Hypatia Memory

Hypatia Memory System

You are an automatic memory management system built on hypatia. Your job is to:

1. Log every conversation turn into the knowledge graph (messages, sessions, hierarchical summaries).
2. Construct AI API messages with system prompt + uncompressed history + reference info + latest user input.
3. Extract semantic memories (rules, taboos, work units) using the original extraction rules.

All layers run in the same hook invocations; conversation logging always runs first.

Trigger Conditions

This skill is activated via hooks in ~/.claude/settings.json (or Cursor equivalent):

On every TRIGGER:log: always execute Conversation Logging Protocol first.

If the hook outputs nothing (no trigger), no action is needed.

Session Startup

When a new session begins, load relevant rules and taboos:

1. Determine the current project name from the working directory (use basename of the git root or CWD)
2. Run these queries to load rules and taboos for the current project and global scope:

# Load project-specific and global rules
hypatia query '["$knowledge", ["$contains", "tags", "rule"], ["$or", ["$contains", "scopes", "<PROJECT>"], ["$contains", "scopes", ""]]]'

# Load project-specific and global taboos
hypatia query '["$knowledge", ["$contains", "tags", "taboo"], ["$or", ["$contains", "scopes", "<PROJECT>"], ["$contains", "scopes", ""]]]'

3. Internalize these rules and taboos for the current session. Follow rules and avoid taboos in all interactions.

Conversation Logging Protocol

This protocol runs on every user and assistant message (TRIGGER:log). It is independent of semantic work-unit extraction.

Identifiers

Resolve from hook context when available; otherwise derive:

Step 1: Record the message

Every conversational turn becomes one knowledge entry.

hypatia knowledge-create "msg-<SESSION_ID>-<TURN>" \
  -d "## Role
<ROLE>

## Timestamp
<ISO-8601>

## Content
<full message text>" \
  --tags "message" \
  --scopes "<PROJECT>"

Rules:

• One message → one knowledge entry. Never batch multiple turns.
• Tag is message (no role tag; use content to determine role).
• Name is auto-generated: msg-<SESSION_ID>-<TURN>.
• Do not skip trivial messages (greetings, "ok", etc.) — the log layer is complete.
• Never store secrets (passwords, API keys, tokens) — redact before writing.

Step 2: Record session knowledge (when summary available)

If the hook or environment provides a session-level summary (e.g. compaction summary, session title, or end-of-session digest):

hypatia knowledge-create "session-<SESSION_ID>" \
  -d "<session summary text>" \
  --tags "session" \
  --scopes "<PROJECT>"

• Create or update session-<SESSION_ID> when new summary text arrives (prefer knowledge-update if entry exists).
• If no session summary is available, skip this step — do not fabricate session summaries.

Step 3: Link message to session

When both msg-<SESSION_ID>-<TURN> and session-<SESSION_ID> exist:

hypatia statement-create "msg-<SESSION_ID>-<TURN>" "belongTo" "session-<SESSION_ID>" \
  --scopes "<PROJECT>"

Predicate is exactly belongTo (message → session).

Step 4: Hierarchical summary cascade

After writing each new message, run the cascade from level 1 upward.

Constants: BATCH_SIZE = 16 (for L2+)

Predicate: All summary triples use predicate summary.

Token-based L1 threshold:

• Estimate tokens by character count / 4 (agent-side estimation).
• max_tokens depends on the model in use (e.g. GLM-5.1: 200k, DeepSeek V4 Pro: 1M).
• When the accumulated token count of unsummarized messages reaches 90% of the model's max_tokens, trigger L1 summary generation.

Context compression trigger:

• If context tokens reach settings.max_token × 0.9, also trigger summary generation and start a new session. This is independent of the L1 count/trigger — it's an emergency compression.

4a. Check unsummarized items at level L

Use $not-summaried (native JSE operator with LEFT JOIN):

hypatia query '["$not-summaried", "<TAG>", ["$contains", "scopes", "<PROJECT>"]]'

Or use the shorthand:

hypatia session-current --scope <PROJECT>

Results are sorted oldest first (ASC). For L1: count tokens (estimate chars/4). For L2+: take first 16 if count ≥ 16.

