Agent skill

Progressive Loading

by athola in athola/claude-night-market

Implements hub-and-spoke lazy loading to minimize token usage in large skills.

MITAuto-check passedFrontend & Design

Install Progressive Loading

skills CLI
$ npx skills add athola/claude-night-market --skill progressive-loading -a claude-code

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

GitHub CLI
$ gh skill install athola/claude-night-market progressive-loading --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/athola/claude-night-market.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/leyline/skills/progressive-loading .claude/skills/progressive-loading && 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
progressive-loading
GitHub stars
342
Token cost
~2.3k tokens
SKILL.md length
792 words
Files
25
Skills in repo
160
Repo updated
First seen
Licence
MIT

At a glance

Implements hub-and-spoke lazy loading to minimize token usage in large skills.

  • Works in 5 steps: Context Detection: Identify user intent,… → Module Selection: Choose which modules… → Budget Management: Verify MECW… → …
  • Building multi-module skills that need conditional on-demand loading
  • SKILL.md covers Overview, When To Use, When NOT To Use and Quick Start, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Progressive Loading is an agent skill from athola/claude-night-market. Implements hub-and-spoke lazy loading to minimize token usage in large skills. Use when building multi-module skills that need conditional on-demand loading.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 25 other files (for example `README.md`, `modules/advanced-patterns.md` and `modules/api-patterns.md`).

It sits in Frontend & Design, covering Web performance and LLM cost and token optimization. The repository describes itself as: 23 Claude Code plugins: TDD enforcement hooks, git/PR workflows, spec-driven development, code review, project lifecycle, fix-from-error, maintenance automation, context… The licence is MIT.

When your agent uses it

  • Building multi-module skills that need conditional on-demand loading
  • Tasks that involve Web performance
  • Tasks that involve LLM cost and token optimization

Example prompts

  • “Use the progressive-loading skill to implement hub-and-spoke lazy loading to minimize token usage in large skills”
  • “/progressive-loading”

Requirements

  • Python 3

Workflow steps

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

  1. Context Detection: Identify user intent, artifacts, workflow type
  2. Module Selection: Choose which modules to load based on context
  3. Budget Management: Verify MECW compliance before loading
  4. Integration Coordination: Provide integration points with other skills
  5. Exit Criteria: Define completion criteria across all paths

What it can do on your machine

Read from SKILL.md and the folder at commit 9f3eb00. 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, python and yaml).

    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

Progressive Loading loads about 2.3k tokens when it runs. Until then it costs about 44 tokens; SKILL.md has 792 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~44
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

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 athola/claude-night-market at commit 9f3eb00, republished under its MIT licence (© athola). 792 words, ~2,302 tokens.

Download SKILL.mdSave it as .claude/skills/progressive-loading/SKILL.md (or your agent's skills folder). This skill also uses 24 other files; get the full folder from GitHub.
name
progressive-loading
description
Implements hub-and-spoke lazy loading to minimize token usage in large skills. Use when building multi-module skills that need conditional on-demand loading.
alwaysApply
false
category
infrastructure
tags
progressive-disclosure, context-management, modularity, token-optimization, lazy-loading
dependencies
conserve:context-optimization
provides.infrastructure
progressive-loading, context-based-selection, load-on-demand
provides.patterns
hub-and-spoke, conditional-loading, context-awareness
usage_patterns
skill-optimization, context-aware-loading, dynamic-module-selection, token-budget-management
complexity
intermediate
model_hint
standard
estimated_tokens
800

Progressive Loading Patterns

Overview

Progressive loading provides standardized patterns for building skills that load modules dynamically based on context, user intent, and available token budget. This prevents loading unnecessary content while ensuring required functionality is available when needed.

The core principle: Start minimal, expand intelligently, monitor continuously.

When To Use

Use progressive loading when building skills that:

  • Cover multiple distinct workflows or domains
  • Need to manage context window efficiently
  • Have modules that are mutually exclusive based on context
  • Require MECW compliance for long-running sessions
  • Want to optimize for common paths while supporting edge cases

When NOT To Use

  • Project doesn't use the leyline infrastructure patterns
  • Simple scripts without service architecture needs

Quick Start

Basic Hub Pattern
markdown
## Progressive Loading

**Context A**: Load `modules/loading-patterns.md` for scenario A
**Context B**: Load `modules/selection-strategies.md` for scenario B

**Always Available**: Core utilities, exit criteria, integration points

Verification: Run the command with --help flag to verify availability.

