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/lap-platform/lap/claude-mdgit clone --depth 1 https://github.com/Lap-Platform/LAPWhat 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 | $0.00971 | $0.00971 |
| Opus 5 | $0.00485 | $0.00485 |
| Sonnet 5 | $0.00194 | $0.00194 |
| Haiku 4.5 | $0.00097 | $0.00097 |
Grade A, and why
LAP CLAUDE.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.
LAP - Lean API Platform
What This Is
LAP compiles API specs (OpenAPI, GraphQL, AsyncAPI, Protobuf, Postman) into LAP -- a token-efficient format for AI agents. Median 2.8x compression across 162 real-world specs.
Quick Reference
# Install
pip install -e ".[dev]"
# Run tests
python -m pytest tests/ -q
# CLI entry point
lapsh compile examples/verbose/openapi/petstore.yaml -o petstore.lap
Project Structure
lap/-- Top-level Python package (installed aslapsh)lap/core/compilers/-- Format-specific compilers (openapi, graphql, asyncapi, protobuf, postman, lap)lap/core/formats/-- Data models (LAP v0.2, LAP v0.1)lap/core/parser.py-- LAP text to Python objects (proves losslessness)lap/core/converter.py-- LAP to OpenAPI roundtriplap/core/differ.py-- Semantic API diff enginelap/core/utils.py-- Shared utilities (token counting, file reading)lap/cli/main.py-- CLI with 15+ subcommands
integrations/-- LangChain, CrewAI, OpenAI, MCP bridgessdks/python/-- Python SDK (thin wrapper around lap.core)sdks/typescript/-- TypeScript/npm SDK (@lap-platform/lapsh)tests/-- pytest suite (11 test files)benchmarks/-- Skill token benchmarksassets/-- Chart PNGs for docsexamples/verbose/-- 162 real-world API specs (36MB corpus), organized by formatexamples/lap/-- Pre-compiled LAP output, organized by format
Architecture
API Spec (YAML/JSON/SDL/proto)
-> Format compiler (lap/core/compilers/*.py)
-> LAP data model (lap/core/formats/*.py)
-> .to_lap(lean=True/False) text output
-> Parser (lap/core/parser.py) for roundtrip validation
-> Converter/Differ for analysis
Tech Stack
- Python 3.10+
- Dependencies: pyyaml, tiktoken, rich (dev)
- Tests: pytest
- Package: setuptools (pyproject.toml)
- License: Apache 2.0
Key Conventions
- All imports use the
lappackage namespace (e.g.,from lap.core.formats.lap import LAPSpec) - Framework integrations handle ImportError gracefully -- no hard deps on LangChain/CrewAI/etc.
- Token counting uses tiktoken with
gpt-4omodel, falls back tolen(text)//4 - CLI entry point:
lap.cli.main:main - Tests run from project root:
python -m pytest tests/ -q - All compilers follow the same pattern: take spec input, return format-specific data model, call
.to_lap() - All file reads use
encoding='utf-8'(Windows cp1255 fix)
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 · 971 tokens per session scan A 9c2448b9440e
LAP CLAUDE.md is an instructions file published in the GitHub repository Lap-Platform/LAP (346 stars, last pushed 16d ago), licensed Apache-2.0. It adds 971 tokens to every session, about $0.0049 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.
Other instructions, from other repositories
api-gateway CLAUDE.md
Instructions for membrane/api-gateway, covering claude.md, working principles, 1. think before coding, 2. simplicity first and 3. surgical changes.
scalar AGENTS.md
Instructions for scalar/scalar, covering agents.md - ai agent guide for scalar, project overview, prerequisites, first-time setup and commands.
data-api-builder copilot-instructions.md
Copilot instructions for Azure/data-api-builder, covering data api builder (dab) - copilot instructions, project overview, key technologies, project structure and building and testing.
grafbase CLAUDE.md
Instructions for grafbase/grafbase, a project described as:
uxc AGENTS.md
Instructions for holon-run/uxc, covering repository guidelines, project structure & module organization, build, test, and development commands, coding style & naming conventions and documentation guidelines.
StationAPI AGENTS.md
Instructions for TrainLCD/StationAPI, covering stationapi repository guidelines, project layout, tooling and environment, running and deploying and data management.