recall

Search Entire history for prior sessions and generate structured playbooks.

208|14|Updated Apr 10, 2026
One-click install
npx skills add https://github.com/entireio/skills --skill recall-entireio
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: recall
Source: https://github.com/entireio/skills/tree/main/skills/recall
Command: npx skills add https://github.com/entireio/skills --skill recall-entireio

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Users often waste time re-solving problems their team has already tackled, or struggle to find relevant past work to inform new development tasks, leading to repeated mistakes and inconsistent implementations.

Core Features & Use Cases

  • Precedent Retrieval: Searches Entire history for prior sessions matching your current task, filtered by topic, recency, and relevance.
  • Playbook Generation: Distills the closest matching session into a structured playbook with what worked, common gotchas, and concrete next steps.
  • Use Case: If you are about to implement a new user permission system, use this skill to find how your team built a similar system six months ago, including edge cases and files to start with.

Quick Start

Ask your agent to recall how your team implemented the previous API rate limiting feature to use as a template for your new integration work.

Frequently Asked Questions about recall

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find prior development sessions to reuse past implementation approaches?

Retrieving prior development sessions requires searching historical checkpoints to find relevant precedent for new tasks, eliminating redundant problem-solving by identifying past implementation approaches and avoiding repeated mistakes.

Do I need a specific CLI to search historical code context for task playbooks?

Generating task playbooks from historical code context requires the Entire CLI to search and explain prior development checkpoints, which synthesizes matched development precedent into a structured actionable playbook.

What is the best way to avoid repeated mistakes when implementing a feature similar to past work?

Avoiding repeated mistakes involves searching past development work to retrieve proven workflows, identifying what worked and common gotchas, then generating a structured playbook with concrete next steps for your current task.

Can I generate a development playbook from past work filtered by task relevance and recency?

Playbook generation from past work filters historical development sessions by topic, recency, and relevance, distilling the closest matching session into a structured playbook with what worked, common gotchas, and concrete next steps.

How does checkpoint search work for finding development precedent?

Checkpoint search works by querying Entire history for prior sessions matching your current task, retrieving relevant development precedent to synthesize results into a structured actionable playbook for workflow reuse.

What limitations exist when reusing proven workflows from historical development sessions?

Reusing proven workflows from historical development sessions depends on the availability and relevance of prior checkpoints in Entire history, meaning playbook quality is constrained by how well past sessions match your current task topic.