security reviews

    4.2k GitHub starsUsed in 1 repo~2.3k tokens
    Auto-check passed

  • Skill Audit

    nyldn/claude-octopus

    Audit codebases for quality, consistency, and broken patterns — use for pre-release or tech debt review

    4.2k GitHub starsUsed in 1 repo~3.2k tokens
    Auto-check passed

  • Skill Content Pipeline

    nyldn/claude-octopus

    Extract patterns and anatomy from URLs — use to reverse-engineer content strategies from live pages

    4.2k GitHub starsUsed in 1 repo~3.9k tokens
    Auto-check passed

  • Skill Context Detection

    nyldn/claude-octopus

    Auto-detect work context (Dev vs Knowledge) — use to tailor workflows based on current task type

    4.2k GitHub starsUsed in 1 repo~2.6k tokens
    Auto-check passed

Categories

Questions about Skill Meta Prompt

What does Skill Meta Prompt do?

Craft better prompts using proven optimization techniques — use when your prompt needs refinement. Skill Meta Prompt is an agent skill from nyldn/claude-octopus.

When should I use Skill Meta Prompt?

Skill Meta Prompt fits situations like: your prompt needs refinement.

How do I install Skill Meta Prompt in Claude Code?

Run `npx skills add nyldn/claude-octopus --skill skill-meta-prompt -a claude-code`. Or copy the skill folder (skills/skill-meta-prompt in nyldn/claude-octopus) into .claude/skills/skill-meta-prompt in your project. Claude Code loads it when a task matches its description.

How do I install Skill Meta Prompt in Codex?

Run `npx skills add nyldn/claude-octopus --skill skill-meta-prompt -a codex`. Or copy the skill folder (skills/skill-meta-prompt in nyldn/claude-octopus) into .agents/skills/skill-meta-prompt in your project. Codex loads it when a task matches its description.

Can I use Skill Meta Prompt 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 nyldn/claude-octopus --skill skill-meta-prompt -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-meta-prompt, .gemini/skills/skill-meta-prompt, .github/skills/skill-meta-prompt and .opencode/skills/skill-meta-prompt in your project.

What does Skill Meta Prompt need to run?

SKILL.md names no scripts, command-line tools or credentials: Skill Meta Prompt is instructions for the agent only. Our summary lists: Python 3.

Does Skill Meta Prompt 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 Skill Meta Prompt 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 Skill Meta Prompt use?

Skill Meta Prompt is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skill Meta Prompt use?

About 3.9k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Skill Meta Prompt?

Skills that share tags, products or a category with Skill Meta Prompt: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 297k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Meta Prompt?

nyldn (a GitHub user) maintains it in nyldn/claude-octopus, which has 4,200 GitHub stars. The repository holds 62 skills in this directory. The repository was last updated on October 11, 2026.

Source: nyldn/claude-octopus on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.