Agent skill

Ariadne Thread

by LeoYeAI in LeoYeAI/openclaw-master-skills

Create AI-friendly project structures using progressive disclosure indexing (AGENTS.md, module INDEX.md files, file intent headers).

MITAuto-check passedAgent Workflows

Install Ariadne Thread

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill ariadne-thread -a claude-code

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

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills ariadne-thread --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/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ariadne-thread .claude/skills/ariadne-thread && 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
ariadne-thread
GitHub stars
2.2k
Token cost
~6.1k tokens
SKILL.md length
2,246 words
Files
7 (incl. references)
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Create AI-friendly project structures using progressive disclosure indexing (AGENTS.md, module INDEX.md files, file intent headers).

  • Works in 2 steps: Navigation efficiency — minimize tokens… → Modification safety — maximize…
  • The user wants to: create
  • SKILL.md covers When to Use, Core Philosophy, The 4-Level Index Architecture and Module Design Principles, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ariadne Thread is an agent skill from LeoYeAI/openclaw-master-skills. Create AI-friendly project structures using progressive disclosure indexing (AGENTS.md, module INDEX.md files, file intent headers). ALWAYS use this skill when the user wants to: create or update AGENTS.md or llms.txt, add INDEX.md files to modules, set up project navigation for AI agents, retrofit an existing codebase with AI-friendly indexes, design module boundaries with explicit dependency and modification-risk documentation, or mentions "AI-friendly project", "progressive disclosure", "project index"…

Its SKILL.md is about 6.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `_meta.json`, `references/doc-standards.md` and `references/index-maintenance.md`).

It sits in Agent Workflows, covering AI search optimization and Agent instruction files. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • The user wants to: create
  • Update AGENTS.md
  • Add INDEX.md files to modules
  • Set up project navigation for AI agents

Example prompts

  • “AI-friendly project”
  • “progressive disclosure”
  • “project index”
  • “/ariadne-thread”

Workflow steps

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

  1. Navigation efficiency — minimize tokens consumed to find the right code
  2. Modification safety — maximize probability of correct changes via explicit contracts and dependency visibility

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. 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 cpp, markdown and typescript).

    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

Ariadne Thread loads about 6.1k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 176 tokens; SKILL.md has 2,246 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~176
When it runs · the whole SKILL.md, loaded when a task matches
~6.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~19k

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 LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 2,246 words, ~6,117 tokens.

Download SKILL.mdSave it as .claude/skills/ariadne-thread/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
ariadne-thread
description
Create AI-friendly project structures using progressive disclosure indexing (AGENTS.md, module INDEX.md files, file intent headers). ALWAYS use this skill when the user wants to: create or update AGENTS.md or llms.txt, add INDEX.md files to modules, set up project navigation for AI agents, retrofit an existing codebase with AI-friendly indexes, design module boundaries with explicit dependency and modification-risk documentation, or mentions "AI-friendly project", "progressive disclosure", "project index", "codebase navigation", or "ariadne". Also use when the user asks how to structure a new project so AI tools can navigate it efficiently, even if they don't use these exact terms.

Ariadne Thread

Create codebases that AI agents can navigate efficiently through progressive disclosure — revealing only what's needed, when it's needed.

When to Use

Apply this skill when the user:

  • Starts a new project and wants AI-friendly structure
  • Restructures or indexes an existing codebase
  • Adds project navigation (AGENTS.md, INDEX.md, llms.txt)
  • Designs module boundaries and dependencies
  • Mentions: "AI-friendly project", "progressive disclosure", "project index", "codebase navigation", or "ariadne"

Language-agnostic: applies to any language. See language-adaptation.md for per-language mappings.

When NOT to apply: Minimal single-file projects, quick prototypes, or codebases where indexing overhead outweighs benefit. Skip full L1/L2 when the project has fewer than ~5 source files; a lightweight AGENTS.md may suffice.

Core Philosophy

Like Ariadne's thread through the labyrinth, a well-indexed codebase gives AI agents a clear path from project overview to implementation detail. An agent should understand your project structure, locate any function, and assess the impact of any modification — without reading the entire codebase.

