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Session Compaction And Restore

CommunityPopular
mvschwarz
session-compaction-and-restore

Use when reasoning about what survives compaction/context-loss/restart/seat-refresh, designing a high-fidelity restore packet, or distinguishing native runtime resume vs fork vs artifact-backed mental-model rebuild. Covers the 4 failure modes that prevent honest restore (compacted seat drops hot potato; restore packet preserves details but loses product intent; runtime resume mistaken for handover or fork; rebuilt seat starts with stale instructions).

Overview

Publishermvschwarz
Repositoryopenrig
Skill namesession-compaction-and-restore
Stars
5K
Forks
366
Bundled files
Instructions only
LicenseApache-2.0
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 mvschwarz on GitHub. Read the source before you install it.

Installation

Install the Session Compaction And Restore 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/mvschwarz/openrig/tree/main/packages/daemon/assets/plugins/openrig-core/skills/session-compaction-and-restore
  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/mvschwarz/openrig.git /tmp/openrig
mkdir -p .claude/skills
cp -r /tmp/openrig/packages/daemon/assets/plugins/openrig-core/skills/session-compaction-and-restore .claude/skills/session-compaction-and-restore
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Session Compaction And Restore 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 Session Compaction And Restore 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 Session Compaction And Restore 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.

Session Compaction and Restore

Preserving useful working state across compaction, context loss, restart, or seat refresh. Includes Claude compaction restore, Codex resume/fork mechanics, transcript-based mental-model rebuilds, and durable handoff packets.

Long-lived seats are valuable only if they can survive context pressure. If compaction turns a senior seat into a cold-started agent, users will avoid persistent topologies and fall back to throwaway agents.

Use this when

  • A seat is approaching compaction or just compacted
  • Designing a high-fidelity restore packet for an active workflow
  • Distinguishing native runtime resume vs fork vs artifact-backed mental-model rebuild
  • Reasoning about what should be preserved vs reconstructable
  • Auditing a restore for product-intent preservation (not just detail preservation)

Don't use this when

  • The session is fresh and has no working state to preserve
  • The intent is to create a new seat from a primed source — that's session-source-fork or agent-starters
  • The packet is a one-off snapshot for human review — restore packets are for re-entering active work

The 5 distinctions (do not collapse)

Per the cross-runtime restore/reentry packet standard:

ModeWhat it meansOutcome literal
Native resumeContinue the same managed seat with native runtime tokenresumed
ForkNew managed seat from prior native runtime conversation; new post-fork tokenforked
RebuildFresh-launch seeded with operator-declared artifacts in trust-precedence orderrebuilt
Artifact-backed mental-model rebuildRestored seat derives understanding from a packet rather than native runtime continuity(case of rebuilt)
Fresh launchNew agent without prior continuityfresh

These are load-bearing distinctions. Do NOT collapse fork into artifact-backed reentry; do NOT collapse rebuild into fork.

The three mechanisms — who owns each (verified vs main d37a08ad, 2026-07-21)

The outcomes above ride on three DISTINCT mechanisms; keep them separate:

  1. Claude built-in /compact — a Claude Code HARNESS feature, not OpenRig code. OpenRig only emits the literal /compact toward the pane after a prep turn; there is no compaction algorithm in the OpenRig repo. Provider-owned.
  2. OpenRig-managed restore — the enforcer + hook bridge: a precompact hook WRITES a restore packet; a bridge READER injects exactly ONE restore directive back via the harness hook channel (hookSpecificOutput.additionalContext), seat-isolated. The OpenRig-owned half.
  3. Codex session continuity — resume/rollout tokens (codex resume <token>), a separate path from the Claude mechanisms. Never conflate with Claude restore.

Safety boundary (ties to native-session-file-lab-boundary): no supported OpenRig path EDITS provider-owned native session / auth / transcript files to inject context. Context enters ONLY through sanctioned channels: the hook additionalContext, a normal user message via rig send, or Codex resume tokens. Precise nuance: OpenRig does write ~/.codex/config.toml — but only to install OpenRig activity hooks and [features], never session or auth state. So the rule is "never edits native session/auth/transcript files," NOT "never touches any provider-owned file."

