hmem-write

name	hmem-write
description	Hmem write protocol — picks prefix (L/E/D/P/N/R/I/H…), tree location, and tags, and detects duplicates before persisting. Use before any write_memory or append_memory call (skipping it creates duplicates and misplaced entries), or when the user says 'remember this', 'save this', 'log this', or invokes /hmem-write.

How to use write_memory

Call the MCP tool write_memory to save lessons, errors, decisions, or project insights to long-term memory.

If write_memory is not available:

1. Tell the user: "write_memory tool not found. Please reconnect the MCP server (in Claude Code: /mcp, in other tools: restart the tool)."
2. NEVER write directly to the .hmem SQLite file via shell commands. The database has WAL journaling, integrity checks, and tree-structure logic that raw SQL INSERT will bypass — causing corruption or data loss.

---

Syntax

write_memory(
  prefix: "E",
  content: "Short Title (~50 chars)\n\nL1 body — detailed explanation, can span multiple lines\nsecond body line with more context\n\tL2 node title\n\n\tL2 body text (supports newlines)\n\tmore L2 body\n\t\tL3 detail (2 tabs)\n\t\t\tL4 raw data (3 tabs — rarely needed)"
)

Title + Body convention (git-commit style): Every node has a title (short navigation label) and an optional body (detailed content loaded on drill-down). Separate them with a blank line — just like a git commit message.

• Title: The first line (L1) or first line at a given indent level (L2+). ~50 chars, like a chapter title.
• Body: Everything after the blank line at the same indent level. Freetext, no special prefix needed. Shown only when the node is drilled into, not in listings.
• Legacy > prefix: Still works for backward compatibility, but blank-line separation is preferred.
• Without body: The full text is stored as content and the title is auto-extracted from the first maxTitleChars characters.

L1 example with body:

Short Error Title

SQLite connection failed because .mcp.json used a relative path.
The fix was to use an absolute path in the HMEM_PATH env var.
	Details about reproduction

	Steps: 1. Set HMEM_PATH=./hmem  2. Run hmem serve  3. Observe SQLITE_CANTOPEN

L1 example without body (still works):

SQLite connection failed due to wrong path in .mcp.json
	Fix: use absolute path in env var

Indentation: 1 tab = 1 level. Alternatively: 2 or 4 spaces per level (auto-detected). Warning: A tab at the start of any line always means "go one level deeper" — it is structural, not content. If you need to store code or text that contains leading tabs, use spaces instead. IDs and timestamps are assigned automatically — never write them yourself.

---

Hashtags — add to every write_memory and append_memory call

Hashtags connect entries across all prefixes and hierarchy levels. They are the only cross-prefix discovery mechanism.

Add 3–5 tags per call (max 10):

write_memory(prefix="E", content="...", tags=["#hmem", "#sqlite", "#bug", "#migration", "#windows"])
append_memory(id="P0029", content="...", tags=["#hmem", "#sync", "#cli"])

Rules:

• Lowercase, starts with #, only letters/digits/hyphen/underscore: #hmem-sync, #api_key
• append_memory tags are additive — they do not replace existing tags
• write_memory tags: if entry has children → land on first child node; if leaf (no children) → land on root
• append_memory tags are stored only on the target node — no upward propagation
• Every node at any depth can have its own tags — use this to make sub-topics discoverable

Good tags: #hmem, #sync, #sqlite, #windows, #release, #bug, #security, #althing, #cli, #migration Bad tags: #fix (too generic), #important (no context), #2026 (not a topic)

Bulk-tagging for existing entries:

tag_bulk(filter={prefix: "E"}, add_tags=["#bug"])           # all E-entries
tag_bulk(filter={search: "hmem-sync"}, add_tags=["#sync"])  # by full-text search
tag_rename(old_tag="#hmem-store", new_tag="#hmem")           # rename a tag everywhere

---

Prefixes

Prefix	Category	When to use
P	(P)roject	Project entries — standardized L1 format (see below)
L	(L)esson	Lessons learned, best practices — cross-project knowledge
E	(E)rror	Bugs, errors + their fix — auto-scaffolded schema (see below)
D	(D)ecision	Architecture decisions with reasoning — cross-project knowledge
T	(T)ask	Cross-project or infrastructure tasks ONLY (see note below)
M	(M)ilestone	Cross-project milestones ONLY — project milestones go in P-entry L2 "Protocol"
S	(S)kill	Skills, processes, how-to guides
N	(N)avigator	Code pointers — where something lives in the codebase
H	(H)uman	Knowledge about the user — preferences, context, working style
R	(R)ule	User-defined rules and constraints — "always do X", "never do Y"
I	(I)nfrastructure	Devices, servers, deployments, network — one entry per device/server

Where do tasks, errors, lessons, and decisions go?

