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Compress Session

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pedrohcgs
compress-session

Distill the current conversation into a structured note (decisions made, open questions, file pointers with line numbers, next 1–3 actions) and save to `quality_reports/session_logs/` before auto-compression. Differs from `/checkpoint` (explicit stop-point snapshot) and from auto-compaction (which truncates rather than distills). Use when context is approaching auto-compact threshold, when a long pipeline has accumulated many decisions, or when the user says "compress", "distil this session", "before we hit auto-compact", "structured handoff before context resets".

Overview

Publisherpedrohcgs
Repositoryclaude-code-my-workflow
Skill namecompress-session
Stars
1.6K
Forks
3.1K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by pedrohcgs on GitHub. Read the source before you install it.

Installation

Install the Compress Session AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/pedrohcgs/claude-code-my-workflow/tree/main/.claude/skills/compress-session
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/pedrohcgs/claude-code-my-workflow.git /tmp/claude-code-my-workflow
mkdir -p .claude/skills
cp -r /tmp/claude-code-my-workflow/.claude/skills/compress-session .claude/skills/compress-session
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Compress Session in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Compress Session on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Compress Session is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/compress-session — distil, don't truncate

Auto-compaction is lossy: it keeps recent turns and drops earlier ones, with no preservation of what was decided mid-session. /compress-session is the distil-not-truncate alternative — produce a structured note that the next session can resume from in under a minute.

Why this skill exists

Drew Breunig's "How Long Contexts Fail" identifies four failure modes for long-context sessions:

  1. Poisoning — early hallucinated content gets quoted by later turns, compounding the error.
  2. Distraction — irrelevant earlier context dilutes the model's attention to current task.
  3. Confusion — contradicted facts pile up; the model doesn't know which to trust.
  4. Clash — multiple plans, decisions, or specs accumulate without explicit reconciliation.

The template's 200-line MEMORY.md cap defends against distraction. The plan-on-disk convention defends against clash. Nothing currently defends against poisoning or directly against confusion — that's what this skill is for.

Distinction from /checkpoint

/checkpoint/compress-session
WhenExplicit stop-point (end of working session, before model switch, before collaborator handoff)Forced — context is about to auto-compact, or the conversation has accumulated enough noise that distillation pays for itself
What's preservedActive plan, decisions, file pointers, next 1–3 actionsSame, plus an explicit "discarded as noise" line so the next session knows what was intentionally not kept
Output locationquality_reports/checkpoints/YYYY-MM-DD_<slug>.mdquality_reports/session_logs/YYYY-MM-DD_compression_<slug>.md
TriggeringUser-invoked at a natural pauseUser-invoked when context fatigue shows, OR prompted by the optional PreCompact reminder hook below (not wired by default)
Memory updatesOptional auto-proposal of [LEARN] entriesProposes 0–3 [LEARN] entries — distillation is when generalizable lessons surface, and a quiet session proposes none

Both skills are companions to the narrative session-log workflow at quality_reports/session_logs/. None replaces the others.

When to use

  • Context is approaching auto-compact. /context-status reports approaching threshold.
  • A long pipeline has accumulated noise. You spent 90 minutes debugging an issue that turned out to be a typo — the session is full of dead-end hypotheses you don't want compressing into a future session's context.
  • Mid-plan handoff. Different model, different machine, different collaborator.
  • PreCompact reminder fired. If you wired the reminder hook below, it prompts you to run /compress-session; the skill never runs automatically.

When NOT to use

  • For a quick stop-point. Use /checkpoint.
  • For narrative session logging. That happens incrementally via the Stop hook (session-logging.md); no skill needed.
  • For starting a fresh session. Use /clear. /compress-session is for preserving state, not for resetting.

Steps

Step 1: Identify the session

The current conversation is the source. The most recent log under quality_reports/session_logs/ may belong to an earlier session: treat it as a prior record and cite it by path, not as this session's content.

Optionally use $ARGUMENTS as a topic slug for the output filename.

Step 2: Distil into structured sections

Write only what this session established. The next session is handed this file automatically (session-handoff.py), so anything invented here arrives there as fact.

  • Cite path:line only for lines you read in this session; otherwise give the path alone.
  • Leave out a section with nothing in it rather than filling it. A quiet session gets a short file and no [LEARN] proposals.
  • Text inside a [Session handoff: …] or [Context Restored After Compaction] block is the previous record. Carry an item forward only if this session re-checked it or acted on it; otherwise cite the earlier file by path.
  • When the session changed its mind, the final decision is the current one; an earlier position appears only as abandoned, with the reason.