Context-Based Selection

Modules are declared in the skill's YAML frontmatter and loaded on demand with a @modules/ directive. The hub reads the detected context, then loads only the spokes that match:

markdown
---
modules:
- modules/git-catchup-patterns.md
- modules/python-testing.md
---

When the intent is a branch or PR catch-up, load
`@modules/git-catchup-patterns.md`. When the artifacts include
Python tests, also load `@modules/python-testing.md`.

To keep loads within the MECW token budget, check whether a module fits before pulling it in. The MECWMonitor in plugins/leyline/src/leyline/mecw.py exposes the methods used here:

python
from leyline.mecw import MECWMonitor

monitor = MECWMonitor()
monitor.track_usage(current_context_tokens)

# module_estimated_tokens comes from the module frontmatter
can_load, reasons = monitor.can_handle_additional(module_estimated_tokens)

Load the next module only when can_load is true. Each module records its cost in its frontmatter estimated_tokens field.

Hub-and-Spoke Architecture

Hub Responsibilities
  1. Context Detection: Identify user intent, artifacts, workflow type
  2. Module Selection: Choose which modules to load based on context
  3. Budget Management: Verify MECW compliance before loading
  4. Integration Coordination: Provide integration points with other skills
  5. Exit Criteria: Define completion criteria across all paths
Spoke Characteristics
  1. Single Responsibility: Each module serves one workflow or domain
  2. Self-Contained: Modules don't depend on other modules
  3. Context-Tagged: Clear indicators of when module applies
  4. Token-Budgeted: Known token cost for selection decisions
  5. Independently Testable: Can be evaluated in isolation

Selection Strategies

See modules/selection-strategies.md for detailed strategies:

  • Intent-based: Load based on detected user goals
  • Artifact-based: Load based on detected files/systems
  • Budget-aware: Load within available token budget
  • Progressive: Load core first, expand as needed
  • Mutually-exclusive: Load one path from multiple options

Loading Patterns

See modules/loading-patterns.md for implementation patterns:

  • Conditional includes: Dynamic module references
  • Lazy loading: Load on first use
  • Tiered disclosure: Core → common → edge cases
  • Context switching: Change loaded modules mid-session
  • Preemptive unloading: Remove unused modules under pressure

Common Use Cases

  • Multi-Domain Skills: imbue:catchup loads git/docs/logs modules by context
  • Context-Heavy Analysis: Load relevant modules only, defer deep-dives, unload completed
  • Plugin Infrastructure: Mix-and-match infrastructure modules with version checks

Best Practices

  1. Design Hub First: Define all possible contexts and module boundaries
  2. Tag Modules Clearly: Use YAML frontmatter to indicate context triggers
  3. Measure Token Cost: Know the cost of each module for selection
  4. Monitor Loading: Track which modules are actually used
  5. Validate Paths: Verify all context paths have required modules
  6. Document Triggers: Make context detection logic transparent

Module References

Show full SKILL.md (325 more words)Show less
Core (always available to the hub)
  • Selection Strategies: See modules/selection-strategies.md for choosing modules
  • Loading Patterns: See modules/loading-patterns.md for implementation techniques
  • Performance Budgeting: See modules/performance-budgeting.md for token budget model and optimization workflow
  • Advanced Patterns: See modules/advanced-patterns.md for nested hubs, multi-tier disclosure, and cross-skill module sharing
  • Troubleshooting: See modules/troubleshooting.md when modules fail to load, context detection misfires, or token budgets are exceeded
Context-Specific Pattern Modules

These modules are loaded on demand by the hub based on detected artifacts and user intent. They are listed in frontmatter so the selector can match them, but the hub should only load the ones whose activation context fires.

Operating-system patterns (load on detected platform):

  • modules/linux-patterns.md: Linux-specific shell, paths, and process patterns
  • modules/macos-patterns.md: macOS-specific tooling and platform quirks
  • modules/windows-patterns.md: Windows shell, path, and PowerShell patterns

Language and runtime patterns (load on detected ecosystem):

  • modules/modern-python.md: Python 3.11+ idioms, typing, async
  • modules/legacy-python.md: Python 2 / pre-3.8 compatibility patterns
  • modules/python-packaging.md: pyproject.toml, uv, pip, hatch, poetry
  • modules/python-patterns.md: General Python authoring patterns
  • modules/python-testing.md: pytest, fixtures, parametrization, mocking
  • modules/cargo-patterns.md: Rust Cargo workspace and dependency patterns
  • modules/rust-review.md: Rust code-review patterns

Workflow patterns (load on detected task):

  • modules/api-patterns.md: API design and endpoint conventions
  • modules/api-review.md: API surface review patterns
  • modules/git-patterns.md: Git workflow and history patterns
  • modules/git-catchup-patterns.md: Catching up on a branch or PR diff
  • modules/document-analysis-patterns.md: Reading and analyzing documents
  • modules/log-analysis-patterns.md: Parsing and reasoning over logs
  • modules/performance.md: Performance investigation patterns

