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softaworks/session-handoff

softaworks

session-handoff

Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.

global
0installs0uses~1.6k
v1.1Saved Apr 20, 2026

Handoff

Creates comprehensive handoff documents that enable fresh AI agents to seamlessly continue work with zero ambiguity. Solves the long-running agent context exhaustion problem.

Mode Selection

Determine which mode applies:

Creating a handoff? User wants to save current state, pause work, or context is getting full.

  • Follow: CREATE Workflow below

Resuming from a handoff? User wants to continue previous work, load context, or mentions an existing handoff.

  • Follow: RESUME Workflow below

Proactive suggestion? After substantial work (5+ file edits, complex debugging, major decisions), suggest:

"We've made significant progress. Consider creating a handoff document to preserve this context for future sessions. Say 'create handoff' when ready."

CREATE Workflow

Step 1: Generate Scaffold

Run the smart scaffold script to create a pre-filled handoff document:

python scripts/create_handoff.py [task-slug]

Example: python scripts/create_handoff.py implementing-user-auth

For continuation handoffs (linking to previous work):

python scripts/create_handoff.py "auth-part-2" --continues-from 2024-01-15-auth.md

The script will:

  • Create .claude/handoffs/ directory if needed
  • Generate timestamped filename
  • Pre-fill: timestamp, project path, git branch, recent commits, modified files
  • Add handoff chain links if continuing from previous
  • Output file path for editing

Step 2: Complete the Handoff Document

Open the generated file and fill in all [TODO: ...] sections. Prioritize these sections:

  1. Current State Summary - What's happening right now
  2. Important Context - Critical info the next agent MUST know
  3. Immediate Next Steps - Clear, actionable first steps
  4. Decisions Made - Choices with rationale (not just outcomes)

Use the template structure in references/handoff-template.md for guidance.

Step 3: Validate the Handoff

Run the validation script to check completeness and security:

python scripts/validate_handoff.py <handoff-file>

The validator checks:

  • No [TODO: ...] placeholders remaining
  • Required sections present and populated
  • No potential secrets detected (API keys, passwords, tokens)
  • Referenced files exist
  • Quality score (0-100)

Do not finalize a handoff with secrets detected or score below 70.

Step 4: Confirm Handoff

Report to user:

  • Handoff file location
  • Validation score and any warnings
  • Summary of captured context
  • First action item for next session

RESUME Workflow

Step 1: Find Available Handoffs

List handoffs in the current project:

python scripts/list_handoffs.py

This shows all handoffs with dates, titles, and completion status.

Step 2: Check Staleness

Before loading, check how current the handoff is:

python scripts/check_staleness.py <handoff-file>

Staleness levels:

  • FRESH: Safe to resume - minimal changes since handoff
  • SLIGHTLY_STALE: Review changes, then resume
  • STALE: Verify context carefully before resuming
  • VERY_STALE: Consider creating a fresh handoff

The script checks:

  • Time since handoff was created
  • Git commits since handoff
  • Files changed since handoff
  • Branch divergence
  • Missing referenced files

Step 3: Load the Handoff

Read the relevant handoff document completely before taking any action.

If handoff is part of a chain (has "Continues from" link), also read the linked previous handoff for full context.

Step 4: Verify Context

Follow the checklist in references/resume-checklist.md:

  1. Verify project directory and git branch match
  2. Check if blockers have been resolved
  3. Validate assumptions still hold
  4. Review modified files for conflicts
  5. Check environment state

Step 5: Begin Work

Start with "Immediate Next Steps" item #1 from the handoff document.

Reference these sections as you work:

  • "Critical Files" for important locations
  • "Key Patterns Discovered" for conventions to follow
  • "Potential Gotchas" to avoid known issues

Step 6: Update or Chain Handoffs

As you work:

  • Mark completed items in "Pending Work"
  • Add new discoveries to relevant sections
  • For long sessions: create a new handoff with --continues-from to chain them

Handoff Chaining

For long-running projects, chain handoffs together to maintain context lineage:

handoff-1.md (initial work)
    ↓
handoff-2.md --continues-from handoff-1.md
    ↓
handoff-3.md --continues-from handoff-2.md

Each handoff in the chain:

  • Links to its predecessor
  • Can mark older handoffs as superseded
  • Provides context breadcrumbs for new agents

When resuming from a chain, read the most recent handoff first, then reference predecessors as needed.