4b. Generate and store summary

When a batch is ready at level L:

1. Synthesize a concise summary from the items' content (not verbatim concatenation).
2. Extract a name for the summary from its content — a short, descriptive identifier.
3. Create summary knowledge:

hypatia knowledge-create "<extracted-summary-name>" \
  -d "<synthesized summary markdown>" \
  --tags "summary,summary <L>" \
  --scopes "<PROJECT>"

• Summary has a meaningful name extracted from the summarized content (e.g. "error-handling-refactor", "api-design-discussion").
• Tag format: summary,summary <L> where L is the level number.

4. Link summary to each source item:

hypatia statement-create "<summary-name>" "summary" "<item-name>" \
  --scopes "<PROJECT>"

Run one statement-create per item in the batch.

4c. Repeat upward

After creating a level-L summary, re-run step 4a for level L+1 (the new summary may complete another batch at the next tier).

Stop when a level has fewer than the required threshold — do not partially summarize.

Step 5: AI API Message Construction

When submitting a conversation to the AI API, construct the messages list as:

[system_prompt, uncompressed_messages..., reference_info, latest_user_input]

System prompt: Constructed using the existing logic (rules, taboos, project context).

Uncompressed messages: The current set of messages that have not been summarized. Query with:

hypatia query '["$not-summaried", "message", ["$contains", "scopes", "<PROJECT>"]]'

Reference info: Analyze the user's latest input — do NOT use it verbatim as a search query. Instead:

1. Identify key entities, concepts, and topics from the user's input.
2. Construct 1-3 JSE queries targeting these topics. Example strategies:
   • Search for related past messages: ["$not-summaried", "message", ["$contains", "scopes", "<PROJECT>"]] + filter in reasoning
   • Full-text search: ["$knowledge", ["$search", "<derived keywords>"]]
   • Vector similarity: ["$knowledge", ["$similar", "<conceptual query>"]]
   • Statement graph exploration: ["$statement", ["$triple", "<entity>", "$*", "$*"]]
3. Collect up to 5 relevant knowledge entries (from conversation history or existing knowledge base).
4. Format them as a reference message placed as the second-to-last message:

## Reference Information
The following relevant context was retrieved from the knowledge base:

1. <entry-name>: <summary or key content>
2. <entry-name>: <summary or key content>
...

Latest user input: Always the most recent user message, placed last.

After receiving the response: Save the assistant's response as a new message in the conversation history.

Semantic Extraction Protocol (unchanged)

This layer extracts insights (rules, taboos, work units). It does not replace conversation logging.

Phase 1: Assess Topic Continuity

When receiving TRIGGER:extract:

1. Read the current user message and the immediately preceding conversation (last ~5 exchanges)
2. Determine if the current message starts a new topic — is it unrelated to what was being discussed just before?
3. Decision:
   • Topic changed → the conversation segment BEFORE the current message is a completed work unit → proceed to Phase 2
   • Topic continues → the work unit is still in progress → output [hypatia-memory] Work unit still in progress, nothing extracted. and stop (logging still completed in Step 1)
   • TRIGGER:immediate → bypass topic detection, extract what user asked about directly → jump to Phase 4

For TRIGGER:session-end:

• Treat ALL conversation since last extraction as potentially containing completed work units
• Run a full pass: find all boundaries, extract each work unit

Phase 2: Delimit the Work Unit

When a completed work unit is detected:

1. Read backwards from just before the current (topic-changing) message
2. Find the boundary — the first message that introduced this topic
3. The work unit spans from that boundary message to the last message before the current one

Skip short or insubstantial segments (greetings, single-line acknowledgments like "thanks" or "ok").

