Craft: Jev Compactor
Jev Compactor runs a made-up coding-agent session and lets Jev compact its context window. Your
craft is the session design: tasks Jev can tell apart, and settings with a clear, honest finding.
Everything is synthetic. It is a demo of the idea, not a real compaction plugin.
The decision
When the window passes budget, the viewer sends Jev:
state: the current task (title and keywords) and the last few messages.- one
choice question per tool result, 100 per call:
[Read] src/refund/ledger_cents.ts · 4,210 tokens. Preview: «export function apply_refund(...) { ... }»
Is this tool result still needed for the current task?
keep still needed, keep it word for word
trim only the gist matters now, keep the head
drop irrelevant now
Questions cannot see each other. Jev judges each block from its own text and the shared state.
Messages are never judged.
Ground truth
Every tool result secretly belongs to one task, or is junk. For the current task:
- Read and Edit results are detail needles. Every token counts. Ideal verdict: keep.
- Grep, Bash and WebFetch results are gist needles. About the first 300 tokens count. Ideal
verdict: trim.
- Everything else (other tasks, junk) is not needed. Ideal verdict: drop.
Needle recall = needed tokens that survived / needed tokens before. Junk removed = the same
for tokens that were not needed. About 4% of real results have a preview that only shows
boilerplate, so even an easy session is not perfect. That is a real limit, not noise.
The knobs
| knob |
what it does |
try |
tasks[].vocabulary |
the words Jev can tell tasks apart by |
8 to 12 single words, no word in two tasks |
distraction |
how much junk borrows the current task's words |
0.1 easy, 0.5 hard, 0.9 very hard |
target |
the window must end under target x budget |
0.4 default, 0.3 squeezes hard, 0.6 is gentle |
trimTo |
tokens a trimmed block keeps |
400 default, under 300 starts to lose gist needles |
budget |
when compaction fires |
200000 default, 1000000 for a long session |
noise, focus, mix |
how much junk, how much on-task work, which events |
more junk means bigger cuts |
taskEvery |
events before the made-up user moves on |
0 keeps one task forever, which fills the window with needles |
What to expect (mock, default tasks, 1500 events)
| distraction |
needle recall |
junk removed |
reduction |
| 0.12 |
about 98% |
about 97% |
about 81% |
| 0.3 |
about 96% |
about 86% |
about 72% |
| 0.5 |
about 90% |
about 82% |
about 70% |
| 0.9 |
about 78% |
about 78% |
about 70% |
The "summarize instead" baseline keeps about 49% of needle tokens on the same session.
Why recall falls: junk that talks like the task gets kept. The window stays above the target, so
the pressure pass trims the keeps Jev was least sure about. Some of those are real needles.
A good tuning loop
- Write the tasks. Run
node "$JEV_DSH/toolchain/check.mjs".
- Start easy (
distraction 0.1). Let a few compactions run. Read .harness/verdict.json:
the summary has recall and reduction, the findings have cost, calls and the baseline.
- Raise
distraction in steps of 0.2. Note where recall crosses recallTarget.
- At that edge, try one fix at a time: a higher
target, a bigger budget, a bigger trimTo,
sharper vocabularies. Say which one helped and what it cost in reduction.
- Try one bad idea on purpose: give two tasks the same words. Watch Jev keep the wrong task's
blocks. Report it.
The person's own transcript
Set "source": "my-session.jsonl" in session.json and the pane analyses a real transcript from
the workspace instead of the made-up session.
- Find it: Claude Code writes one
.jsonl per session under ~/.claude/projects/<project folder>/
(the project path with / turned into -). ls -t ~/.claude/projects/*/*.jsonl | head shows the
newest. Ask the person which one.
- Copy it into the workspace:
cp "<file>" ./my-session.jsonl. Inside the workspace, not under
.harness, at most 64 MB. Never edit the original.
- Never paste its contents into chat. Do not read it. Read
compaction-plan.json and
.harness/verdict.json instead: they hold tool names, input summaries and numbers only.
- With a Jev key, the task message, the last few messages and the head (about 300 characters) of
each tool result go to the Jev API. With no key on the machine (environment or credentials file) the offline mock
judges and nothing leaves it. Tell the person which one ran.
- There is no ground truth: no recall, no truth strip, no baseline. Report tokens before and after,
the reduction, keep / trim / drop counts, questions, calls, time, cost and the biggest drops.
- The task is the last user message. If the cut looks wrong, suggest another task button, or a pin,
then "Compact now".
trimTo is the only tuning knob that matters here.
- It is an analysis of what Jev would cut, not a plugin. No live session changes.
A good report: "my-session.jsonl, 1,144,012 -> 67,087 estimated tokens (94.1% cut). 167 tool
results: keep 13, trim 19, drop 135. 2 calls, mock. Biggest cuts: package-lock.json reads and
npm install logs."
Reporting
Give the settings and the measured numbers together, for example:
"distraction 0.3, target 0.4, trimTo 400: recall 96%, reduction 76% over 12 compactions, mock."
Always say it is a synthetic session, and whether the mock or live Jev ran. Never describe the
"summarize instead" lane as a real product. It is a simple baseline for contrast.
Definition of done
session.json passes check.mjs.- Your own tasks and repo name, not the template's.
- One settings line that holds the recall target with a large reduction, and one that breaks it,
both with numbers.
