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Context Handoff Bundle

CI Python 3.10+ License: MIT

Resume yesterday's AI coding session for ~3% of the tokens.

Every new session, your AI coding agent re-orients from zero: re-reads the README, re-walks the source tree, re-derives everything it already knew yesterday. With frontier models like Anthropic's Fable 5 and Opus, that cold start burns tens of thousands of input tokens -- every single session -- before any real work happens.

Context Handoff Bundle captures what the agent learned during a session into a structured, evidence-backed bundle. The next session loads it as a compact resume -- typically a few hundred tokens instead of a multi-thousand-token re-read -- with honest drift detection that tells you what's still safe to trust. Plain files on disk. No cloud, no API keys, zero dependencies.

Terminal 1 (end of day):
$ context-handoff-bundle save --title "Auth refactor progress"
  saved: strong (0.84) -- 6 findings, 4 evidence anchors, 3 open questions

Terminal 2 (next morning):
$ context-handoff-bundle load

# Resume: Auth refactor progress

## State
- Active areas: middleware, token-validation, session-handling
- Work in progress (2 modified, 1 staged): src/middleware/auth.ts, src/routes/api.ts
- Technology stack: TypeScript, Node.js, Docker

## Drift
Severity: LOW -- 2 commits, 3 files touched since save.
- src/middleware/auth.ts CHANGED
- Findings: **medium** | Evidence: partial (1/4 affected) | Recommendations: **medium**

## Open First
- src/middleware/auth.ts (changed since save)
- src/routes/api.ts (active WIP)

## Next Moves
1. Complete token rotation implementation
2. Add integration tests for session handling
3. Update API docs for new auth flow

[tokens] resume: ~640 | re-deriving from source (6 files): ~23.0k | saved: ~97% (chars/4 estimate)

No archaeology. No re-reading. No "let me look at the codebase..."


Why This Exists

AI coding agents build deep understanding during a session: which files matter, how components connect, what was tried, what decisions were made, what's still uncertain.

All of that disappears when the session ends.

The next session starts from zero. You re-explain. The agent re-reads. You repeat research. Every time.

Context Handoff Bundle fixes this with three ideas:

  1. Structured save -- not a chat export, but findings + evidence + open questions
  2. Drift detection -- on load, tells you exactly what changed and which findings are still safe
  3. No fake certainty -- quality scores, per-section confidence, honest "I don't know" signals

The Token Math

Cold-starting an agent session means re-reading source files at full input price. A handoff resume replaces that with a few hundred tokens of distilled context.

Measured on this repo (every load prints your own numbers):

[tokens] resume: ~640 | re-deriving from source (6 files): ~23.0k | saved: ~97% (chars/4 estimate)
Cold start Handoff resume
What the agent reads Evidence files + README + docs, rediscovered by trial and error One distilled, drift-checked resume
Input tokens (this repo) ~23,000 ~640
Multiplied by every session, every terminal, every teammate once per save

Two sessions a day on a mid-size repo easily means 50k-400k tokens/day spent on pure re-orientation. With current frontier-model pricing, that's real money for zero new understanding -- the agent is paying to relearn what it already knew.

How the estimate works, honestly: tokens are estimated as chars / 4 (the standard heuristic), the re-read cost counts the bundle's evidence-anchored files plus standard orientation files (README, CLAUDE.md, etc.) at their current size. It's an order-of-magnitude measurement, not a billing statement -- and it's printed on every load so you can judge it against your own repo.


Install

pip install git+https://github.com/ucsandman/context-handoff-bundle.git

or with pipx for an isolated CLI install:

pipx install git+https://github.com/ucsandman/context-handoff-bundle.git

Development:

git clone https://github.com/ucsandman/context-handoff-bundle.git
cd context-handoff-bundle
pip install -e .
pytest

Requires Python 3.10+. Zero runtime dependencies (jsonschema is optional, for schema validation in validate).