Storage Location

Handoffs are stored in: .claude/handoffs/

Naming convention: YYYY-MM-DD-HHMMSS-[slug].md

Example: 2024-01-15-143022-implementing-auth.md

Resources

scripts/

Script Purpose
create_handoff.py [slug] [--continues-from <file>] Generate new handoff with smart scaffolding
list_handoffs.py [path] List available handoffs in a project
validate_handoff.py <file> Check completeness, quality, and security
check_staleness.py <file> Assess if handoff context is still current

references/

Files12
12 files · 75.4 KB

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Overall Score

87/100

Grade

A

Excellent

Safety

90

Quality

88

Clarity

85

Completeness

82

Summary

The session-handoff skill creates comprehensive handoff documents that enable AI agents to seamlessly continue work across sessions, solving context exhaustion in long-running projects. It provides CREATE and RESUME workflows with smart scaffolding, validation, staleness detection, and handoff chaining to preserve context, decisions, and state across agent transitions.

Static Analysis Findings

1 finding

Patterns detected by deterministic static analysis before AI scoring. Hover over any finding code for detailed information and remediation guidance.

Credential Exposure
SEC-020Direct .env File Access2x in 1 file

Direct .env file access

evals/setup_test_env.py.env2x

Detected Capabilities

create timestamped handoff documents with auto-detected git metadatavalidate handoff completeness and detect potential secretsassess handoff staleness by comparing git state to creation timelist available handoffs in a projectlink handoffs in chains for project continuityextract and scaffold critical context sectionsverify file references and project state

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

create handoffsave session statecontext is fullresume from handoffcontinue where left offdocument decisionspreserve progresspause work session

Risk Signals

INFO

Direct .env file access during test setup

evals/setup_test_env.py:create_sample_handoffs()
INFO

Referenced domain: jwt.io in handoff template examples

references/handoff-template.md, evals/test-scenarios.md
INFO

Secret detection patterns scanned in validate_handoff.py

scripts/validate_handoff.py:SECRET_PATTERNS

Referenced Domains

External domains referenced in skill content, detected by static analysis.

jwt.io

Use Cases

  • preserve context before context window fills
  • enable agent continuity on multi-session projects
  • document architectural decisions and rationale
  • resume work after breaks or session transitions
  • chain related handoffs for long-running tasks

Quality Notes

  • Excellent documentation: comprehensive README, detailed SKILL.md instructions, clear references for template and resume checklist
  • Strong validation layer: scripts check for incomplete sections, secrets, file references, and quality scoring with clear pass/fail criteria (>=70 required)
  • Good error handling: staleness checker provides granular assessment (FRESH/SLIGHTLY_STALE/STALE/VERY_STALE) with specific issue lists and recommendations
  • Well-structured workflows: CREATE and RESUME modes clearly separated with numbered steps and decision logic
  • Comprehensive testing: includes test scenarios, model-specific expectations, and baseline results for Opus
  • Security-conscious: explicit warnings about secrets, environment variable name-only documentation, validation prevents finalization with detected credentials
  • Handoff chaining design is thoughtful: uses --continues-from flag, maintains predecessor links, supports long-running project context lineage
  • Scripts are production-quality: proper error handling, timeout protection, git integration, timestamp parsing with fallbacks
  • Minor: Some TODO sections in template are instructional examples (e.g., '[Untitled - needs completion]') that could be clearer as distinct from user-fill TODOs, though validate_handoff.py correctly catches actual incomplete content
Model: claude-haiku-4-5-20251001Analyzed: Apr 20, 2026

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Version History

v1.1

Content updated

2026-04-20

Latest
v1.0

No changelog

2026-04-12

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