WhispAssist/.claude/skills/memanto/SKILL.md

name, description

name	description
memanto-memory	Use this skill when you need to store or search MEMANTO persistent memories. It defines mandatory guidelines for best practices, memory types, confidence levels, tagging, and patterns for effective agent memory usage.

MEMANTO Memory Skill

Detailed reference for using MEMANTO persistent memory effectively.

Memory Types: Decision Matrix

Type	When to Use	Confidence	Example
fact	Verified information, project status	0.9-1.0	"MEMANTO uses PostgreSQL for metadata"
decision	Architecture choices, approach selections	0.9-1.0	"Chose React over Vue for frontend"
instruction	Standing rules, preferences, guidelines	0.9-1.0	"Always use type hints in Python"
commitment	Promises, TODOs, obligations	1.0	"Will deploy monitoring by Friday"
preference	User/team preferences	0.8-1.0	"User prefers dark mode"
goal	Objectives, targets, milestones	0.8-1.0	"Launch CLI by end of March"
artifact	Tool outputs, reports, file locations	0.9-1.0	"Report saved at ./reports/q1.md"
learning	Knowledge acquired from experience	0.7-0.9	"Batch operations 100x faster"
event	Important conversations, milestones	0.8-0.95	"Completed Phase 1 features"
relationship	Team context, collaboration patterns	0.85-0.95	"Alice is lead backend engineer"
observation	Patterns noticed, behaviors	0.6-0.85	"User prefers short responses"
error	Failures, bugs, lessons learned	0.95-1.0	"Namespace format bug - use underscores"
context	Session summaries, status updates	0.9-1.0	"Project 70% done, API complete"

Confidence Levels

• 1.0 — Explicit user statement, verified fact, standing instruction
• 0.9-0.95 — Strong consensus, well-tested approach, clear team preference
• 0.8-0.85 — Observed pattern (3+ times), indirect but supported preference
• 0.7-0.75 — Emerging pattern (2 times), reasonable inference
• 0.6-0.65 — Single observation, uncertain interpretation
• < 0.6 — Don't store. Too uncertain.

Provenance Types

Always categorize the source of the memory. Valid options:

• explicit_statement — Directly stated by user
• inferred — Derived from behavior/context
• observed — Seen in action
• corrected — Updated after contradiction
• validated — Confirmed/verified
• imported — Brought in from an external source (file upload, sync, migration)

Source Types

Always specify the tool or agent creating the memory.

• For AI agents: Use the agent name (e.g., --source claude_code or --source cursor)
• Valid base sources (if not using specific agent name): user, agent, tool, system

Tagging Best Practices

Use 2-5 tags per memory. Tags make memories findable.

Good: --tags "authentication,oauth,security" Good: --tags "bug-fix,namespace,commit-3f39351" Bad: --tags "important" (too generic) Bad: --tags "thing" (not descriptive)

Conventions:

• Lowercase with hyphens: bug-fix not BugFix
• Be specific: authentication-oauth not auth
• Include refs: commit-abc123 for git references

Patterns

Session Start

# recall — load raw context (instructions, decisions, goals) to guide this session
memanto recall "instructions decisions goals" --limit 20

# answer — get a direct synthesized summary of pending commitments
memanto answer "What are my pending commitments?"

After Important Work

memanto remember "Implemented X using approach Y because Z. Commit abc123." --type decision --tags "feature-x" --confidence 0.95 --provenance "inferred" --source "claude_code"
memanto remember "Learned that batch ops reduce API calls 100x." --type learning --tags "performance" --confidence 0.85 --provenance "observed" --source "claude_code"

When User Corrects You

memanto remember "User corrected: prefer pytest over unittest." --type learning --tags "correction,testing" --confidence 1.0 --provenance "corrected" --source "claude_code"

Choosing Between recall and answer

These are equal-priority tools. Pick the right one — do NOT always default to recall.

Situation	Use
Need raw memory chunks to read and apply as context	recall
Need a direct synthesized answer to give (or act on)	answer
Building context before a complex multi-step task	recall
User asks "what did we decide / prefer / commit to?"	answer
Comparing multiple matching memories	recall
Need one grounded yes/no or summary response	answer

Decision rule: If your next step is "read these memories and act" → recall. If your next step is "answer this question directly" → answer. Both save tokens equally — answer synthesizes so you don't have to.

# Use recall — need raw context to work from
memanto recall "authentication approach" --limit 10

# Use answer — need a direct synthesized answer
memanto answer "What auth approach did we decide on and why?"

Pitfalls to Avoid

1. Memory hoarding — Ask "Will this matter in a week?" before storing
2. Vague content — Bad: "better performance" → Good: "API response < 200ms"
3. No context — Bad: "fixed bug" → Good: "Fixed OAuth expiry bug. Commit abc123."
4. Duplicates — Search first (memanto recall), then store if not found
5. Missing tags — Always include tags for retrieval

recall vs answer: Choose the Right Tool

Equal priority — do NOT always default to recall. Pick based on what you need next:

Use recall when...	Use answer when...
You need raw memory chunks as context	You need one direct synthesized response
Building context before a complex task	User asks "what did we decide / prefer?"
Comparing or reviewing multiple memories	Getting a grounded summary or yes/no
Next step: read these and act on them	Next step: deliver this as the answer

Short rule: need context to work from → recall. Need a ready answer → answer. Both save the agent tokens and time — answer synthesizes so you don't have to read and merge manually.

Command Reference

# Store memory
memanto remember "content" --type TYPE --tags "tag1,tag2" --confidence 0.9 --provenance "inferred" --source "claude_code"

# Raw memory search (use for context-building, multi-step tasks)
memanto recall "query" --limit 10 --type TYPE --min-similarity 0.8

# Temporal recall variants (no query needed)
memanto recall --recent --limit 10                 # newest first
memanto recall --as-of "2026-01-15"                # state at a point in time
memanto recall --changed-since "last 7 days"       # what changed since

# Synthesized answer (use for direct questions, "what did we decide about X?")
memanto answer "question"

# Sync memories to project
memanto memory sync --project-dir .