Two optimization targets:

  1. Navigation efficiency — minimize tokens consumed to find the right code
  2. Modification safety — maximize probability of correct changes via explicit contracts and dependency visibility

The 4-Level Index Architecture

L0  PROJECT ROOT     ← AGENTS.md (80–150 lines, prefer brevity)
│                       Project map, navigation table, build commands, cross-cutting patterns
│
L1  MODULE INDEX     ← Each directory gets an INDEX.md (~50 lines)
│                       Purpose, public API, bidirectional dependencies, contracts,
│                       task routing, modification risk, test mapping
│
L2  FILE INTENT      ← File headers (~5-10 lines per file)
│                       What this file does, public interface, contracts, side effects
│
L3  INLINE DETAIL    ← Comments on non-obvious logic only
                        Why, not what; tricky edge cases; business rules

AI agents start at L0 and drill down only as needed.

L0: Project Root Index

Create an AGENTS.md at the project root with:

  1. One-line project description
  2. Directory map — ASCII tree with purpose annotations
  3. Quick navigation table — key entry points by area
  4. Build & test commands
  5. Code conventions summary
  6. Architectural constraints — illegal dependency directions, global invariants
  7. Cross-cutting behavior patterns — error handling, logging, concurrency model
  8. Agent Workflow — at the end of the file (Checklist-at-END format): mandatory steps before editing — locate module via Quick Navigation, open INDEX.md and check Dependents before modifying public API, confirm Modification Risk for breaking changes, run Tests before finishing
  9. Index exclusions — Optional .cursorignore (same syntax as .gitignore) to exclude build artifacts, dependencies, generated code; common examples: node_modules/, dist/, vendor/, __pycache__/, target/, *.generated.* — adapt to your ecosystem

The cross-cutting patterns section is critical: it prevents the AI from reading 3-4 existing files just to infer "how do we handle errors here?". One 30-line declaration replaces ~1500 tokens of pattern inference.

All 9 sections are required — they are the difference between an AGENTS.md that actually guides AI behavior and one that merely documents the project for humans. The sections that get skipped most often (Architectural Constraints, Cross-Cutting Patterns, Agent Workflow checklist) are also the ones with the highest AI value.

Navigation priority: Prefer navigating via INDEX.md Dependents and Dependencies over semantic/keyword search. Structural dependency paths surface architecturally critical files that retrieval often misses. When modifying a module's public API, always consult its Dependents list first to assess blast radius.

Information placement (Lost in the Middle): LLMs attend more to context start and end. Place high-priority content (Build, Quick Navigation, Agent Workflow) at the beginning or end of AGENTS.md; keep secondary sections toward the middle.

markdown
## Architectural Constraints

<!-- Typical Web app layers; adapt to project: app/, packages/, layers/, etc. -->
- Dependency direction: presentation → domain → infrastructure (never reverse)
  Example: ui/ → core/ → infra/ — or app/features/, packages/core/, etc.
- All external calls (HTTP, DB, cache) go through infrastructure layer — e.g. `src/infra/` or `[project-path]/infra/`
- No direct file I/O outside designated storage module

## Cross-Cutting Patterns

### Error Handling
- Throw typed errors (e.g. [ErrorType], [ValidationError]), never raw strings
- Log errors before re-throwing across module boundaries
- Public functions document all throwable error types in file header

### Logging
- Use structured logger from [project logger location]
- Include [trace_id or request_id] in all log entries
- Levels: ERROR (failures), WARN (degradation), INFO (operations)

### Concurrency (if applicable)
- Document thread-safety per module (single-threaded vs thread-safe)
- Define lock ordering strategy (e.g. by resource name, by hierarchy) and record in AGENTS.md
- Apply consistently to avoid deadlocks

For the full AGENTS.md template, see index-templates.md.

L1: Module Index

Every module directory gets an INDEX.md — any directory that has a public API, a barrel/__init__, or 3+ source files. This is the most information-dense layer — it must answer not just "what does this module do?" but also "who depends on it?", "what will break if I change it?", and "which file should I edit for task X?".

Example below uses domain auth; replace with your domain — billing, reporting, catalog, etc.

markdown
# Module: auth

Handle user authentication and session management.