Current CLI surface (operator / kernel seats; verified vs main d37a08ad)

Managed compaction/restore is driven by five shipped verbs — pick per intent, and stay durable-substrate-first (the packet/artifacts are the truth; the CLI is the trigger):

  • rig compact <session> — guided managed compaction for ONE Claude seat (prep → /compact → restore → audit). Non-Claude seats rejected. ~180s.
  • rig compact-plan — READ-ONLY triage; does not compact. Flags --rig, --refresh, --threshold-tokens, --threshold-percent. (Codex seats flagged codex_not_managed_by_claude_compact_in_place.)
  • rig restore-check — restore-readiness probe across running rigs; read-only; exit 0/1/2. Flags --full, --ready, --rig, --no-queue, --no-hooks.
  • rig restore-packet {write,read,validate} — the cross-runtime restore packet (v0): write / read / validate the durable packet this skill's contract defines.
  • rig restore <snapshotId> --rig <rigId> — ⚠ RIG SNAPSHOT restore (infra), a DIFFERENT mechanism — NOT session-context restore. Do not conflate.

Failure modes (4)

  1. A compacted seat forgets active workflow state and drops the hot potato. Compaction without continuity preservation is silent failure.
  2. A restore packet preserves details but loses the user's product intent. Restore must preserve why this work matters, not just what was happening.
  3. A runtime resume is mistaken for a seat handover or fork. These have different continuity outcomes and provenance — don't conflate.
  4. A rebuilt seat starts with stale instructions that conflict with current workflow mode. Restore must include current state, not just historical state.

Re-entering project context, on any runtime. A restore packet carries the work in flight, not the project's declared intent and context files. rig context work-install lists what the project declares; read the pieces the restored task needs. --deliver prints them all, and when several projects are declared (--json lists the ids), select one with --project <id> instead of guessing.

Proof standard

Proof should include a deliberate compaction/restart of a seat with active work, followed by measured recovery: identity, current workflow, next owner, relevant files, and constraints all restored without human re-briefing.

Canonical packet contract (16-field, v0)

The cross-runtime restore/reentry packet standard v0 defines:

  • Source/target identity
  • Runtimes
  • Workspace root, default repo, role pointer
  • Bounded latest transcript
  • Touched-path inventory
  • Durable work pointers
  • Current work + next owner
  • Caveats + authority boundaries
  • Omitted classes + redaction policy
  • Source-trust ranking
  • Generated-at + generator version

Plus a 6-item restored-seat acceptance checklist.

Source-trust ranking applies when restored seat ingests packet evidence: rig whoami > target rigspec > bounded latest transcript > full transcript > touched-files > restore-summary.json.

Memory surfaces consumed at restore time

A restore may consume transcripts, durable messages, startup context, checkpoints and a restore packet. Inventory the surfaces actually present for this seat, with their source, freshness and purpose. Do not infer that a named surface exists or grants write authority.

The active project/rig policy and task authorization determine what may be written. Treat provider-owned conversation records as evidence to read through supported tooling. For placement and durable context, load skills/openrig-operating-model/SKILL.md with rig context get; for the selected startup path, use skills/core/agent-startup-and-context-ingestion/SKILL.md. A packet or marker proves retained/delivered evidence, not successful provider restoration; measure the resumed seat against the proof standard above.

See also

  • claude-compaction-restore skill — the Claude Code restore SOP (PreCompact hook + JSONL restore script for post-compaction recovery)
  • session-source-fork skill — fork mode for native-runtime-continuity-based restoration
  • seat-continuity-and-handover skill — occupant-creation primitives (resume/fork/rebuild/fresh) that this primitive instantiates
  • openrig-operating-model skill — placement and authority of durable context

Frequently asked questions

What does the Session Compaction And Restore AI skill do?

Use when reasoning about what survives compaction/context-loss/restart/seat-refresh, designing a high-fidelity restore packet, or distinguishing native runtime resume vs fork vs artifact-backed mental-model rebuild. Covers the 4 failure modes that prevent honest restore (compacted seat drops hot potato; restore packet preserves details but loses product intent; runtime resume mistaken for handover or fork; rebuilt seat starts with stale instructions).

Why use Session Compaction And Restore on TypingMind?

Because you install it once and use it with any model. Session Compaction And Restore 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 Session Compaction And Restore in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mvschwarz/openrig/tree/main/packages/daemon/assets/plugins/openrig-core/skills/session-compaction-and-restore. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Session Compaction And Restore?

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 Session Compaction And Restore?

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

Is the Session Compaction And Restore AI skill free?

Yes. It is published on GitHub by mvschwarz under the Apache-2.0 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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