Tasks belong inside the project's P-entry L2 "Open tasks" node:

append_memory(id="P0048.8", content="Implement multi-server sync\n\tPush/pull to all configured servers", tags=["#hmem-sync"])

Use the T-prefix ONLY for tasks that span multiple projects or are infrastructure/meta tasks (e.g. "Set up Strato server", "Run curation pass"). These get links=["P00XX"] to the most relevant project.

Milestones belong in the P-entry L2 "Protocol" node as a chronological entry:

append_memory(id="P0048.7", content="v4.0.0 published — project gate + load_project tool (2026-03-27)", tags=["#release"])

Use the M-prefix ONLY for milestones that span multiple projects (e.g. "First cross-device sync working").

Errors (E), Lessons (L), Decisions (D) stay as independent root entries — they are cross-project knowledge. An SQLite lesson learned in hmem applies to every SQLite project. Always add tags and links to connect them back:

write_memory(prefix="E", content="...", tags=["#hmem", "#sqlite"], links=["P0048"])

P-entry "Known issues" (L2) contains short summaries pointing to E-entries — not the errors themselves:

append_memory(id="P0048.6", content="Auto-sync fails with multiple .hmem in CWD → E0097, T0043", tags=["#hmem-sync"])

Custom prefixes: If none of the above fit, you can use any single uppercase letter. To register it officially (so the system validates it), add it to hmem.config.json under "prefixes":

{ "prefixes": { "R": "Research" } }

Custom prefixes are merged with the defaults — they don't replace them. Without registering, the system will reject the prefix.

Schema-Enforced Entries (P and any prefix with a defined schema)

The MCP server enforces schemas for any prefix that has a schemas entry in hmem.config.json. For those prefixes, two rules apply:

1. write_memory: L2 node names must match the defined section names (error otherwise).
2. append_memory to root entry (e.g. P0029, no dot): blocked for arbitrary content — you cannot add new L2 sections outside the schema. Exception: if the first line of content is a valid schema section name (e.g. "Rules"), the append is allowed — useful for adding a missing section to an existing entry. Otherwise append to a specific section: append_memory(id="P0029.N", content="...").

By default, P has a schema. If L, D, or other prefixes are configured with schemas, the same rules apply.

P-Entry Standard Schema

Every project entry MUST follow this structure.

L1 Title: Name | Status | Tech Stack | GH: owner/repo | Short description The GH field is optional — include it when a GitHub repo exists, omit otherwise. L1 Body: (same line or next non-indented line) One-sentence project summary.

Status values:

Status	Meaning
New	Just started, concept phase
Active	In active development
Mature	Feature-complete, only bugfixes
Paused	On hold, will resume later
Archived	Done or abandoned

L2 categories (fixed order, skip sections that are empty):

The MCP server validates that L2 nodes start with one of these names. Minimum for a new project: Overview + Codebase (or Usage).

L2 Category	What goes here	L3 children
Overview	First thing an agent reads (like CLAUDE.md /init)	Current state, Goals, Architecture, Environment
Codebase	Code structure — NO code, only names + signatures	Entry point, Core modules (each module = L4 node with signature + purpose + return), Helpers, Config, Tests
Usage	How the project is used	Installation/Setup, CLI/API commands, Common workflows
Context	Background and motivation	Initiator, Target audience, Business context, Dependencies (links)
Deployment	Build/CI/CD/publish process	(flat or with L3 sub-steps)
Bugs	Active bugs + known limitations	L3: inline report (symptom + cause) OR pointer to E-entry (→ E0097). L4: reproduction steps
Protocol	Session log, chronological	One-liner per session + links to O-entries
Open tasks	Project-specific TODOs	One per L3 node. Cross-project tasks → T-prefix with links
Ideas	Feature ideas, brainstorming	L3: short description, L4: implementation details

load_project tool: Use load_project(id="P0048") to activate a project and get the full briefing (L2 content + L3 titles) in one call. This is the recommended way to start working on a project — it combines read + activate.