Status: stable | Fan-in: 5 | Fan-out: 2

## Public API

| Export | File | Description |
|--------|------|-------------|
| `authenticate()` | `authenticate.*` | Verify credentials, return session |
| `authorize()` | `authorize.*` | Check permissions for resource |
| `SessionStore` | `session_store.*` | Session lifecycle management |

## Dependencies (Fan-out: 2)

<!-- Use repo-root-relative paths. Optional edge type: [imports] | [inherits] | [instantiates] -->

- `src/infra/db` — user record lookups
- `src/shared/crypto` — password hashing

## Dependents (Fan-in: 5)

<!-- Prefer structural navigation via this list over semantic search. Use repo-root-relative paths. -->

- `src/api/routes/user` → authenticate()
- `src/api/middleware/auth-guard` → authorize()
- `src/core/billing` → get_session()
- `src/core/notification` → get_user_from_session()
- `src/api/routes/admin` → authorize()

## Interface Contract

- authenticate(): returns Session (token non-empty, expires_at > now) or throws AuthError
- All failed auth attempts → audit log written before exception propagates
- Thread-safe: all public functions can be called concurrently
- Side effect: authenticate() writes to audit_log on failure

## Modification Risk

- Add field to Session → compatible, no consumer changes needed
- Change authenticate() signature → BREAKING, update 5 dependents
- Change error types → BREAKING for all specific catch blocks

## Task Routing

- Add login method → modify authenticate.*, possibly add new file
- Change session TTL → modify session_store.*, update config
- Add permission type → modify authorize.*, update Permission enum
- Fix token validation bug → modify token_validator.*

## Tests

- Unit: `tests/unit/auth/`
- Integration: `tests/integration/auth_flow_test.*`
- Critical: auth_flow_test (must pass before merge)
- Untested: token_refresh() — extra caution when modifying

Voice Coding: L1 Task Routing supports "say task → get file path" — map natural-language tasks to specific files for hands-free navigation.

Common mistake — avoid narrative prose in INDEX.md: The structured sections (Dependents, Modification Risk, Task Routing) are what make an INDEX.md useful for AI navigation. A long prose description of each file ("This file is responsible for X, it does Y, here are some notes...") might look thorough but it fails to answer the core AI questions: who depends on this? what breaks if I change it? which file do I edit? Prefer the structured template even if it feels terse — density and scanability beat completeness.

Why each section matters for AI:

SectionAI Question It AnswersWhat it replaces
Dependents"Who calls me? What breaks if I change?"Full-project grep to discover callers
Interface Contract"What invariants must I preserve?"Re-reading caller files to infer constraints
Modification Risk"Is this change safe? How many files to update?"Underestimating blast radius and missing dependents
Task Routing"Which file should I edit for task X?"Reading wrong files before finding the right one
Tests"What should I run to verify?"Searching for test files across the project

The actual token savings depend heavily on project size and how the AI handles uncertainty — they scale with the number of modules and files. The value is real but varies; don't treat any specific number as a guarantee.

For the full INDEX.md template, see index-templates.md.

L2: File-Level Intent + Contract

Every source file starts with an intent header. For public API files (called by other modules), extend the header with contract annotations:

C++ (public header with contract):

cpp
/**
 * @brief Verify user credentials against stored records.
 *
 * @pre password is non-empty, username has passed format validation
 * @post On success: returned Session.token is non-empty and unique
 * @post On failure: audit log entry written before exception
 * @throws AuthError if credentials are invalid
 * @throws RateLimitError if attempts exceed MAX_ATTEMPTS
 * @sideeffect Writes to audit_log table on failure
 * @threadsafe
 *
 * @module auth
 * @public authenticate
 */

TypeScript (public API with contract):

typescript
/**
 * Verify user credentials against stored records.
 *
 * @pre password is non-empty, username passed format validation
 * @post Success: Session.token is non-empty and unique
 * @post Failure: audit log entry written before throwing
 * @throws {AuthError} credentials invalid
 * @throws {RateLimitError} attempts exceed MAX_ATTEMPTS
 * @sideeffect Writes audit_log on failure
 *
 * @module auth
 * @public authenticate
 */

Internal files (not called across module boundaries) keep the simple 3-5 line header. Only public API files need full contracts.