1---2name: jev-compactor3description: Design and tune a Jev Compactor session. Use when editing session.json, writing task vocabularies, or looking for settings where needle recall holds while the reduction is large.4---56# Craft: Jev Compactor78Jev Compactor runs a made-up coding-agent session and lets Jev compact its context window. Your9craft is the session design: tasks Jev can tell apart, and settings with a clear, honest finding.10Everything is synthetic. It is a demo of the idea, not a real compaction plugin.1112## The decision1314When the window passes `budget`, the viewer sends Jev:1516- `state`: the current task (title and keywords) and the last few messages.17- one `choice` question per tool result, 100 per call:1819```20[Read] src/refund/ledger_cents.ts · 4,210 tokens. Preview: «export function apply_refund(...) { ... }»21Is this tool result still needed for the current task?22 keep still needed, keep it word for word23 trim only the gist matters now, keep the head24 drop irrelevant now25```2627Questions cannot see each other. Jev judges each block from its own text and the shared state.28Messages are never judged.2930## Ground truth3132Every tool result secretly belongs to one task, or is junk. For the current task:3334- Read and Edit results are **detail** needles. Every token counts. Ideal verdict: keep.35- Grep, Bash and WebFetch results are **gist** needles. About the first 300 tokens count. Ideal36 verdict: trim.37- Everything else (other tasks, junk) is not needed. Ideal verdict: drop.3839**Needle recall** = needed tokens that survived / needed tokens before. **Junk removed** = the same40for tokens that were not needed. About 4% of real results have a preview that only shows41boilerplate, so even an easy session is not perfect. That is a real limit, not noise.4243## The knobs4445| knob | what it does | try |46|---|---|---|47| `tasks[].vocabulary` | the words Jev can tell tasks apart by | 8 to 12 single words, no word in two tasks |48| `distraction` | how much junk borrows the current task's words | 0.1 easy, 0.5 hard, 0.9 very hard |49| `target` | the window must end under target x budget | 0.4 default, 0.3 squeezes hard, 0.6 is gentle |50| `trimTo` | tokens a trimmed block keeps | 400 default, under 300 starts to lose gist needles |51| `budget` | when compaction fires | 200000 default, 1000000 for a long session |52| `noise`, `focus`, `mix` | how much junk, how much on-task work, which events | more junk means bigger cuts |53| `taskEvery` | events before the made-up user moves on | 0 keeps one task forever, which fills the window with needles |5455## What to expect (mock, default tasks, 1500 events)5657| distraction | needle recall | junk removed | reduction |58|---|---|---|---|59| 0.12 | about 98% | about 97% | about 81% |60| 0.3 | about 96% | about 86% | about 72% |61| 0.5 | about 90% | about 82% | about 70% |62| 0.9 | about 78% | about 78% | about 70% |6364The "summarize instead" baseline keeps about 49% of needle tokens on the same session.6566Why recall falls: junk that talks like the task gets kept. The window stays above the target, so67the pressure pass trims the keeps Jev was least sure about. Some of those are real needles.6869## A good tuning loop70711. Write the tasks. Run `node "$JEV_DSH/toolchain/check.mjs"`.722. Start easy (`distraction` 0.1). Let a few compactions run. Read `.harness/verdict.json`:73 the summary has recall and reduction, the findings have cost, calls and the baseline.743. Raise `distraction` in steps of 0.2. Note where recall crosses `recallTarget`.754. At that edge, try one fix at a time: a higher `target`, a bigger `budget`, a bigger `trimTo`,76 sharper vocabularies. Say which one helped and what it cost in reduction.775. Try one bad idea on purpose: give two tasks the same words. Watch Jev keep the wrong task's78 blocks. Report it.7980## The person's own transcript8182Set `"source": "my-session.jsonl"` in `session.json` and the pane analyses a real transcript from83the workspace instead of the made-up session.8485- Find it: Claude Code writes one `.jsonl` per session under `~/.claude/projects/<project folder>/`86 (the project path with `/` turned into `-`). `ls -t ~/.claude/projects/*/*.jsonl | head` shows the87 newest. Ask the person which one.88- Copy it into the workspace: `cp "<file>" ./my-session.jsonl`. Inside the workspace, not under89 `.harness`, at most 64 MB. Never edit the original.90- **Never paste its contents into chat.** Do not read it. Read `compaction-plan.json` and91 `.harness/verdict.json` instead: they hold tool names, input summaries and numbers only.92- With a Jev key, the task message, the last few messages and the head (about 300 characters) of93 each tool result go to the Jev API. With no key on the machine (environment or credentials file) the offline mock94 judges and nothing leaves it. Tell the person which one ran.95- There is no ground truth: no recall, no truth strip, no baseline. Report tokens before and after,96 the reduction, keep / trim / drop counts, questions, calls, time, cost and the biggest drops.97- The task is the last user message. If the cut looks wrong, suggest another task button, or a pin,98 then "Compact now". `trimTo` is the only tuning knob that matters here.99- It is an analysis of what Jev would cut, not a plugin. No live session changes.100101A good report: "my-session.jsonl, 1,144,012 -> 67,087 estimated tokens (94.1% cut). 167 tool102results: keep 13, trim 19, drop 135. 2 calls, mock. Biggest cuts: package-lock.json reads and103npm install logs."104105## Reporting106107Give the settings and the measured numbers together, for example:108"distraction 0.3, target 0.4, trimTo 400: recall 96%, reduction 76% over 12 compactions, mock."109Always say it is a synthetic session, and whether the mock or live Jev ran. Never describe the110"summarize instead" lane as a real product. It is a simple baseline for contrast.111112## Definition of done113114- `session.json` passes `check.mjs`.115- Your own tasks and repo name, not the template's.116- One settings line that holds the recall target with a large reduction, and one that breaks it,117 both with numbers.
autonomous-ai