# Firehose > Craft — Jev Firehose - Skill: `autonomous-ai/firehose` (Agent Skill) - Install (CLI): `npx skillmds@latest add autonomous-ai/firehose` - Raw SKILL.md: https://api.skillmd.com/api/skills/autonomous-ai/firehose/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: autonomous-ai (https://skillmd.com/u/autonomous-ai) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/autonomous-ai/firehose --- # Craft — Jev Firehose Jev Firehose is a triage demo on made-up data. Thousands of synthetic inbox messages stream in. Jev answers five typed questions about each one in a single call. Messages with low `team` confidence are escalated instead of auto-routed. Your craft is the desk: the teams, their words, the noise and the threshold. ## The decision, per message One `evaluate()` call. The state is the message text. The questions: ``` team choice one option per team, each with its description urgency score 5 levels, "no rush" to "emergency" spam noul needs_human noul mood score 3 levels, calm to furious ``` Routing rule, in this order: 1. `spam` is 0.5 or more: the message goes to the spam bin. 2. `team` confidence is under `threshold`: it goes to the escalate lane. 3. Otherwise it goes to the bin of the chosen team. A routed message is right when its bin matches its true team (or it is spam in the spam bin). Accuracy counts auto-routed messages only. Escalated ones are counted apart. ## The honest dial: `noise` `noise` adds `floor(noise * 3 + random)` phrases from ONE wrong team to each message, never more than the number of true phrases (1 to 3). - `0.05`: almost every message is clean. Expect 99% right and 2 to 4% escalated on the starter desk. - `0.2`: the starter setting. About 97% right, 10% escalated at threshold 0.55. - `0.5`: many messages are half about another team. About 80% right, 19% escalated. - `0.85`: nearly every message is an even split. About 60% right, 25% escalated. Nothing random is added to Jev's answers. The messages get more mixed, so the right answer gets less clear. A person would struggle too. ## The threshold trade-off Raising `threshold` sends more messages to the big model and makes the rest more accurate. The pane draws this curve above the threshold slider (green is accuracy, amber is the escalated share). `measure.mjs` prints the same thing as a table: ``` threshold right escalated 0.40 93.5% 0.7% 0.55 97.6% 10.3% <- threshold in the file 0.70 99.4% 19.0% lowest threshold with at least 95.0% right: 0.46 (95.5% right, 4.2% escalated) ``` Pick the lowest threshold that meets `targetAccuracy`. If escalations at that point are too high, the fix is the desk (sharper descriptions, less overlap), not the threshold. At high noise no threshold may reach the target without escalating most of the stream. That is a finding. Report it. ## Writing a desk that routes Jev gets each team's `id` and `description`, never the phrases. So write them as a pair: ```jsonc { "id": "brakes", "description": "Brakes: pads, rotor, lever, squeal, hydraulic bleed.", "phrases": [ "my brake lever is soft after the bleed", "the rotor rubs the pads and there is a squeal", "new pads but the lever still pulls to the bar", "hydraulic bleed needed, the brakes feel spongy" ] } ``` - 6 to 10 plain nouns in the description. No filler words. - Every phrase uses two or three of those nouns, in the same form (the offline stand-in does not match "invoice" with "invoices"). - No noun shared between two descriptions, unless you want that pair to be confused. - 8 to 12 phrases per team gives varied messages. 4 is the minimum. - Use `weight` to make some teams busier than others. Bins then fill at different speeds. - Keep ids short. They are the labels on the bins. `check.mjs` warns when a phrase shares fewer than 2 words with its own description, when a phrase leans toward another team, and when two descriptions share 3 or more words. ## The person's own messages Set `"source": "inbox.jsonl"` (also `.csv`, `.tsv`, `.json`) and the pane triages the person's own file instead of made-up messages. The file must be inside the workspace, up to 8 MB and 20,000 messages. `textColumn` is optional. Phrases are not needed, and `noise` is ignored. There is no answer key, so there is no accuracy, no red rings and no confusion matrix. What you can read instead: - **The confidence histogram.** A tall pile near 1.0 means the teams fit the messages. A wide spread, or a pile under the threshold, means the descriptions are vague or a team is missing. - **The share escalated** at your threshold, and the curve of it above the slider. - **The count per team.** A team with nothing in it is probably not in this inbox. A huge team is probably two teams. - **The most and least confident message of a team** (click a bin). The least confident one shows where a description is blurry. The craft, in order: 1. Read the file's header first, then 20 to 30 messages. Never the whole file. 2. Design the teams from what is really there. 4 to 10 teams. Plain nouns the senders really use. 3. `check.mjs`, then `measure.mjs`. Read the least confident ids and the two teams each sits between. Sharpen those two descriptions, or add the team that is missing. 4. Pick the threshold from the escalated share the person can live with. There is no accuracy to aim at here. 5. Tell the person where `triage.csv` is. The `triage` block in `.harness/verdict.json` has the path and the counts. Never copy the person's data around: no pasting it into `firehose.json`, no copies or extracts, as little of it in the chat as you can. With a key, only the text column goes to the Jev API. Without one, nothing leaves the machine, and the stand-in's word matching is only a rough triage. ## A good session 1. Write the desk. Run `check.mjs`. Fix errors, read warnings. 2. Run `measure.mjs` at your noise. Look at the most confused pairs. Sharpen those descriptions. 3. Set `threshold` to the lowest value that meets the target. Run `measure.mjs` again. 4. Try `--noise 0.6`. Say how the desk degrades. 5. Tell the person what you found and what to poke in the pane. ## Verifying ```bash node "$JEV_DSH/toolchain/check.mjs" node "$JEV_DSH/toolchain/measure.mjs" --n 800 ``` Then watch the pane: bins fill, the accuracy number settles near what `measure.mjs` printed, and the confusion matrix lights up on the pairs you expected. ## Definition of done - `firehose.json` passes `check.mjs`. - The desk is your own and says it is made up. - You can state accuracy and escalations at your threshold, and the lowest threshold that meets the target, from a measurement and not from a guess.