memory
The memory CLI provides a durable knowledge layer for AI DevKit users to store and retrieve verified project-specific information. Use it to search for established conventions, reusable fixes, and tested patterns before starting implementation or debugging work, then store only high-confidence knowledge that future sessions will likely need, excluding transcripts, secrets, and temporary task progress.
git clone --depth 1 https://github.com/codeaholicguy/ai-devkit /tmp/memory && cp -r /tmp/memory/skills/memory ~/.claude/skills/memorySKILL.md
# AI DevKit Memory CLI Use `npx ai-devkit@latest memory ...` as the durable knowledge layer. ## Workflow 1. For implementation, debugging, review, planning, or documentation tasks, search before deep work unless the task is trivial: ```bash npx ai-devkit@latest memory search --query "<task, subsystem, error, or convention>" --limit 5 ``` For broad or risky tasks, search multiple angles: subsystem, error text, framework, command, and task intent. 2. Use results as context: - Trust repo files, tests, fresh command output, and explicit user instructions over memory. - If memory conflicts with verified evidence, use the evidence and update the stale memory. - Mention memory only when it changes the plan or avoids asking the user again. 3. Search before storing: ```bash npx ai-devkit@latest memory search --query "<knowledge to store>" --table ``` 4. Store or update only after the quality gate passes. ## Quality Gate Before storing, all must be true: - Future sessions are likely to reuse it. - It is verified by code, docs, tests, command output, or explicit user instruction. - It is not merely a restatement of obvious nearby files unless it prevents repeated agent mistakes. - It is scoped narrowly enough. - Existing memory does not already cover it. - It contains no secrets, credentials, private customer data, personal data, raw logs, or temporary paths. Store: - Project conventions, user preferences, durable decisions. - Reusable fixes, testing patterns, commands, setup gotchas. - Non-obvious constraints, architecture rules, failure patterns. Do not store: - Task progress, transcripts, speculation, generic programming facts. - Raw errors without diagnosis. - Anything the user did not intend to persist. ## Commands ### Search ```bash npx ai-devkit@latest memory search \ --query "<query>" \ --tags "<tags>" \ --scope "<scope>" \ --limit 5 ``` Use `--table` to get IDs for updates: ```bash npx ai-devkit@latest memory search --query "<query>" --table ``` Options: `--query/-q` required; `--tags`; `--scope/-s`; `--limit/-l` from 1-20; `--table`. ### Store ```bash npx ai-devkit@latest memory store \ --title "<actionable title, 10-100 chars>" \ --content "<context, guidance, evidence, exceptions>" \ --tags "<lowercase,tags>" \ --scope "<global|project:name|repo:org/repo>" ``` Use this content shape when helpful: ```text Context: Where this applies. Guidance: What to do. Evidence: File, command, test, or user instruction. Exceptions: When not to apply it. ``` ### Update Find the ID with `search --table`, then update only changed fields: ```bash npx ai-devkit@latest memory update \ --id "<memory-id>" \ --title "<updated title>" \ --content "<updated content>" \ --tags "<replacement,tags>" \ --scope "<updated scope>" ``` `--tags` replaces all existing tags. ## Scoping Use the narrowest useful scope: - `repo:<org/repo>` for one repository. - `project:<name>` for one app, product, or workspace. - `global` only for knowledge that applies across unrelated projects. If unsure, use a narrower scope. ## Troubleshooting - CLI missing: run `npx ai-devkit@latest --version`. - Duplicate title: search, then update the existing item if it is the same knowledge. - Empty results: broaden terms, remove filters, or search symptoms and subsystem names separately. - Validation error: check title/content lengths, query length, and `--limit` range. - DB path: default is `~/.ai-devkit/memory.db`; project config can override it automatically.
AI DevKit · Exchange information with active Codex, Claude Code, and other AI agents using ai-devkit agent list, detail, and send. Use when an agent needs to find another active agent, read its recent context, send it information, or request information back.
AI DevKit · Manage running AI agents with ai-devkit agent commands. Use when an agent needs to identify itself, list agents, start workers, inspect agent detail, assign work, group agents, resume sessions, stop agents, or delegate work to other agents.
AI DevKit · Supervise multi-agent workflows over repeated passes: poll progress, unblock waiting agents, coordinate dependencies, relay outputs, resolve conflicts, and verify completion. Use only for ongoing multi-agent coordination, not one-off list/detail/send/start/kill actions.
AI DevKit · Use when the user asks to brainstorm, ideate, generate ideas, expand options, challenge ideas, pressure-test ideas, compare concepts, narrow choices, name something, plan content angles, explore strategy, evaluate product ideas, technical approaches, experiments, or decisions.
AI DevKit · Compare implementation with design and requirements docs to ensure alignment.
AI DevKit · Pre-push code review against design docs.
AI DevKit · Safe git commit workflow for AI coding agents. Use when the user asks to commit, prepare a commit, stage changes, create a PR-ready checkpoint, or finish work with a conventional commit while avoiding unrelated user changes.
AI DevKit · Design phase guidance for reviewing feature design against requirements. Use when the user wants to validate architecture, review design docs, resolve design trade-offs, or run dev-lifecycle phase 3.