Portable Handoff

Move work between AI sessions without losing what mattered.

You spend an hour with a coding agent. It learns your constraints, makes three
decisions you corrected twice, and gets halfway through a refactor. Then the
context window fills up, or you switch tools, and the next session starts from
nothing. Asking the model to "summarise what we did" gets you a summary that
reads well and quietly invents the parts it doesn't remember.

Portable Handoff writes a capsule instead: one Markdown file where the model
supplies the meaning and local code supplies the facts. Git state, file hashes
and timestamps come from your machine, not from the model's recollection. Every
claim is labelled with where it came from and how much to trust it.

What a capsule gets you
Install
Have your agent install this for you
Creating one
Reading one
Where it runs
Security
Limits
Contributing
License

What a capsule gets you

Loading one produces a briefing sized for a fresh session:

Written: 2026-08-18T05:52:51Z (less than a day old)
Staleness: **fresh**

## Current State
- Status: `blocked`
- Blockers: Awaiting a user decision before the next action can proceed

## Repository facts recorded in the capsule
- Branch: `master`
- Commit: `6e963af6a921724a647ffe09426ba12d17cd2d17`
- HEAD reachable from a remote: no
- Warning: the recorded commit was not present on any remote, so it may exist
  only on the machine that wrote this capsule.

## Blocking question - answer before editing files
> Should phase 1 land on main or on the release branch?

Three things there are worth noticing. The status is blocked, not
in_progress, because the draft carried an unanswered question and the tool
promoted it. The publication warning was not written by a model; it came from
git branch --remotes --contains. And the capsule refuses to let a later
session read "no blockers" while a decision is still open, which is how these
things quietly go wrong.

Install

Clone the repository. pip install alone gets you the CLI, but the skill
files an agent actually reads live next to it in the same clone, and pip does
not package them.

git clone https://github.com/legoambarish/portable-handoff
cd portable-handoff
pip install -e .
portable-handoff doctor --cwd .

Python 3.11 or newer. No runtime dependencies at all, no network access, no
account, no API key, no database, no vector store. doctor prints
supported, degraded, or unsupported for wherever you just ran it, which
is worth checking before anything else.

The commands below assume you're inside that clone.

Claude Code

Skills live in ~/.claude/skills/ for every project on the machine, or in
.claude/skills/ for one project only.

python scripts/install_skill.py --destination ~/.claude/skills/handoff

Start a new session and handoff shows up in the skill list. There's also a
/handoff slash command at integrations/claude/commands/handoff.md; copy it
to .claude/commands/handoff.md in a project if you'd rather type the command
than wait for the skill to trigger on its own.

OpenAI Codex CLI

Same idea, Codex's own directory: ~/.codex/skills/ machine-wide, or
.codex/skills/ for one project.

python scripts/install_skill.py --destination ~/.codex/skills/handoff

Codex picks up a new skill on its own, or you can call it directly with
$handoff.

Cursor

Cursor's agent already has a shell, so it can run the CLI commands above with
nothing extra installed. If you want it to reach for the skill without being
told, turn the existing instructions into a project rule:

mkdir -p .cursor/rules
cp integrations/cursor/commands/handoff.md .cursor/rules/handoff.mdc

That file has no frontmatter yet, so add the alwaysApply or globs block
Cursor's rules docs describe if you want it
to load without being asked.

ChatGPT, or anything with no shell

There's nowhere here to write a file and no CLI to run doctor with, so
there's nothing to install. Paste the shape from
skills/handoff/assets/handoff-template.md into the chat, have the model fill
it in, and save what comes back. That's a draft, not a capsule. It has no
digest and no verified repository facts. Run finalize on it later, from a
machine that has the CLI, to get an actual one.

Anything else with a shell

python scripts/install_skill.py --destination PATH_TO_HOST_SKILLS/handoff

integrations/generic/HANDOFF_INSTRUCTIONS.md has the same instructions
written for a host that doesn't read SKILL.md on its own.

Have your agent install this for you

Paste into Claude Code, Codex, Cursor, or anything else with a shell:

Clone https://github.com/legoambarish/portable-handoff, run pip install -e .
inside the clone, then run python scripts/install_skill.py --destination
pointed at your own skills directory: ~/.claude/skills/handoff if you're
Claude Code, ~/.codex/skills/handoff if you're Codex, or wherever else is
right for you. Run portable-handoff doctor --cwd . afterward and paste its
output back to me exactly as printed, with nothing added.

