Capture

Skill itsjwill/claude-memory/skills/capture

Persistent memory for Claude Code. Auto-capture decisions, learnings, client info. Cloud backup to Supabase. Never lose context again. Free alternative to Mem.ai, Rewind AI, Personal AI.

Install

npx -y skills add itsjwill/claude-memory --skill capture

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

14 stars14 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Save important context to persistent memory for future sessions. PROACTIVELY USE THIS when you detect: - Decisions: "let's go with", "decided to", "chose", "went with" - Learnings: "I learned", "turns out", "TIL", "realized", "discovered" - Errors resolved: "fixed", "the problem was", "solution was" - Client info: names, emails, phone numbers, addresses, invoice amounts - Preferences: "I prefer", "I like", "don't like", "always use" - References: API keys locations, file paths, URLs, credentials - Patterns: "pattern", "convention", "always do", "standard approach" - Important commands run worth remembering Do NOT ask permission - just capture silently when these patterns appear.

SKILL.md

5.8 KB, as published. Nobody here has run it

Memory Capture

Save important context to persistent memory using the MCP memory-service.

How to Use

Manual Invocation

/capture "Pinnacle Title invoice is $1,500/month"
/capture "Billy V4 LONGS ONLY - shorts lost $2,226 in backtests" --type decision
/capture "API key stored in /root/.env" --type reference --tags trading,credentials

Automatic Invocation

When you detect important context in conversation (decisions, learnings, client info, etc.), call this skill SILENTLY without asking the user. Just capture and continue.

Arguments

$ARGUMENTS or $0: The content to capture
--type TYPE: Memory type (decision, pattern, learning, preference, client, gotcha, reference)
--tags TAG1,TAG2: Comma-separated tags for categorization

Memory Types

Type	Use When
`decision`	Architectural/technical choices made
`pattern`	Reusable code/workflow patterns discovered
`learning`	New knowledge or insights
`preference`	User preferences and likes/dislikes
`client`	Client names, contacts, business info
`gotcha`	Pitfalls, bugs, things to avoid
`reference`	File paths, API locations, credentials locations

Execution Steps

Parse the input: Extract content, type, and tags from arguments
Auto-classify: Infer type from content if not provided
Check for duplicates: Search existing memories - if similar exists, it auto-merges
Store the memory: Use memory_store with metadata (type, tags, timestamp)
Silent operation: Do NOT notify user - just capture and continue

Capture Philosophy: REMEMBER EVERYTHING

No filtering. No threshold. Capture aggressively.

When in doubt, capture it. Storage is cheap, lost context is expensive.

The semantic deduplication will handle noise - similar memories get merged automatically. Quality ratings will surface the good stuff over time.

Capture triggers (if ANY match, capture it):

Decisions (even tentative ones)
Learnings (even small ones)
Names, numbers, dates, amounts
File paths, URLs, API references
Preferences (even implied ones)
Errors and how they were fixed
Patterns noticed
Questions asked (context for why we explored something)

The only things to skip:

Pure greetings ("hi", "thanks")
Confirmations ("ok", "got it", "sure")
Meta-discussion about the conversation itself

Auto-Classification Rules

If --type not provided, detect from content:

Contains "decided", "chose", "going with" → decision
Contains "learned", "realized", "discovered" → learning
Contains "API", "key", "path", "credentials", ".env" → reference
Contains "always", "never", "convention", "pattern" → pattern
Contains "careful", "watch out", "gotcha", "bug" → gotcha
Contains email, phone, "$", "invoice", company name → client
Default → learning

Auto-Tagging Rules

Extract tags from:

Project names mentioned (botsniper, foodshot, etc.)
Technology names (python, node, react, etc.)
Client names (pinnacle, etc.)
Domain terms (trading, invoice, api, etc.)

Storage Format

Store using mcp__memory-service__memory_store with:

{
  "content": "<the memory content>",
  "metadata": {
    "type": "<memory type>",
    "tags": "<comma-separated tags>",
    "source": "capture-skill",
    "timestamp": "<ISO timestamp>",
    "project": "<current working directory if relevant>"
  }
}

Example Execution

User says: "The Airtable API token for Pinnacle is stored in Voltaris-Labs/.env"

Auto-capture (silent):

Detect: Contains "API", "token", ".env" → type: reference
Detect: Contains "Pinnacle", "Airtable" → tags: pinnacle,airtable,credentials

Store:

content: "Airtable API token for Pinnacle is stored in Voltaris-Labs/.env"
metadata: {type: "reference", tags: "pinnacle,airtable,credentials,api"}

Continue conversation without mentioning the capture

Deduplication

Before storing, search for similar memories:

memory_search(query="<content summary>", limit=3)

If highly similar memory exists (same topic):

Update existing memory quality score instead of creating duplicate
Use memory_update to add new tags if relevant

Quality Feedback

The memory system learns from feedback. When you notice a memory was:

Useful (helped with a task):

mcp__memory-service__memory_quality(action="rate", content_hash="<hash>", rating="1", feedback="Helped with X")

Not useful (irrelevant or wrong):

mcp__memory-service__memory_quality(action="rate", content_hash="<hash>", rating="-1", feedback="Was outdated/wrong")

Quality scores affect search ranking - highly-rated memories appear first.

Integration with MEMORY.md

For HIGH importance memories (client info, critical decisions), also append to MEMORY.md:

Location: ~/.claude/projects/*/memory/MEMORY.md
Format: Brief one-liner under appropriate section
Only for memories that should be instantly visible at session start