A knowledge management tool that saves session findings and decisions, allowing future sessions to start with full context.
Install
mkdir -p .claude/skills/skills-jackccrawford && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/15420" && unzip -o skill.zip -d .claude/skills/skills-jackccrawford && rm skill.zipInstalls to .claude/skills/skills-jackccrawford
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Your next session starts cold. No memory of what you built, what broke, what you decided. Every memory you write is a gift to that future session. The richer the memory, the less time re-learning.Key capabilities
- →Save session memories to a SQLite database
- →Recall memories by meaning, not just keywords
- →Tag memories with gists for quick identification
- →Thread follow-up memories to existing ones
- →Bulk-load existing files as memories
- →Build and manage a local BERT model for semantic search
How it works
Memories are stored in a SQLite database, and semantic search is performed using a local BERT model that finds memories by meaning rather than keywords.
Inputs & outputs
When to use skills
- →Save session notes
- →Recall past debugging findings
- →Document architectural decisions
- →Maintain technical history
About this skill
geniuz
Your next session starts cold. No memory of what you built, what broke, what you decided. Every memory you write is a gift to that future session. The richer the memory, the less time re-learning.
Works with any framework — Claude Code, Cursor, Windsurf, Aider, or anything with shell access.
You (the agent reading this) use the CLI commands below. geniuz remember writes; geniuz recall reads. There's also a terminal UI (geniuz tui) and a graphical dashboard — those are for your human user to browse memories, not for you to call. The TUI refuses to launch from non-interactive callers and exits with code 2; calling it from a subprocess locks nothing up.
Why this matters
A memory that says "fixed auth bug" saves your future self zero time.
A memory that says what broke, why it broke, and how you fixed it saves an hour.
Memories compound. A single memory is a note. A folder of memories is institutional knowledge. Write for the version of you that knows nothing.
Commands
# Save what you learned — pipe in for depth, inline for quick notes
echo "Token validation was running before refresh in auth.rs.
Swapped lines 42-47. Root cause: middleware ordering assumed
sync validation, but OAuth refresh is async. Three edge cases
tested: expired token, revoked token, concurrent refresh." \
| geniuz remember -c - -g "fix: auth token refresh — async ordering in middleware"
# Quick memory when depth isn't needed
geniuz remember -c "Upgraded rusqlite to 0.32" -g "dep: rusqlite 0.32"
# Remember from a file
geniuz remember -c @session-notes.md -g "session: March 19 architecture review"
# Thread a follow-up
geniuz remember -c "Same fix needed in staging compose" -g "fix: staging auth ordering" -p A1B2C3D4
# Search by meaning — not keywords
geniuz recall "authentication middleware"
geniuz recall "what broke in production last week"
# Keyword fallback
geniuz recall --keyword "auth"
# Recent memories
geniuz recent
geniuz recent -l 5
# Full content (not just gist)
geniuz recall --full "auth"
# Discover something you forgot you knew
geniuz recall --random
# Bulk-load existing files
geniuz capture ./docs/
geniuz capture --openclaw
# Build embedding cache (once, then automatic)
geniuz backfill
# Folder stats
geniuz status
Shared folders
Multiple agents can write to the same folder. What one agent learns, every agent finds.
GENIUZ_STATION=/shared/team.db geniuz remember -c "Deploy complete" -g "ops: deploy v2.1"
GENIUZ_STATION=/shared/team.db geniuz recall "deploy"
When to remember
- After solving something hard. Your next session will hit the same problem. Give it the answer.
- Before your session ends. Everything in your context window dies with the session. Your folder doesn't.
- When you discover something non-obvious. The small facts — which config flag, which line number, which edge case — are the ones that save the most time.
When to recall
- Starting a new task. You may have already solved part of it last week.
- When stuck. The answer might be in a memory from three sessions ago.
- After compaction. Your context was compressed. Your folder wasn't.
Note:
geniuz recall helpprints recall's help instead of searching for the literal word "help". To find memories about that word, use a phrase containing it — e.g.geniuz recall "help with debugging"orgeniuz recall "how to help".
Writing good memories
The gist is how your future self finds this memory. The content is what makes it useful when found.
Gist: compress the insight. "fix: auth token refresh — async ordering in middleware" — category, what, why, where.
Content: make it self-contained. If your future self reads only this memory — no session history, no surrounding context — can they understand what happened and act on it?
A memory doesn't need to be long. It needs to be complete.
How it works
Memories live in a SQLite database. Semantic search uses a local BERT model — no API calls, no cloud, runs fully offline. The model downloads once (~118MB) on first search. Every memory after that is embedded automatically.
Search finds memories by meaning, not keywords. "Authentication middleware" matches a memory about "token validation ordering" because the concepts overlap.
When not to use it
- →When the user needs a real-time, in-context memory that dies with the session
- →When the task does not involve saving or recalling past findings
- →When the user prefers a graphical dashboard over CLI commands for memory management
Limitations
- →The terminal UI refuses to launch from non-interactive callers
- →The BERT model downloads once (~118MB) on first search
- →GENIUZ_STATION environment variable is used for shared folders
How it compares
This skill provides a persistent, semantic memory system that operates offline and across sessions, allowing recall by meaning, unlike temporary context windows or keyword-only search.
Compared to similar skills
skills side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| skills (this skill) | 0 | 2mo | Review | Beginner |
| knowledge-ops | 0 | 4mo | No flags | Advanced |
| knowledge-base | 1 | 6mo | Review | Beginner |
| notion-knowledge-capture | 10 | 9mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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