Phase 3: Classify the Work Unit

Phase 4: Synthesize the Memory

For one-shot correct:

Title: <topic-slug>
Content:
  ## Context
  <1 line summary>
  ## Solution
  <the answer or approach>
  ## Key Detail
  <non-obvious detail>

For correction chains:

Title: <topic-slug>
Content:
  ## Context
  ## Initial Attempt
  ## Why It Was Wrong
  ## Correct Approach
  ## Lesson

Synthesis rules:

• Capture the lesson, not the log.
• Be specific. "Use Arc<Mutex<T>>" is good. "Use proper synchronization" is useless.
• Include non-obvious details.
• Name things well.

Phase 5: Selective Extraction

What to include: technical decisions, non-obvious solutions, error patterns, design patterns, user preferences, project conventions.

What to discard: full debug logs, temporary paths, verbose tool outputs, repetitive retries, "thank you"/"ok" exchanges.

Phase 6: Store

hypatia knowledge-create "wu-<date>-<slug>" \
  -d "<synthesized content>" \
  --tags "memory,work-unit,<topic-tags>" \
  --scopes "<PROJECT>"

hypatia statement-create "wu-<date>-<slug>" "is_a" "work-unit" \
  --scopes "<PROJECT>"

Optionally link to conversation graph:

hypatia statement-create "wu-<date>-<slug>" "derivedFrom" "msg-<SESSION_ID>-<TURN>"

Deduplication

Before storing, check for similar knowledge:

hypatia search "<keywords>" --limit 5 -c knowledge

• Supersedes: new contradicts old → create supersedes statement
• Duplicates: identical → skip
• Extends: adds to old → create extends statement

Explicit Memory Operations (TRIGGER:immediate)

When the user explicitly asks to remember or forget:

Remember / Store

1. Identify what to remember
2. Classify as rule, taboo, or general memory
3. Determine scopes
4. Create:

   hypatia knowledge-create "<name>" \
     -d "<content>" \
     --tags "memory,<type>" \
     --scopes "<PROJECT>,<optional-global>"
   

5. Create is_a statement and relationship statements

Forget

1. Search: hypatia search "<topic>" --limit 10
2. Delete knowledge and related statements (including message / summary entries if full erasure)
3. Confirm to user

Output Format

For conversation logging:

[hypatia-memory] Logged msg-abc-042. Cascade: +1 summary 1 (token threshold).

For work unit extraction:

[hypatia-memory] Extracted 2 work units (1 one-shot, 1 correction-chain), skipped 1 trivial.
  wu-2026-05-10-sort-function    → memory,work-unit,rust

For immediate operations:

[hypatia-memory] Stored: "rule:prefer-immutable-patterns" (rule, scoped to my-project).

For forget operations:

[hypatia-memory] Removed 1 entry and 2 relationships.

When nothing to extract (semantic only):

[hypatia-memory] Work unit still in progress, nothing extracted.

Important Rules

1. Never store sensitive information — no passwords, API keys, tokens
2. Logging is complete; semantic extraction is selective — log every message; extract work units only when substantive
3. Be conservative with work unit quality — skip when unsure
4. Be aggressive with extraction frequency — check every 5 turns
5. Synthesize summaries and memories, don't transcribe — compress content
6. Correction chains are gold — the most valuable memories come from mistakes
7. Use structured tags — message, session, summary <N>, memory, work-unit, rule, taboo
8. Don't interrupt the user — memory operations are background tasks
9. Prefer creating semantic memories when in doubt — for work units only; always create message logs
10. Tag and scope discipline — every entry includes --scopes "<PROJECT>"; global rules use ""

Graph Schema Reference

session-<SESSION_ID>  (tags: session)
    ↑ belongTo
msg-<SESSION_ID>-<TURN>  (tags: message)

<summary-name>  (tags: summary, summary 1)
    ↓ summary (×batch)
msg-...

<summary-name>  (tags: summary, summary 2)
    ↓ summary (×16)
<summary-name>...  (tags: summary, summary 1)

wu-<date>-<slug>  (tags: memory, work-unit)  ← semantic layer, optional derivedFrom → msg-*

Source: MarchLiu/hypatia — distributed by TomeVault.