Contract annotation reference:

AnnotationPurposeWhen to Use
@preWhat must be true before callingInput validation assumptions
@postWhat is guaranteed after returnReturn value constraints
@throwsWhat errors can be thrownAll error conditions
@sideeffectWhat state changes beyond return valueDB writes, events, cache invalidation
@threadsafeConcurrency guaranteeMulti-threaded systems
@performanceLatency/throughput constraintsHot-path functions
@lines / @rangeKey line ranges for large filesWhen file exceeds ~300 LOC; e.g. @lines 45-89 main logic

Path convention: In L1, use repo-root-relative paths (e.g. src/auth/authenticate.ts) — avoid ./, ../ for clarity and Voice Coding compatibility.

See index-templates.md for all language templates.

L3: Inline Annotations

Comment ONLY non-obvious logic:

cpp
// Rate limit: 5 attempts per IP per minute (compliance FR-201)
if (attempts > MAX_ATTEMPTS) throw RateLimitError();

// Must resolve parent before children — circular refs in legacy schema
auto resolved = resolve_parent_first(node);

Never comment obvious code. AI reads code natively — comments that restate the code are pure noise.

Module Design Principles

Single Responsibility
  • One module = one clear purpose (stated in INDEX.md)
  • One file = one concept, under 300 lines (or ~500 for verbose languages)
  • If you cannot name it clearly, split it
✅ src/billing/calculate_invoice.*    — does one thing
✅ src/billing/apply_discount.*       — does one thing
❌ src/billing/utils.*                — does many things
❌ src/helpers.*                      — grab bag
Clear Dependency Direction

Typical layering (example; adapt names to your project — e.g. app/, packages/, layers/):

presentation/ → domain/ → infrastructure/
         ↘ shared/ (cross-cutting) ↙
  • Depend inward (presentation → domain → infrastructure), never outward
  • Document the actual direction in AGENTS.md Architectural Constraints
Small, Focused Files
MetricTargetReason
Lines per file< 300 (< 500 for verbose languages)AI reads full files; smaller = less wasted context
Public symbols per file1-5Clear public surface
Dependencies per file< 5-8 imports/includesLimits blast radius
Library vs Application Projects

The index structure works for both, but libraries differ in several key areas:

AspectApplicationLibrary
Build & TestHas build + run commandsMay have no build step (header-only); tests often in separate projects
Public APIInternal modules calling each otherConsumers are external; public API = exported headers / package interface
Dependents (Fan-in)Other modules in the same projectPrimarily external consumers (note "External: N/A — library" in Dependents)
Dependency directionpresentation → domain → infra (example)Libraries: typically flat or layered by abstraction (e.g., core ← filtering ← testing)
File size targetsStrict (you control the code)Guideline only for upstream/third-party code you do not own

Header-only libraries (C++): no include/ + src/ split — all code is in headers. The entry header (e.g., library.hpp) acts as the barrel/__init__. There is no independent build step; document how consumers include and link.

Upstream / third-party code: When indexing a library you do not maintain (e.g., a vendored dependency), create indexes (L0 + L1) for navigation but do NOT modify source files (no L2 header changes, no refactoring oversized files). Flag issues in indexes with ⚠ UPSTREAM instead of ⚠ needs split.

Intent-Oriented Code

Write code that expresses what it accomplishes, not how.

Naming Principles (Universal)
LayerPrincipleC++ ExampleTypeScript ExamplePython Example
Filesdescriptive, one conceptinvoice_calculator.cppcalculate-invoice.tsinvoice_calculator.py
Dirsgroup by domainbilling/, auth/billing/, auth/billing/, auth/
Functionsverb-first, outcomecalculate_total()calculateTotal()calculate_total()
ClassesPascalCase nounInvoiceCalculatorInvoiceCalculatorInvoiceCalculator
ConstantsSCREAMING_SNAKEMAX_RETRY_COUNTMAX_RETRY_COUNTMAX_RETRY_COUNT
Booleansis/has/shouldis_authenticatedisAuthenticatedis_authenticated
Function Signatures — Express Intent
cpp
// ✅ Intent-oriented: tells you WHAT
MonetaryAmount calculate_invoice_total(
    const std::vector<LineItem>& line_items,
    const std::vector<Discount>& discounts);

// ❌ Implementation-oriented: tells you HOW
int process_items(void* items, int count, int flags);
API Design

Applies to any external interface (REST, gRPC, library headers, CLI):

  • Name by resource/concept, not implementation
  • Consistent input/output contracts
  • Version your API (URL path, header, or namespace)
  • Document error conditions explicitly

For detailed conventions, see naming-api-conventions.md.

