Rohit Ghumare

Review Orchard deployment manifests, the files describing deployment settings, and keep source paths in the result. The workflow stops when evidence is missing, tying review findings to the available source material.

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• Reviewing Orchard deployment manifests
• Keeping source paths in review results
• Stopping a review when evidence is missing

Prepare for the MCPA certification through interactive lessons, local Python labs, diagnostic assessments, and mock exams. Track progress, revisit weak domains, and resume learning, with guided support for learners who do not write code.

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• Starting an MCPA study plan
• Resuming the next unfinished lesson
• Learning through guided no-code labs

Study AI engineering through interactive terminal lessons with explanations, coding exercises, and quizzes. The tutor follows your recorded progress, revisits previous material, adapts to your answers, and logs results and topics that need review.

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• Resuming lessons from recorded progress
• Learning concepts through coding exercises
• Checking understanding with lesson quizzes

Find and delete selected memories stored in agentmemory after showing the matches and receiving explicit confirmation. It checks deletion results and distinguishes stored memories from lessons, observations, and complete session records.

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• Removing outdated stored memories
• Deleting selected memories containing private information
• Reviewing matching memories before deletion

Trace a file, function, or line through Git history to the agent session linked to its commit. It presents recorded commit details, session summaries, and important observations to help explain the change.

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• Investigating why a particular line changed
• Finding the agent session behind a function change
• Checking a file's latest commit and author

Guides HTTP integration with the agentmemory server for saving and searching knowledge. It explains authentication, request conventions, and endpoint references, including how to connect systems that do not support MCP, the agent tool connection protocol.

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• Saving and searching knowledge over HTTP
• Connecting a host that does not support MCP
• Checking authentication and response conventions

Explain how agentmemory stores and retrieves coding agents’ memories, including its engine, search methods, memory lifecycle, ports, and web viewer. Useful for understanding the system or planning extensions to its functions.

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• Understand how agent memories are stored and retrieved
• Plan extensions to agentmemory
• Check memory capture in the web viewer

Understand how agentmemory automatically records tool activity, prompts, and session boundaries through hooks, actions triggered by session events. Use it to check integration setup, investigate missing records, and distinguish capture from optional summaries and context injection.

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• Understanding automatic session recording
• Investigating missing observations
• Checking capture and summary settings

Summarize recent agent sessions for the current project, grouped by date with highlights from recorded observations. Review today, the last seven days, or a chosen number of sessions, with totals for sessions, days, and observations.

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• Review today's agent work
• Recap project activity from the last seven days
• Summarize a chosen number of recent sessions

Shows recent work sessions for the current project as a timeline, newest first. It includes session identifiers, start times, status, recorded observation counts, and available highlights or decisions, using only history returned by the memory tool.

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• Checking what happened in the previous session
• Reviewing recent work in one project
• Finding decisions recorded in past sessions

List Git commits linked to agent work sessions, with branch or repository filters. See each commit’s message, date, linked sessions, and observation counts to review recorded changes alongside their session context.

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• Review recent commits linked to agent sessions
• Filter linked commits by branch
• Review linked commits for a specific repository

Resume work using the latest recorded session matching the current project directory. The skill surfaces unanswered questions first, summarizes relevant decisions and files, and provides a concrete next step while checking that records belong to the selected project.

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• Resuming work interrupted in a previous session
• Checking an unanswered question before continuing
• Reviewing key decisions and related files

Set up and troubleshoot a local agentmemory server by checking MCP connections, authentication, ports, and environment settings. Understand which optional memory features require additional configuration, model downloads, or token usage before enabling them.

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• Set up a local agentmemory connection
• Diagnose MCP connection failures
• Review authentication and custom ports

Guide an agent through searching past decisions before work, saving new decisions with their reasons, and treating corrections as lessons. The workflow helps distinguish useful long-term knowledge from temporary details, code facts, and routine activity already captured by hooks.

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• Checking earlier decisions before starting a task
• Saving a resolved issue with its underlying reason
• Preserving lessons from corrections

Turns user corrections and recurring mistakes into actionable rules with confidence scores. It records when each rule applies and why it matters, so relevant lessons can be retrieved before similar work begins.

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• Recording a rule after a user correction
• Saving a lesson from a recurring mistake
• Keeping rules specific to one project

Create, update, or review agentmemory skills using a shared format. It guides document structure, supporting files, and generated reference data so contributions follow consistent rules and stay aligned with the source code.

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• Creating a new agentmemory skill
• Restructuring an existing skill
• Reviewing skill contributions for consistency

Connect agentmemory to coding agents such as Claude Code and Codex while preserving existing server settings. It guides configuration, host restarts, and checks that memory tools are available and can save and retrieve information.

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• Connecting agentmemory to a coding agent
• Checking supported agents and connection methods
• Investigating an incorrect configuration path

Retrieve past work, decisions, and lessons from agentmemory using keyword, meaning, and relationship searches. It expands selected records, groups findings by session, and prioritizes important observations so earlier context can inform the current task.

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• Recalling an earlier decision and its context
• Finding how a similar issue was handled
• Reviewing one project's or agent's history

Save facts, decisions, and discoveries to agentmemory for future sessions. The skill preserves the user's wording, adds specific search tags and referenced file paths, and confirms which terms can retrieve the saved knowledge.

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• Preserving a decision for future sessions
• Saving a discovery with related file paths
• Updating a previously saved fact

Helps agents choose the right agentmemory tool through MCP, a protocol for connecting agents to tools. It explains tool roles and points to parameter references for saving, retrieving, organizing, and maintaining stored knowledge.

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• Choosing a tool to save or retrieve knowledge
• Checking tool names and required parameters
• Reviewing available memory management operations

Apply interface craft when building or reviewing UI - motion, easing, timing, springs, component feel, and visual foundations. Use when building a component, animation, transition, hover or press state, modal, drawer, toast, or when polishing an interface so it feels right. Says "make this feel better", "add an animation", "polish the UI", "review this component".

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Configure file watching hooks to auto-react to config changes, env file updates, and dependency modifications. Use to set up reactive workflows.

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Generate a structured handoff document capturing current progress, open tasks, key decisions, and context needed to resume work. Use when ending a session, saying "continue later", "save progress", "session summary", or "pick up where I left off".

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Auto-grow a pro-workflow wiki by running a budget-capped BFS research loop over pluggable source fetchers (web, arXiv, GitHub). Each iteration pops a seed from the queue, fetches sources, drafts a wiki page, dedupes claims against existing pages, enqueues follow-up seeds. Halts on budget cap, depth cap, or convergence. Use when the user says "research <topic>", "grow the <slug> wiki", "auto-research", or wants a knowledge base that builds itself overnight.

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