Markers you may see on entries:

Marker	Meaning
[♥]	Favorite — always expanded in bulk reads
[★]	Top-accessed — high weighted access score
[≡]	Top-subnode — many children
[⚡]	Task-promoted — relevant to an active T/P/D entry (tag overlap)
[*]	Active — currently in focus
[P]	Pinned — super-favorite, shows full L2
[!]	Obsolete — superseded, kept for history
[-]	Irrelevant — hidden from bulk reads
✓	Synced — backed up to all sync servers

Complete P-entry example (WeatherBot):

write_memory(
  prefix="P",
  content="WeatherBot | New | Python/Discord.py | GH: user/weatherbot\n\nDiscord bot for weather forecasts — slash commands for current weather and multi-day forecasts\n\tOverview\n\t\tCurrent state\n\n\t\tScaffolding done, no commands yet. Bot connects to Discord but has no slash commands registered.\n\t\tGoals\n\n\t\tDaily/hourly forecasts via slash commands, multi-city support, embed formatting\n\t\tArchitecture\n\n\t\tDiscord slash command → OpenWeatherMap API → formatted embed. Single-file cog pattern.\n\t\tEnvironment\n\n\t\t/home/user/weatherbot, python bot.py, needs DISCORD_TOKEN + WEATHER_API_KEY in .env\n\tCodebase\n\t\tEntry point — bot.py, start: python bot.py\n\t\tCore modules\n\t\t\tweather_cog.py — WeatherCog(Cog); fetch_forecast(city: str) → discord.Embed\n\t\t\tformatter.py — format_embed(data: dict) → discord.Embed\n\t\tHelpers / Utilities\n\t\t\tapi_client.py — get_weather(city: str) → dict; wraps HTTP to OpenWeatherMap\n\t\tConfig / Constants — .env: DISCORD_TOKEN, WEATHER_API_KEY, DEFAULT_CITY\n\t\tTests — pytest, test_weather_cog.py (3 tests)\n\tUsage\n\t\tInstallation / Setup — pip install -r requirements.txt, cp .env.example .env\n\t\tCLI / API — /weather <city>, /forecast <city> (planned)\n\tContext\n\t\tInitiator — personal project, Mar 2026\n\t\tTarget audience — personal Discord server\n\t\tDependencies — discord.py, OpenWeatherMap API, aiohttp\n\tOpen tasks\n\t\tImplement /forecast command\n\n\t\tMulti-day view with daily highs/lows and weather icons per day\n\t\tAdd city autocomplete",
  tags=["#discord", "#python", "#weather", "#bot"],
  links=[]
)

Note: L2 nodes use 1 tab, L3 uses 2 tabs, L4 uses 3 tabs. Separate title from body with a blank line at the same indent level. Skip empty sections — no need for placeholder text.

Auto-Scaffold for Schema Prefixes (v7.0.0+)

Any prefix with a schema defined in hmem.config.json gets its sections auto-created on write when you don't supply L2 nodes. This includes I, A, P, and any custom prefix with a schema. The write response shows the full section list:

write_memory(prefix="I", content="Laptop | Active | macOS 14 | macbook", tags=["#device"])
→ Schema: .1 Specs, .2 OS, .3 IP Address, .4 Access, .5 Services, .6 Apps, .7 Storage, .8 Notes

Fill sections afterwards with append_memory(id="I00XX.1", content="..."). If you supply L2 nodes yourself, they are validated against the schema but no additional nodes are created.

E-Entry Schema (auto-scaffolded)

E-entries have a pre-built structure — just provide a title and short description, the server creates the rest:

write_memory(prefix="E", content="hmem sync bug on v1.0.1\n\nConnection fails when HMEM_PATH contains spaces", tags=["#hmem", "#sync", "#path"])

This auto-creates:

• .1 Analysis (your description goes here automatically)
• .2 Possible fixes
• .3 Fixing attempts
• .4 Solution
• .5 Cause
• .6 Key Learnings

Plus #open tag. Fill in the nodes as you debug with append_memory/update_memory. The response shows similar E/D entries by tag overlap — check them before reinventing the wheel. When solved, replace #open with #solved and fill .4 + .5 + .6.

E-entries are not shown in bulk reads — they surface automatically via tag overlap when you create new E/D entries. Solved bugs are knowledge, not clutter.

Marking entries as favorites

Mark any entry as a favorite to ensure it always appears with its L2 detail in bulk reads (alongside a [♥] marker). Use this for reference info you need to see every session — API endpoints, key decisions, frequently looked-up patterns.

write_memory(prefix="D", content="...", favorite=true)           # set at creation
update_memory(id="D0010", content="...", favorite=true)          # set on existing
update_memory(id="D0010", content="...", favorite=false)         # clear

Favorites are not a prefix — they are a flag on any entry regardless of category. Use sparingly: if everything is a favorite, nothing is. Prefer high-value reference entries over fleeting notes.

---

Marking entries as obsolete

When you notice that an entry is outdated — superseded by a newer approach, a fixed bug, or changed architecture — do not delete it. Mark it as obsolete with a correction reference:

# Step 1: Write the correction FIRST
write_memory(prefix="E", content="Correct approach is XYZ\n\tDetails...")  # → E0076

# Step 2: Mark old entry obsolete — MUST include [✓ID] tag
update_memory(id="E0023", content="Wrong approach — see [✓E0076]", obsolete=true)

The [✓ID] tag is enforced. The system will reject obsolete=true without a correction reference. This ensures every obsolete entry points to its replacement. The system also creates bidirectional links automatically (E0023↔E0076).