That last instruction is not incidental. The skill's own rule is to quote a
command's result, not describe it, because a paraphrased success and an
invented one read the same on the page. Asking for it during install is what
catches the run that silently failed instead of the one that says it did.

Creating one

The model writes a draft JSON from what it can see. Local code collects the
facts and merges them, overriding the model wherever the two disagree.

portable-handoff preflight --cwd . --source-host codex
portable-handoff finalize --preflight .handoff/evidence/preflight-xxxx.json --draft DRAFT.json --output auto

finalize prints one line you can quote:

{"outcome":"created","path":"...","schema_version":"1.2","validated":true,"redactions":[]}

Drafts are meant to be cheap to write. Any list of claims takes a bare string,
which defaults to model_inference / inferred, so a model only spends tokens
on the few items that deserve a stronger label:

"constraints": [
  "Never deploy the full dirty worktree",
  {"text": "Page speed is a hard requirement", "provenance": "conversation:user", "trust": "claimed"}
]

trust: verified needs a deterministic source (git, tool, test, file,
transcript). Anything else gets downgraded to claimed on the way in, so a
model cannot launder its own recollection into a fact.

Reading one

portable-handoff load latest --cwd .          # briefing, ~15% the size of the file
portable-handoff validate CAPSULE.md --cwd .
portable-handoff list --cwd .
portable-handoff export CAPSULE.md --format prose   # for pasting into a chat

A capsule holds its content twice, as prose and as canonical JSON, because
humans and parsers want different things. Nobody needs both at once, so load
and export each hand you one half.

Loading checks the embedded JSON, the SHA-256 digest, agreement between the two
halves, and the current repository against what was recorded. Staleness comes
back as fresh, possibly_stale, stale, obsolete, unverified, or
missing. A capsule that fails its integrity check is refused, never repaired.

Where it runs

Portable Handoff needs a filesystem and a shell. It works in Claude Code,
Codex, Cursor, or any terminal. It does not work inside a plain ChatGPT or
Claude web conversation, where there is nowhere to write a capsule and nothing
to validate it with. doctor answers this per host:

`capability`	Meaning
`supported`	Capsules can be created and validated here
`degraded`	No Git, so repository facts are recorded as unknown
`unsupported`	No writable capsule directory; the skill will say so instead of improvising a prose summary

Slash-command registration differs per host; the artifact and the CLI behave
the same everywhere. Wrappers for Claude Code and Cursor live in
integrations/.

Security

The threat model assumes a capsule may have been written by someone hostile,
because the whole point is that capsules travel. Imported prose is data, never
instructions. Paths coming from a capsule are validated as repository-relative
before use. A next_action.command is printed as inert fenced text with a
read_only / review / dangerous label and is never executed.

Text is stripped of terminal control sequences and of invisible or
bidirectional Unicode, so a reviewer sees what the model reads. High-confidence
secrets are redacted before anything is hashed or written, and the report keeps
counts and categories without the matched values.

One thing the digest does not do: prove authorship. It is an unkeyed SHA-256,
so it catches truncation and accidental edits, and anyone who edits the JSON
can recompute it. SECURITY.md has the full model and the reporting process.

Limits

Semantic quality tracks the model doing the compaction. In practice most
entries in a real capsule end up labelled inferred, which is honest but thin,
and a capsule is only as good as what the model bothered to notice. Compaction
loses things. Git facts prove the repository state and nothing about what
anyone said.

v0.1 has no sync, no accounts, no team features, and no second model checking
the first one's work. The quality harness at tests/quality/evaluate_quality.py
runs twelve scenarios through the real pipeline and is itself mutation-tested,
which tells you the plumbing preserves what it is given. It cannot tell you the
model gave it much.

Contributing

CONTRIBUTING.md covers the invariants that shape every change, the
development loop, and what to update when the capsule format moves. Commits
need a Signed-off-by line (git commit -s) under the DCO.

License

Apache-2.0. See LICENSE, NOTICE, and THIRD_PARTY_NOTICES.md.
CLEAN_ROOM.md records the provenance of the implementation.

portable-handoff