CH

Pause autonomous agents for human feedback.

Install

mkdir -p .claude/skills/checkpoint-mode && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/3136" && unzip -o skill.zip -d .claude/skills/checkpoint-mode && rm skill.zip

Installs to .claude/skills/checkpoint-mode

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Pause for review every N tasks - selective autonomy pattern
59 charsno explicit “when” trigger
Beginner

Key capabilities

  • Pause agent execution after N tasks
  • Generate accomplishment summaries
  • Monitor for human approval signals
  • Resume work upon signal detection

How it works

The orchestrator tracks completed tasks against a frequency threshold, pauses execution, and waits for a specific signal file before resuming.

Inputs & outputs

You give it
Task completion count
You get back
Checkpoint summary file

When to use checkpoint-mode

  • Adding checkpoints to long-running tasks
  • Reviewing agent work in novel projects
  • Managing high-cost agent operations

About this skill

Checkpoint Mode Skill

Overview

Implements selective autonomy - shorter bursts of autonomous work with feedback loops.

Research Source: "Use Agents or Be Left Behind" by Tim Dettmers


Philosophy

"More than 90% of code should be written by agents, but iteratively design systems with shorter bursts of autonomy with feedback loops." — Tim Dettmers, 2026

Problem with Perpetual Autonomy:

  • Can waste resources on wrong approach
  • No opportunity for course correction
  • User feels disconnected from progress

Solution:

  • Pause after N tasks or M minutes
  • Generate summary of accomplishments
  • Wait for explicit approval to continue

When to Use

Use Checkpoint Mode For:

  • Novel projects where approach may need adjustment
  • High-cost operations (expensive API calls, cloud resources)
  • Learning phases where user wants to guide direction
  • Regulated environments requiring audit trail

Use Perpetual Mode For:

  • Well-defined PRDs with clear requirements
  • Established patterns with high confidence
  • Overnight builds where interruption isn't desired
  • CI/CD pipelines requiring full automation

Configuration

# Enable checkpoint mode
LOKI_AUTONOMY_MODE=checkpoint

# Pause frequency
LOKI_CHECKPOINT_FREQUENCY=10  # tasks
LOKI_CHECKPOINT_TIME=60  # minutes

# Always pause after these phases
LOKI_CHECKPOINT_PHASES="architecture,deployment"

Checkpoint Workflow

[Work on 10 tasks] → [Pause] → [Generate Summary] → [Wait for Approval]
                                                           ↓
                                              [User reviews and approves]
                                                           ↓
                                                    [Resume work]

On Checkpoint:

  1. Generate Summary

    # Checkpoint Summary
    
    ## Tasks Completed (10)
    - Implemented POST /api/todos endpoint
    - Added unit tests (95% coverage)
    - Set up CI/CD pipeline
    - ...
    
    ## Next Actions
    - Deploy to staging
    - Run integration tests
    - Security audit
    
    ## Resources Used
    - 15 minutes elapsed
    - 3 Haiku agents, 2 Sonnet agents
    - Estimated cost: $0.45
    
  2. Create Approval Signal

    # System writes:
    .loki/signals/CHECKPOINT_SUMMARY_2026-01-14-10-30.md
    
    # User reviews and creates:
    .loki/signals/CHECKPOINT_APPROVED
    
  3. Wait for Approval

    • Orchestrator pauses execution
    • Monitors for approval signal
    • Resumes when signal detected

Agent Instructions (Orchestrator)

When LOKI_AUTONOMY_MODE=checkpoint:

completed_tasks = load_completed_tasks()
tasks_since_checkpoint = completed_tasks - last_checkpoint_count

if tasks_since_checkpoint >= CHECKPOINT_FREQUENCY:
    # Pause and generate summary
    summary = generate_checkpoint_summary()
    write_signal("CHECKPOINT_SUMMARY", summary)

    # Wait for approval
    log_info("Waiting for checkpoint approval...")
    while not signal_exists("CHECKPOINT_APPROVED"):
        sleep(5)

    # Resume work
    remove_signal("CHECKPOINT_APPROVED")
    log_info("Checkpoint approved. Resuming work...")
    last_checkpoint_count = completed_tasks

Comparison with Other Modes

ModeBest ForApproval FrequencyUse Case
PerpetualOvernight buildsNeverFully automated CI/CD
CheckpointNovel projectsEvery 10 tasksLearning new domain
SupervisedCritical systemsEvery taskProduction deployments

Metrics

Track checkpoint effectiveness:

{
  "checkpoint_id": "cp-2026-01-14-001",
  "tasks_completed": 10,
  "time_elapsed_minutes": 15,
  "approval_time_seconds": 45,
  "course_corrections": 0,
  "user_satisfaction": "approved_without_changes"
}

Storage: .loki/metrics/checkpoint-mode/


References


Version: 1.0.0

When not to use it

  • Fully automated CI/CD pipelines

Limitations

  • Requires manual approval to resume work

How it compares

It enforces a feedback loop for human oversight rather than allowing perpetual autonomous execution.

Compared to similar skills

checkpoint-mode side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
checkpoint-mode (this skill)17moReviewBeginner
using-superpowers953moNo flagsBeginner
ultrawork112moNo flagsAdvanced
clawhub252moReviewIntermediate

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Example prompts that trigger this skill in your AI assistant.

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