Core Workflow

Save

context-handoff-bundle save --title "API migration analysis"

Automatically gathers from your repo:

  • Project purpose (README, CLAUDE.md)
  • Active code areas from file-path analysis
  • Recent git history and diff stats
  • Work in progress (staged, modified, untracked)
  • Test failure indicators
  • Evidence file paths
  • TODO/FIXME counts
{
  "saved": true,
  "bundle_id": "20260406-143022-api-migration",
  "quality": "strong",
  "score": 0.84,
  "warnings": [],
  "token_estimates": {
    "bundle_tokens": 3696,
    "source_reread_tokens": 23043,
    "source_files_counted": 6,
    "heuristic": "chars/4"
  }
}

Load

context-handoff-bundle load              # latest for current repo
context-handoff-bundle load api-migration # by slug
context-handoff-bundle load --deep       # full context mode

The resume output is operational, not archival:

Section What it tells you
State What's true, what was established, active code areas
Drift What changed since save, severity, which files moved
Section confidence Findings: strong, Evidence: partial, Recommendations: STALE
Open First Which files to look at, guided by drift and evidence
Must Reverify What not to trust blindly
Next Moves Specific actions, not generic "review the codebase"

Diff

context-handoff-bundle diff <older-bundle> <newer-bundle>

Compare two bundles to see what evolved:

  • New and gone findings
  • Resolved questions
  • Dropped recommendations
  • Evidence changes

Manage

context-handoff-bundle list                # see all bundles
context-handoff-bundle show latest         # inspect one bundle
context-handoff-bundle save --update       # refresh latest bundle
context-handoff-bundle prune --keep 1      # clean old duplicates
context-handoff-bundle delete <bundle-id>  # remove specific bundle

Drift Intelligence

This is the core differentiator. When you load a bundle, the tool doesn't just give you a summary -- it tells you how much of that summary is still safe.

At save time, every file anchor (src/auth/middleware.ts:40-62 — JWT verify) is resolved and its referenced line range content-hashed. At load time the hash is re-checked, commit anchors are verified against git, and each anchor gets an honest status: verified (content identical -- trust it, don't re-read it), ok (file untouched since save), changed (re-read before relying on it), gone (file or commit no longer exists), or n/a (URLs/commands -- not file-checkable). Line-range hashing means unrelated edits elsewhere in the file don't invalidate the anchor. All pure Python + git, zero LLM calls.

When something actually drifted:

## Drift
Severity: MEDIUM -- 8 commits, 12 files touched, 2 finding(s) at risk.
Evidence anchors: 5 verified, 1 changed, 1 gone (2 not file-checkable)

Anchors needing attention:
- src/auth/middleware.ts:40-62 — JWT verify CHANGED
- docs/auth-spec.md GONE

Findings that may be stale:
- F2 Auth middleware uses JWT with 24h expiry -- Evidence anchor changed
- F4 Session tokens stored in httpOnly cookies -- Evidence anchor gone

And when the repo moved but nothing the bundle relies on was touched, the whole section collapses to two lines instead of crying wolf:

## Drift
8 commit(s) and 12 file(s) touched since save; no evidence anchors affected. Evidence anchors: 7 verified

Findings are flagged only when their own anchors changed or disappeared; recommendations only when the majority of checkable anchors drifted. Raw commit volume is context, never a staleness verdict.

Severity levels:

  • NONE -- Bundle matches current repo state
  • LOW -- Repo activity, but all evidence anchors intact
  • MEDIUM -- Some anchored content changed; re-check the flagged findings
  • HIGH -- Multiple anchors gone or most findings at risk

Per-Section Confidence

Instead of one overall score, the resume shows confidence where it matters:

Section confidence:
- Findings: mixed (2/6 at risk)
- Evidence: partial (2/8 affected)
- Recommendations: STALE
- Overall: MEDIUM - some drift

This tells you: trust the findings that aren't flagged, re-check the two that are, and don't act on the recommendations without verifying first.


Quality Scoring

Every bundle is scored on trustworthiness, not prettiness.

Dimension Weight What it measures
Evidence coverage 3x Do findings point to real source files?
Open question honesty 2.5x Does the bundle admit what it doesn't know?
Repo specificity 2.5x Is this about a real project, not filler?
Findings substance 2x Are findings specific and actionable?
Resume usability 2x Does the resume actually help?
Completeness 1x Are files present with real content?
Markdown clarity 1x Is the narrative readable?
Entity richness 0.5x Structural extras (nice to have)
Relation richness 0.3x Structural extras (nice to have)

Evidence, honesty, and specificity are weighted 3-5x more than structural completeness. A bundle with strong evidence and honest uncertainties scores higher than a pretty bundle with vague findings.

Ratings:

  • strong (>= 0.6) -- Trust it for continuation
  • acceptable (>= 0.35) -- Usable, may have gaps
  • weak (< 0.35) -- Saved but flagged

Claude Code Integration

Slash commands for Claude Code:

/handoff-save    Author a notes file from the session, then save a handoff bundle
/handoff-load    Load and resume from a saved handoff
/handoff-list    List available bundles
/handoff-show    Inspect a specific bundle

Setup (global — available in any project):

cp commands/handoff-*.md ~/.claude/commands/

Setup (project-local — available only in this repo):

cp commands/handoff-*.md .claude/commands/

All logic lives in the Python CLI, not in the prompt text. The command files are thin wrappers.