Show full SKILL.md (887 more words)Show less

Documentation Tiers

Not all documentation serves the same audience or requires the same update frequency. This skill distinguishes two tiers:

TierFilesAudienceUpdate TimingStaleness Impact
A — AI Runtime IndexAGENTS.md, INDEX.md, file headers (L2), inline comments (L3)AI agentsReal-time — atomic with code changesCritical: stale → wrong navigation, missed dependencies, broken contracts
B — Human CommunicationREADME.md, architecture.md, ADRs, API docs, guides, changelogsHuman developersBatch — at iteration endModerate: stale → human confusion, but AI unaffected

Key insight: Tier A indexes are the AI's "working memory" — they must be updated in the same operation as the code change. Tier B documents are "reports" derived from Tier A — they can be regenerated at iteration boundaries.

During active development: AI agents only maintain Tier A files. Tier B documents are transparent to the AI — they are neither read nor written until the iteration-end sync.

Token economics: This separation reduces per-change AI overhead by ~60%, while actually improving Tier B document quality (batch updates are more coherent than incremental patches).

Project Documentation (Tier B)

Human-facing documentation follows the same progressive disclosure structure, but is maintained at iteration boundaries rather than in real-time. Common structure (adapt to project; ADR is optional — omit adr/ if you don't use ADRs):

docs/
├── README.md              # What is this, how to start (< 1 page)
├── architecture.md        # System overview, component diagram
├── adr/                   # Architecture Decision Records (optional)
│   ├── INDEX.md           # ADR list with one-line summaries
│   └── 001-database.md
└── api/                   # API reference (or equivalent)
    ├── INDEX.md           # Endpoint / header summary table
    └── users.md           # Detailed docs

Rules:

  1. One doc = one purpose
  2. INDEX.md in every doc directory
  3. Link, don't inline — reference Tier A indexes (INDEX.md) as source of truth
  4. Max 500 lines per doc
  5. Updated at iteration end — not on every code change

For doc templates and ADR format, see doc-standards.md.

Language Adaptation

The 4-level index and design principles are universal. Per-language conventions (C++, TypeScript, Python, Go, Rust, Java/Kotlin) — directory layout, file naming, L2 contract annotations, build commands — are in language-adaptation.md. Load that file when indexing a project in a specific language.

Workflow: New Project

  1. Identify language ecosystem — see language-adaptation.md
  2. Design the directory map before writing code
  3. Create AGENTS.md — project table of contents + cross-cutting patterns + AI Agent Instructions
  4. Create module INDEX.md as you create each directory (include all sections)
  5. Add file intent headers — simple for internal files, full contract for public API
  6. Follow naming conventions from day one
  7. Defer Tier B docs — create docs/ directory structure but fill content only at first iteration end

Workflow: Retrofit Existing Project

Principle: Document first, refactor later. Create indexes for the codebase AS-IS. Flag issues (oversized files, violations) in the indexes but do not block on fixing them.

Monorepo / sub-project: If the target is a sub-directory within a larger repo, place AGENTS.md at the sub-project root (e.g., lib/AGENTS.md). Build commands should cover this sub-project only (or note the parent command). See the monorepo variant in templates.

Priority: Start with the highest Fan-in module (most depended-on) — it yields the most navigation savings.

Partial migration: If migrating a single module (not the whole project), start at L1 — create INDEX.md for the target module directly. Skip L0; assume the parent AGENTS.md will be created later or already exists.

