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jev-compaction

Use when a transcript has to be cut to a fixed size and you must choose which turns go. Jev marks each turn keep, summarize or drop. Measured: it does not make a handoff better.

Install / Use

npx skills add kerpopule/hermes-jev-skills --skill jev-compaction

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

85/100

Supported Platforms

Universal

Tags

Our assessment of jev-compaction

jev-compaction scores 85/100 on our quality scale, 2649th of 4,650 Development & Engineering skills we index.

Its SKILL.md is 4.7 KB long, split into 5 sections and no code examples: a solid amount of guidance for an agent.

With 1,266 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
11/20
Description
15/15
Adoption
13/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 9 days ago, so jev-compaction is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

jev-compaction compared with similar skills

All 4 of these similar skills score higher than jev-compaction; compare them before choosing.

SkillScoreStarsUpdatedFormat
jev-compaction (this skill)by kerpopule851.3k9d agoSKILL.md
taste-skillby Leonxlnx100111.8ktodayCLAUDE.md
ai-job-searchby ai-job-search-dev10070.4ktodayCLAUDE.md
claude-howtoby luongnv8910046.7k5d agoCLAUDE.md
lobehubby lobehub10084.4ktodayMCP Server

Frequently asked questions

How do I install jev-compaction?
Run npx skills add kerpopule/hermes-jev-skills --skill jev-compaction. The install tabs above show the steps for each supported agent.
Which AI agents does jev-compaction work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is jev-compaction safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is jev-compaction still maintained?
The repository was last updated 9 days ago, so jev-compaction is actively maintained.

name: jev-compaction description: Use when a transcript has to be cut to a fixed size and you must choose which turns go. Jev marks each turn keep, summarize or drop. Measured: it does not make a handoff better. version: 0.2.0 license: MIT metadata: hermes: tags: [jev, typesafe, compaction, handoff, context]

Choosing turns with Jev, and what actually makes a handoff work

Jev cannot write a summary. It can mark each turn of a transcript:

  • keep: carries a decision, a constraint, a preference, unfinished work, or an exact value, path, id, command or error that later work depends on.
  • summarize: background whose gist matters. In the digest this is the turn's first 400 characters, nothing more. Jev writes no gist.
  • drop: chatter, superseded attempts, repeated output.

It judges a long turn on its first 350 and last 350 characters, redacted, 40 turns per request, and sees no other turn while it does.

Read this before you use it for a handoff

This skill used to say a handoff written from Jev's digest "stops losing the one line that mattered". We measured that on seven real sessions and 104 recall questions (scorecard) and it was wrong:

| the writer reads | capsule | recall alone | with one search of the old session | |---|---|---|---| | Jev's digest | 400 words | 37.5% | 68.3% | | the plain last 24,000 characters | 400 words | 48.1% | 68.3% | | the whole dialogue | 1,200 words | 58.7% | 75.0% | | nothing: no handoff at all | | | 56.7% |

Jev's marks did beat the same marks handed out by recency (11 questions to 4), so the judgement is real. The digest built around it clips every other turn to 400 characters, and that cost more than the judgement earned. Nous Research found the same shape with a different Jev design (hermes-agent PR 116246).

So, for a handoff:

  1. Give the writer the whole dialogue. A current flash model reads 100,000 characters for about a cent. Do not pre-filter it.
  2. Ask for up to 1,200 words, five headings: Working on, State, Decisions, Pointers, Next. At 400 words the capsule was full whatever the writer had read.
  3. Name the session in the handoff and say it is searchable. One search was worth 16 to 33 points to every handoff we tried, and a session with no handoff and one search beat every handoff without one. On Hermes: session_search(query="..."), then session_search(session_id=..., around_message_id=...). Passing query together with session_id ignores the query.
  4. Do not append a list of "identifiers seen". It looked free and obvious; it changed nothing with a handoff and cost 13 points without one.

The hermes-handoff plugin does all four. HANDOFF_JEV=1 puts the Jev pre-pass back if you want to compare on your own sessions with evals/compaction/run_eval.py.

When this skill is still the right tool

When the size is fixed and something has to go: a small local writer, a context you cannot grow, a digest for a person to skim. There, choosing turns with Jev beat choosing them by recency.

  1. Get the transcript as a list of {role, content} messages. On Hermes: hermes sessions export --session-id <id> --format jsonl -.

  2. Select:

    • Hermes: call jev_compact_select with messages.

    • Anywhere else:

      jev compact-select --digest < transcript.json      # {"messages":[...]} or a bare list
      
  3. Write from digest. [KEEP VERBATIM] lines go in unchanged. [background] lines are clipped already; treat them as context, not as the record.

  4. The digest is cut to its last 24,000 characters by default, oldest first, keep lines included. Pass a larger limit if early keep lines matter.

Guarantees

  • The last six messages are always kept (keep_last); system messages are always kept.
  • Nothing is dropped unless Jev was confident (0.7+). An unjudged turn is marked summarize, never drop. Summarize still means clipped to 400 characters.
  • Turns that look like they hold a secret are not sent to Jev.
  • Jev down: every turn comes back summarize. That is a worse input than the plain transcript, so on status: "fail_open" use the plain transcript instead.
  • status: "partial" means some batches answered and some did not; the ids in unjudged sat at the summarize default with nobody judging them. Treat it like fail_open unless unjudged is short and you can see it does not cover the turns you care about. It used to report ok in this case, so one good batch hid every failed one.

When to compact at all

should_compact is arithmetic, not a model call: compact at 60% of the window, urgently at 85%. Do not ask a model whether the window is full.

Related Skills

View on GitHub
GitHub Stars1.3k
CategoryDevelopment
Updated9d ago
Forks124

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions