Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/dandanllab/legadoskill/copilot-instructionsgit clone --depth 1 https://github.com/DandanLLab/legadoSkillWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00795 | $0.00795 |
| Opus 5 | $0.00398 | $0.00398 |
| Sonnet 5 | $0.00159 | $0.00159 |
| Haiku 4.5 | $0.00080 | $0.00080 |
Grade A, and why
legadoSkill copilot-instructions.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Legado书源驯兽师 - AI Coding Agent Instructions
Project Overview
This is an AI-driven tool for automatically generating book sources for the Legado reading Android app. The system analyzes website HTML structures and creates compliant JSON configurations using a knowledge-driven approach.
Architecture
- Skill Package:
SKILL.md- Core AI agent logic with 23 specialized tools - Debug Engine:
debugger/- Python-based testing framework simulating Legado's Kotlin parser - Knowledge Base:
assets/- 24.93MB of documentation, 1751 real book source examples - Legado Source:
legado/- Official app source code for compatibility validation
Critical Workflows
Testing Book Sources
# Test a book source with search keyword
python debugger/test_universal.py path/to/book_source.json -k "斗破苍穹"
# Enable auto-fix with max attempts
python debugger/test_universal.py path/to/book_source.json -k "keyword" --auto-fix
Environment Setup
# Install dependencies
pip install -r debugger/requirements.txt
# Run in Trae IDE with MCP protocol support
# Skill package auto-loads from .trae/skills/
Key Patterns & Conventions
Knowledge-First Approach
Always query knowledge base before rule generation:
search_knowledge()for CSS selectors, POST configs, regex patternsget_css_selector_rules()for complete selector syntaxget_real_book_source_examples()for proven templates
Rule Validation
- Strict Legado JSON schema compliance
- No forbidden fields:
textNodes,ownText,allElements - Regex patterns use
##delimiter for replacement nextContentUrlrequires pagination detection
5-Stage Development Workflow
- Collect Info: Query knowledge, detect encoding, fetch HTML
- Review Rules: Write rules, validate syntax, handle edge cases
- Create Source: Generate complete JSON, debug test
- Output JSON: Save to
output/book_sources/ - Self-Evolve: Learn from successful patterns
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 86 lines · 795 tokens per session scan A df9af0c79231
legadoSkill copilot-instructions.md is an instructions file published in the GitHub repository DandanLLab/legadoSkill (203 stars, last pushed 5mo ago), licensed MIT. It adds 795 tokens to every session, about $0.0040 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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