| name | scripting |
| description | Pick the right scripting runtime. Deno+dax by default, Python+uv only when a Python-only library is needed. Load before generating any script. |
Scripting Defaults
Decision
| Script needs… | Use | Skill to load |
|---|
| Shell commands, pipes | Deno + dax | deno |
| HTTP/data/batch | Deno + dax | deno + longrun |
| Python-only lib (Telethon, bip_utils, libgen-api, radon…) | Python + uv run | python |
Default is Deno. Only pick Python when a required library doesn't exist in JS/TS.
Deno (default)
#!/usr/bin/env -S deno run --allow-all
import $ from "jsr:@david/dax@0.44.2";
import { pooledMap, retry } from "jsr:@std/async";
import { sortBy, partition, chunk, sumOf } from "jsr:@std/collections";
Single file, inline jsr: imports, no config files. Load deno skill for full reference.
Python (fallback)
from plumbum import local, RETCODE
from plumbum.cmd import git, curl, jq
PEP 723 inline deps, zero-setup. plumbum for shell commands (only lib where piping works). Load python skill for full reference.
Never generate venvs, requirements.txt, or deno.json for throwaway scripts.
pi-llm — LLM Instead of Heuristics
Before writing fuzzy matching, entity resolution, or classification logic — use ask(). It's cheaper than iterating on string algorithms.
| Task | ❌ Don't | ✅ Do |
|---|
| "Same hotel?" | Jaccard, identity keywords, compound tokenizer (10 iterations) | ask("Same hotel? A='X' B='Y'") — done, first try |
| "What category?" | regex chains, keyword lists | ask("Classify: ...") |
| "Extract field" | regex with 12 edge cases | ask("Extract room type from: ...") |
import { ask, run } from "~/.pi/agent/lib/pi-llm.ts";
const answer = await ask("Is this critical?", { model: "claude-haiku-4-5" });
const result = await run("Research X", { tools: "full", maxTurns: 8 });
Rule: if you're on iteration 2+ of fixing edge cases in string matching, you already wasted time — should have used ask() from the start. Tokens are unlimited (Max subscription). Optimize for results, not cost. See longrun skill for batch patterns.
