Agent skill
memory
Use AI DevKit memory via CLI commands. Search before non-trivial work, store verified reusable knowledge, update stale entries, and avoid saving transcripts, secrets, or one-off task progress.
Install this agent skill to your Project
npx add-skill https://github.com/codeaholicguy/ai-devkit/tree/main/skills/memory
SKILL.md
AI DevKit Memory CLI
Use npx ai-devkit@latest memory ... as the durable knowledge layer.
Workflow
-
For implementation, debugging, review, planning, or documentation tasks, search before deep work unless the task is trivial:
bashnpx ai-devkit@latest memory search --query "<task, subsystem, error, or convention>" --limit 5For broad or risky tasks, search multiple angles: subsystem, error text, framework, command, and task intent.
-
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.
-
Search before storing:
bashnpx ai-devkit@latest memory search --query "<knowledge to store>" --table -
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
npx ai-devkit@latest memory search \
--query "<query>" \
--tags "<tags>" \
--scope "<scope>" \
--limit 5
Use --table to get IDs for updates:
npx ai-devkit@latest memory search --query "<query>" --table
Options: --query/-q required; --tags; --scope/-s; --limit/-l from 1-20; --table.
Store
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:
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:
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.globalonly 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
--limitrange. - DB path: default is
~/.ai-devkit/memory.db; project config can override it automatically.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
simplify-implementation
Analyze and simplify existing implementations to reduce complexity, improve maintainability, and enhance scalability. Use when users ask to simplify code, reduce complexity, refactor for readability, clean up implementations, improve maintainability, reduce technical debt, or make code easier to understand.
dev-lifecycle
Structured SDLC workflow with 8 phases — requirements, design review, planning, implementation, testing, and code review. Use when the user wants to build a feature end-to-end, or run any individual phase (new requirement, review requirements, review design, execute plan, update planning, check implementation, write tests, code review).
tdd
Test-driven development — write a failing test before writing production code. Use when implementing new functionality, adding behavior, or fixing bugs during active development.
debug
Guide structured debugging before code changes by clarifying expected behavior, reproducing issues, identifying likely root causes, and agreeing on a fix plan with validation steps. Use when users ask to debug bugs, investigate regressions, triage incidents, diagnose failing behavior, handle failing tests, analyze production incidents, investigate error spikes, or run root cause analysis (RCA).
technical-writer
Review and improve documentation for novice users. Use when users ask to review docs, improve documentation, audit README files, evaluate API docs, review guides, or improve technical writing.
verify
Enforce evidence-based completion claims — require fresh command output before reporting success. Use when completing any task, fixing a bug, finishing a phase, running tests, building, deploying, or making any "it works" claim.
Didn't find tool you were looking for?