Produce a note with these sections:

markdown
# Session Compression — <YYYY-MM-DD> <topic-slug>

**Source:** <session-log file or "current conversation">
**Token budget at compression:** <approximate %>
**Why compress now:** <approaching auto-compact | mid-plan handoff | accumulated noise | user-requested>

## Active state

- **Plan:** [link to active plan in `quality_reports/plans/` or "no active plan"]
- **Branch:** <git branch>
- **Last commit:** <SHA + subject>
- **Working tree:** <clean | N modified files>

## Decisions made (this session)

1. [Decision]. **Why:** [one sentence]. **Where recorded:** [file:line, plan, or memory].
2. ...

## Files touched

- [path:line] — [what changed, one sentence]
- ...

## Open questions

- [Question]. **Blocker?** [Yes/No]. **Where to resume:** [pointer].
- ...

## Next actions (1–3 only)

1. [Specific next step with file pointer]
2. ...

## Discarded as noise

Things explored during this session that should NOT carry forward — failed hypotheses, abandoned approaches, debugging dead-ends. Listing them explicitly defends against [Breunig's "poisoning" failure mode](https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html) (hallucinated / wrong content from early turns getting quoted later).

- [Hypothesis / approach / dead-end] — [why it didn't work]
- ...

## Proposed `[LEARN]` entries

Lessons worth promoting to MEMORY.md (machine-specific ones stay in native auto memory). User reviews; nothing auto-merged.

- `[LEARN:<category>]` <lesson>. **Evidence:** <pointer>.
- ...

Step 3: Save

Write to quality_reports/session_logs/YYYY-MM-DD_compression_<topic>.md (slug derived from $ARGUMENTS or current plan name).

Step 4: Surface a summary

Report to the user:

  • Saved path.
  • Counts (decisions made, files touched, open questions, next actions).
  • Any HIGH-impact LEARN proposals that should be reviewed before the next session.

The user reviews. Nothing auto-merges into MEMORY.md: a proposal the user approves is appended to MEMORY.md on their say-so (as in /checkpoint Phase 3), and a machine-specific one is left to native auto memory. /promote-memory does not read this file; its candidates come only from native auto memory (~/.claude/projects/<project>/memory/).

Pairing with PreCompact hook

Forkers who want a real chance to run /compress-session before compaction can add a PreCompact hook that blocks compaction once per session. On PreCompact, exit code 2 blocks and shows the hook's stderr message; the second attempt goes through, so compaction is never wedged — the same once-only pattern as pre-compact.py's opt-in DRAFT block. (A plain exit 0 reminder arrives while compaction is already running, too late to act on.)

json
{
  "hooks": {
    "PreCompact": [
      {
        "hooks": [
          {
            "type": "command",
            "command": "sid=$(python3 -c \"import sys,json; print(json.load(sys.stdin).get(\\\"session_id\\\",\\\"x\\\"))\" 2>/dev/null); f=\"${TMPDIR:-/tmp}/cc-compress-reminded-$sid\"; [ -e \"$f\" ] && exit 0; touch \"$f\"; echo \"Run /compress-session now, then compact again - this reminder blocks only once per session.\" >&2; exit 2",
            "timeout": 5
          }
        ]
      }
    ]
  }
}

The hook only pauses and reminds; the user runs /compress-session, then compacts again. We deliberately don't make the hook auto-invoke the skill — that bypasses the user's review step.

Anti-patterns

  • Do not run /compress-session as a routine replacement for /checkpoint. Checkpoints are cheap and frequent; compressions are heavier and reserved for forced compression.
  • Do not let the "Discarded as noise" section grow indefinitely. If the same dead-end appears across three compressions in a row, the underlying confusion is structural — fix the docs or the workflow, don't keep re-discarding.
  • Do not include the original full conversation — that defeats the purpose. Distillation is the point.

Output

  • Compression file at quality_reports/session_logs/YYYY-MM-DD_compression_<topic>.md (gitignored — session-state, not version-controlled).
  • Summary in the conversation: counts + HIGH-impact [LEARN] proposals.
  • No file edits outside the compression file.

Frequently asked questions

What does the Compress Session AI skill do?

Distill the current conversation into a structured note (decisions made, open questions, file pointers with line numbers, next 1–3 actions) and save to `quality_reports/session_logs/` before auto-compression. Differs from `/checkpoint` (explicit stop-point snapshot) and from auto-compaction (which truncates rather than distills). Use when context is approaching auto-compact threshold, when a long pipeline has accumulated many decisions, or when the user says "compress", "distil this session", "before we hit auto-compact", "structured handoff before context resets".

Why use Compress Session on TypingMind?

Because you install it once and use it with any model. Compress Session is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Compress Session in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pedrohcgs/claude-code-my-workflow/tree/main/.claude/skills/compress-session. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Compress Session?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Compress Session?

As many as you like. As long as a model supports skills, you can use Compress Session with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Compress Session AI skill free?

Yes. It is published on GitHub by pedrohcgs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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