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/programasweights/programasweights-python/agents-mdgit clone --depth 1 https://github.com/programasweights/programasweights-pythonWhat 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 | $0.02567 | $0.02567 |
| Opus 5 | $0.01283 | $0.01283 |
| Sonnet 5 | $0.00513 | $0.00513 |
| Haiku 4.5 | $0.00257 | $0.00257 |
Grade A, and why
programasweights-python AGENTS.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 2d 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 — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ProgramAsWeights (PAW)
PAW compiles natural language specifications into tiny neural functions that run locally. Each function takes a single text input and returns a single text output. Use it when you need fuzzy text processing — classification, extraction, format repair, search, triage — that regex can't handle but a full LLM is overkill for.
Website: https://programasweights.com Full documentation: https://programasweights.readthedocs.io
When to Use PAW
- Fuzzy search — typo-tolerant matching, semantic search, near-duplicate detection
- Format repair — fix broken JSON, normalize dates, repair malformed inputs
- Classification — sentiment, urgency, categories defined in your own words
- Extraction — emails, names, dates from messy unstructured text
- Log triage — extract errors from verbose output, filter noise
- Intent routing — map user descriptions to the closest URL, menu item, or setting
- Agent preprocessing — parse tool calls, validate outputs, route tasks
Install
pip install programasweights --extra-index-url https://pypi.programasweights.com/simple/
Quickstart
import programasweights as paw
# Use a pre-compiled function (downloads once, runs locally forever)
fn = paw.function("email-triage")
fn("Urgent: server is down!") # "immediate"
fn("Newsletter: spring picnic") # "wait"
# Compile your own from a description
program = paw.compile(
"Fix malformed JSON: repair missing quotes and trailing commas"
)
fn = paw.function(program.id)
fn("{name: 'Alice',}") # '{"name":"Alice"}'
# Or compile and load in one step
fn = paw.compile_and_load("Classify sentiment as positive or negative")
fn("I love this!") # "positive"
If you want the smaller browser-compatible runtime explicitly, pass compiler="paw-4b-gpt2". Otherwise, omit compiler and let the server default decide.
Current Public Compilers
- Standard (
paw-4b-qwen3-0.6b) — higher accuracy, 594 MB base + ~22 MB/program. This is the current server default. - Compact (
paw-4b-gpt2) — smaller (134 MB base + ~5 MB/program), runs in browser via WebAssembly.
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.
- 2d ago First seen · 250 lines · 2,567 tokens per session scan A 5a9cc3b4c385
programasweights-python AGENTS.md is an instructions file published in the GitHub repository programasweights/programasweights-python (268 stars, last pushed 1mo ago), licensed MIT. It adds 2,567 tokens to every session, about $0.0128 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
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
buildNext
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
spec-kit AGENTS.md
Instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
langchain AGENTS.md
Instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.