Important — /handoff-save requires --notes: The CLI cannot see your conversation. When you run /handoff-save, Claude Code will author a structured notes file from the live session (findings, evidence, open questions) and pass it to the CLI via --notes. Saving without notes falls back to a blind directory scan and produces a low-quality stub. See the notes format in Richer Bundles from Notes below.


Storage

Repo-local:  .context-handoffs/index.json     (per-repo, gitignored)
Global:      ~/.context-handoff-bundles/index.json   (cross-repo)

Bundles are stored in registry-backed directories with index.json for fast lookup by ID, slug, title, repo, or tags. save defaults to the global store for cross-terminal portability. load searches both stores. Use --repo-local to force project-scoped storage.

Set CONTEXT_HANDOFF_HOME to relocate the global store (the test suite uses this so it never touches your real bundles).

Ambiguous queries surface options instead of guessing.


Bundle Format

Each bundle is a directory of structured files:

File Purpose
CONTEXT_HANDOFF.md Human-readable narrative
summary.json Findings, recommendations, resume instructions
entities.json Normalized entities (projects, modules, concepts)
relations.json Typed relationships between entities
evidence_index.json Source file anchors backing findings
open_questions.json Uncertainties with verification paths
resume_prompt.txt Bootstrap prompt for fresh sessions
bundle_metadata.json Git state, tags, quality score

Plain JSON and Markdown. No proprietary formats. No cloud. No API keys.


Richer Bundles from Notes

For maximum quality, save from structured session notes:

context-handoff-bundle save --title "Architecture review" --notes notes.md
## Scope
Cross-service auth consolidation review.

## Projects mentioned
- API Gateway
- Auth Service
- User Service

## Findings
- Auth middleware duplicated across 3 services with subtle differences
- Session tokens use inconsistent expiry across services
- Gateway handles rate limiting but auth service doesn't

## Open questions
- Should we consolidate to a shared auth library or API gateway middleware?
- What's the migration path for existing session tokens?

## Evidence anchors
- api-gateway/src/middleware/auth.ts
- auth-service/src/handlers/session.ts
- user-service/src/middleware/verify.ts

All Commands

Command Purpose
save Generate, score, and store a handoff bundle
save --update Refresh the latest bundle with current context
save --notes <file> Generate from structured session notes
load [query] Resolve and emit operational resume context
load --deep Full context mode with all evidence
diff <a> <b> Compare two bundles, show evolution
list List stored bundles with quality ratings
show <query> Inspect a single bundle in detail
score <path> Recompute quality score
prune Remove old duplicates per slug
delete <query> Remove a specific bundle
validate <path> Check bundle files and JSON schemas
init Create bundle directory from templates

Architecture

src/context_handoff_bundle/
  cli.py           CLI commands and argument parsing
  autocontext.py   Task-aware repo context gathering
  storage.py       Registry-backed bundle store
  quality.py       Weighted trustworthiness scoring
  drift.py         File-level drift analysis
  freshness.py     Age and branch checks
  resume.py        Operational resume composition
  compare.py       Bundle-to-bundle comparison
  tokens.py        Token estimation (resume cost vs source re-read)
  templates/       Bundle file templates
  schemas/         JSON schemas for validation

Design Principles

  1. No fake certainty. If evidence is weak, say so. If nothing is uncertain, warn "suspiciously certain."
  2. No silent guessing. Ambiguous queries surface options. Stale bundles surface drift.
  3. Code over prompts. Core logic in Python, not trapped in prompt text.
  4. Orient, don't flood. Load gives you re-entry guidance, not a document dump.
  5. Disk-backed. Everything on disk. No hidden session state. Works across terminals.
  6. Trustworthiness over prettiness. Quality scores weight evidence and honesty, not formatting.

Works With Any AI Coding Agent

The CLI and bundle format are agent-agnostic. Any tool that can run a shell command and read JSON/Markdown can use this. The slash commands target Claude Code, but the core is portable.


Contributing

PRs welcome. See docs/ROADMAP.md for current status and planned work.

Priority areas:

  • Sharper auto-context from deeper repo analysis
  • Richer drift intelligence (test result comparison, config changes)
  • Multi-session bundle merging
  • Integrations for Cursor, Copilot, Aider, Windsurf

License

MIT

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Resume yesterday's AI coding session for ~3% of the tokens. Save what your agent learned, load it in any terminal -- with drift detection.

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