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 · Compare implementation with design and requirements docs to ensure alignment.
AI DevKit · Pre-push code review against design docs.
AI DevKit · Execute a feature plan task by task.
AI DevKit · Scaffold feature documentation from requirements through planning.
AI DevKit · Store reusable guidance in the knowledge memory service.
AI DevKit · Review feature design for completeness.
AI DevKit · Review feature requirements for completeness.
AI DevKit · Update planning docs to reflect implementation progress.