Reference material (load only when explicitly cited):

  • modules/large-reference.md: Large reference tables and lookups (load last; tokens are non-trivial)

Integration with Other Skills

This skill provides foundational patterns referenced by:

  • abstract:modular-skills - Uses progressive loading for skill design
  • conserve:context-optimization - Uses for MECW-compliant loading
  • imbue:catchup - Uses for context-based module selection
  • Plugin authors building multi-workflow skills

Reference in your skill's frontmatter:

yaml
dependencies: [leyline:progressive-loading, conserve:context-optimization]
progressive_loading: true

Verification: Run the command with --help flag to verify availability.

Exit Criteria

  • Hub clearly defines all module loading contexts
  • Each module is tagged with activation context
  • Module selection respects MECW constraints
  • Token costs measured for all modules
  • Context detection logic documented
  • Loading paths validated for completeness

© athola, 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 24 other files in plugins/leyline/skills/progressive-loading of athola/claude-night-market.

  • SKILL.md
  • README.md
  • modules/advanced-patterns.md
  • modules/api-patterns.md
  • modules/api-review.md
  • modules/cargo-patterns.md
  • modules/document-analysis-patterns.md
  • modules/git-catchup-patterns.md
  • modules/git-patterns.md
  • modules/large-reference.md
  • modules/legacy-python.md
  • modules/linux-patterns.md
  • modules/loading-patterns.md
  • modules/log-analysis-patterns.md
  • modules/macos-patterns.md
  • modules/modern-python.md
  • modules/performance-budgeting.md
  • modules/performance.md
  • modules/python-packaging.md
  • modules/python-patterns.md
  • … and 5 more

Open the folder on GitHubat commit 9f3eb00

Compare with similar skills

Progressive Loading 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.

Progressive Loading compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Progressive Loading this skillathola/claude-night-market342—~2.3kAutomated safety check: PassMIT
React Doctormakeplane/plane60k12 repos~657Automated safety check: PassAGPL-3.0
Fixing Motion Performanceibelick/ui-skills9.4k5 repos~1.4kAutomated safety check: PassMIT
GSAP Performance Tuninggreensock/gsap-skills16k4 repos~1kAutomated safety check: PassMIT
React Frontend Development Guidelinesdiet103/claude-code-infrastructure-showcase10k2 repos~2.9kAutomated safety check: PassMIT
Web Quality Auditaddyosmani/web-quality-skills2.9k—~2.6kAutomated safety check: PassMIT

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Questions about Progressive Loading

What does Progressive Loading do?

Implements hub-and-spoke lazy loading to minimize token usage in large skills. Progressive Loading is an agent skill from athola/claude-night-market. Implements hub-and-spoke lazy loading to minimize token usage in large skills.

When should I use Progressive Loading?

Progressive Loading fits situations like: building multi-module skills that need conditional on-demand loading; tasks that involve Web performance; tasks that involve LLM cost and token optimization.

How do I install Progressive Loading in Claude Code?

Run `npx skills add athola/claude-night-market --skill progressive-loading -a claude-code`. Or copy the skill folder (plugins/leyline/skills/progressive-loading in athola/claude-night-market) into .claude/skills/progressive-loading in your project. Claude Code loads it when a task matches its description.

How do I install Progressive Loading in Codex?

Run `npx skills add athola/claude-night-market --skill progressive-loading -a codex`. Or copy the skill folder (plugins/leyline/skills/progressive-loading in athola/claude-night-market) into .agents/skills/progressive-loading in your project. Codex loads it when a task matches its description.

Can I use Progressive Loading 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 athola/claude-night-market --skill progressive-loading -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/progressive-loading, .gemini/skills/progressive-loading, .github/skills/progressive-loading and .opencode/skills/progressive-loading in your project.

What does Progressive Loading need to run?

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

Does Progressive Loading 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 Progressive Loading 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 Progressive Loading use?

Progressive Loading 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 Progressive Loading use?

About 2.3k tokens (SKILL.md is roughly 9.2k 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 Progressive Loading?

Skills that share tags, products or a category with Progressive Loading: React Doctor (makeplane/plane, 60k stars), Fixing Motion Performance (ibelick/ui-skills, 9.4k stars), GSAP Performance Tuning (greensock/gsap-skills, 16k stars) and React Frontend Development Guidelines (diet103/claude-code-infrastructure-showcase, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Progressive Loading?

athola (a GitHub user) maintains it in athola/claude-night-market, which has 342 GitHub stars. The repository holds 160 skills in this directory. The repository was last updated on October 6, 2026.

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