Library projects: If the target is a library (not an application), see Library vs Application Projects for differences in Build & Test, Public API, and Dependents. For header-only C++ libraries, the entry header acts as the barrel — document it in AGENTS.md and treat the main header directory as a single module.

Upstream / third-party code: If you do not own the source code (vendored dependency, upstream library), create L0 + L1 indexes for navigation but skip L2 (do not modify source file headers) and L3 (do not modify inline comments). Flag oversized files with ⚠ UPSTREAM instead of suggesting splits.

  1. Create L0: Write AGENTS.md with directory map, navigation table, cross-cutting patterns, and AI Agent Instructions. Mark any discovered architectural violations as ⚠ KNOWN VIOLATION in Architectural Constraints
  2. Add L1: Create INDEX.md in each major module — start with Public API + Dependencies + Dependents. Use grep -rn "from [module]" . or IDE search to discover Dependents. For flat directories with many files, use section headers within INDEX.md to organize logical groups (see L1 Retrofit Tips)
  3. Enrich L1: Add Interface Contract, Task Routing, Modification Risk, Tests. In Files table, flag oversized files (e.g., ⚠ 1066 lines, needs split or ⚠ UPSTREAM for code you do not own). Also flag files with forbidden names — utils.*, helpers.*, types.*, common.* — with ⚠ needs split/rename and add a rename task to Task Routing. These names indicate unclear responsibility and should be resolved over time
  4. Verify L2: Ensure public API files have contract headers; internal files have intent headers (skip for upstream code)
  5. Audit L3: Remove obvious comments, add explanations for tricky logic (skip for upstream code)
  6. Validate per module: After each module's INDEX.md, test: ask an AI to locate a function and assess change impact. If it reads > 5 files or misses a dependent, improve that index before moving on
  7. Sync Tier B: Once all Tier A indexes are complete, run an iteration-end sync to generate/update all human-facing docs from Tier A content

Index Maintenance

Directive: Every code change MUST include atomic updates to relevant Tier A indexes. See index-maintenance.md for the full trigger→action table, anti-patterns, and Tier B sync workflow. When the user says "documentation sync" or "iteration end", execute the Tier B batch process.

Detailed References

© LeoYeAI, 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 (references) in skills/ariadne-thread of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json
  • references/doc-standards.md
  • references/index-maintenance.md
  • references/index-templates.md
  • references/language-adaptation.md
  • references/naming-api-conventions.md

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Ariadne Thread 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.

Ariadne Thread compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ariadne Thread this skillLeoYeAI/openclaw-master-skills2.2k—~6.1kAutomated safety check: PassMIT
Johnny Suede WriteJasonColapietro/suede-creator-skills127—~7.8kAutomated safety check: PassMIT
Sync Public DocsCaldis/react-zmage946—~3.3kAutomated safety check: PassMIT
Ralph Docsmikeyobrien/ralph-orchestrator3.2k—~1.5kAutomated safety check: PassMIT
Docs Seekereinverne/dotfiles121—~1.4kAutomated safety check: PassGPL-3.0
Geo PipelineViryaZheng/recomby-geo459—~1.1kAutomated safety check: PassMIT

Similar skills

  • Johnny Suede Write

    JasonColapietro/suede-creator-skills

    Suede Labs full writing stack: sharper copy for docs, pages, email, social, headlines, CTAs, product listings, and public explainers, with an SEO/AEO/AI EO pass, persona and framework selection…

    127 GitHub stars~7.8k tokensUpdated today
    Marketing & SEOAuto-check passed
  • Sync Public Docs

    Caldis/react-zmage

    A skill your agent uses when modifying public API in packages/core (types/global.ts, types/default.ts, index.ts, or package.json exports field), adding/renaming/removing props, changing default…

    946 GitHub stars~3.3k tokensUpdated 4 mo ago
    Agent WorkflowsAuto-check passed
  • Ralph Docs

    mikeyobrien/ralph-orchestrator

    Introspect, explain, and improve Ralph Orchestrator using its published llms.txt doc map.

    3.2k GitHub stars~1.5k tokensUpdated 4 days ago
    Marketing & SEOAuto-check passed
  • Docs Seeker

    einverne/dotfiles

    Searching internet for technical documentation using llms.txt standard, GitHub repositories via Repomix, and parallel exploration.

    121 GitHub stars~1.4k tokensUpdated 1 mo ago
    Marketing & SEOAuto-check passed
  • Geo Pipeline

    ViryaZheng/recomby-geo

    Entry point + orchestrator for the recomby-geo GEO (Generative Engine Optimization) workflow on OpenAI Codex CLI.

    459 GitHub stars~1.1k tokensUpdated 3 mo ago
    Marketing & SEOAuto-check passed
  • Content Decay Scan

    indranilbanerjee/digital-marketing-pro

    Scan the content library for decay — declining traffic, falling keyword positions, stale content, broken links, lost AI citations — scoring each URL 0-100 via creative-fatigue-predictor.py and…

    859 GitHub starsUsed in 1 repo~3k tokens
    Writing & ContentAuto-check passed

More from LeoYeAI/openclaw-master-skills

All 1,235 skills in this repo
  • DevOps Pipeline Management

    LeoYeAI/openclaw-master-skills

    Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.

    2.2k GitHub stars~4.2k tokensUpdated 2 mo ago
    Auto-check: notes
  • Feishu Document Collaboration

    LeoYeAI/openclaw-master-skills

    Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.

    2.2k GitHub stars~2k tokensUpdated 2 mo ago
    Auto-check passed
  • Files Memory System

    LeoYeAI/openclaw-master-skills

    Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.

    2.2k GitHub stars~3.8k tokensUpdated 2 mo ago
    Auto-check passed
  • GEO-Claw AI Visibility Agent

    LeoYeAI/openclaw-master-skills

    Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.

    2.2k GitHub stars~4.7k tokensUpdated 2 mo ago
    Auto-check passed
  • Google Workspace CLI

    LeoYeAI/openclaw-master-skills

    Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.

    2.2k GitHub stars~2.6k tokensUpdated 2 mo ago
    Auto-check: notes
  • HealthFit Health Advisors

    LeoYeAI/openclaw-master-skills

    Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.

    2.2k GitHub stars~4.4k tokensUpdated 2 mo ago
    Auto-check passed

Questions about Ariadne Thread

What does Ariadne Thread do?

Create AI-friendly project structures using progressive disclosure indexing (AGENTS.md, module INDEX.md files, file intent headers). Ariadne Thread is an agent skill from LeoYeAI/openclaw-master-skills.md files, file intent headers).

When should I use Ariadne Thread?

Ariadne Thread fits situations like: the user wants to: create; update AGENTS.md; add INDEX.md files to modules; set up project navigation for AI agents.

How do I install Ariadne Thread in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill ariadne-thread -a claude-code`. Or copy the skill folder (skills/ariadne-thread in LeoYeAI/openclaw-master-skills) into .claude/skills/ariadne-thread in your project. Claude Code loads it when a task matches its description.

How do I install Ariadne Thread in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill ariadne-thread -a codex`. Or copy the skill folder (skills/ariadne-thread in LeoYeAI/openclaw-master-skills) into .agents/skills/ariadne-thread in your project. Codex loads it when a task matches its description.

Can I use Ariadne Thread 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 LeoYeAI/openclaw-master-skills --skill ariadne-thread -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ariadne-thread, .gemini/skills/ariadne-thread, .github/skills/ariadne-thread and .opencode/skills/ariadne-thread in your project.

What does Ariadne Thread need to run?

SKILL.md names no scripts, command-line tools or credentials: Ariadne Thread is instructions for the agent only.

Does Ariadne Thread 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 Ariadne Thread 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 Ariadne Thread use?

Ariadne Thread 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 Ariadne Thread use?

About 6.1k tokens (SKILL.md is roughly 24k 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 13k tokens, read only when the agent opens those files.

What are the alternatives to Ariadne Thread?

Skills that share tags, products or a category with Ariadne Thread: Johnny Suede Write (JasonColapietro/suede-creator-skills, 127 stars), Sync Public Docs (Caldis/react-zmage, 946 stars), Ralph Docs (mikeyobrien/ralph-orchestrator, 3.2k stars) and Docs Seeker (einverne/dotfiles, 121 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ariadne Thread?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,160 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.