The entry stays in memory with a [!] marker. Past errors still carry learning value ("we tried this and it failed because..."). The curator may eventually prune it, but that's their decision, not yours.

Shortcut for stale entries: If no correction exists (entry is just old/irrelevant, not wrong), only the curator can mark it obsolete without [✓ID].

---

N — Navigator (Code Pointers)

Use N to save a pointer to a specific file, function, or code location so you don't have to search for it next session.

write_memory(
  prefix="N",
  content="Link-Auflösung beim read_memory-Aufruf
	src/hmem-store.ts ~line 269 — read() method, ID branch
	Guard: resolveLinks !== false prevents circular refs
	Introduced in v1.4.0",
  links=["E0069"]
)

L1: What it is — one sentence describing the concept/feature L2: Exact file path + line range + function/method name L3: Context, caveats, related patterns Links: Related entries (errors, decisions, lessons)

Your responsibility: Update your N entries whenever you notice code has moved or logic has changed. You don't need the curator for this — use update_memory directly. Stale pointers are worse than none. If you cannot verify whether the pointer is still valid, mark it obsolete: update_memory(id="N0012", content="...", obsolete=true).

---

Title + Body Quality Rules

Title: Short navigation label, ~50 chars (configurable via maxTitleChars). Think "chapter title in a book".

• Good: "hmem.py Performance: Bulk-Queries statt N+1", "Ghost Wakeup Bug in msg-router.ts"
• Bad: "Fixed a bug", "Important lesson" (too vague)

Body (after blank line): Detailed explanation — full sentences, multiline OK. Shown on drill-down, hidden in listings.

• Must be understandable without any context
• Not "Fixed a bug" — instead explain root cause, fix, and impact

With title + body (recommended):

write_memory(prefix="L", content="hmem.py Performance: Bulk-Queries statt N+1\n\nAlle Nodes in 2 Bulk-Queries laden, nicht pro Entry einzeln.\nVorher: load_nodes() pro Entry = N+1 SQLite-Connections.\n\tImplementation detail\n\n\tChanged read() to batch-fetch all nodes for visible entries in one query")

Without body (simple entries, backward-compatible):

write_memory(prefix="E", content="SQLite connection failed due to wrong path in .mcp.json\n\tFix: use absolute path in env var")

Title auto-extracted: "SQLite connection failed due to wrong path in .mc"

---

Company Knowledge (requires AL+ role)

write_memory(
  prefix: "S",
  store: "company",
  content: "..."
)

---

Before Writing: Navigate the Tree First

Never write blindly. Before creating a new entry or appending, navigate the existing tree top-down to find the correct insertion point. New information almost always belongs inside an existing entry — not as a new root.

Protocol

Step 1 — Check L1 summaries (already in context) Scan the root entries visible in your context. Is there a matching root for this topic?

• No match → write_memory() creates a new root
• Match found → continue to Step 2

Step 2 — Read the matching root's children

read_memory(id="P0029")   # shows root + all L2 titles

Do any L2 titles match the sub-topic?

• No match (and no schema) → append_memory(id="P0029", content="...") adds a new L2
• No match (schema-constrained entry like P) → find the closest existing section and append there (arbitrary L2 additions are blocked; only valid schema section names are allowed at root)
• Match found (e.g. .15) → continue to Step 3

Step 3 — Drill into that L2

read_memory(id="P0029.15")   # shows L2 node + all L3 titles

Does an L3 match even more specifically?

• No match → append_memory(id="P0029.15", content="...") adds a new L3
• Match found → continue drilling (Step 4: read_memory(id="P0029.15.2"), etc.)

Stop drilling when: no child matches, or you've reached the level of granularity that fits.

Example

New insight about MCP server restart behavior after TypeScript compile:

# Step 1: L1 summaries show L0074 "MCP server muss nach Kompilierung neugestartet werden"
# Step 2: read_memory(id="L0074") → shows .1 "Fix: kill + auto-respawn", .2 "Context: Althing only"
# No L2 matches the new sub-case → append at L2

append_memory(id="L0074", content="Standalone hmem-mcp: npm restart required (no auto-respawn)")
# → adds L0074.3 (L3 under the existing lesson)

Instead of: write_memory(prefix="L", content="MCP restart needed after compile...") — which would duplicate L0074.

When write_memory is correct

Only use write_memory when:

• No root entry exists for this topic at all
• The topic is genuinely orthogonal (different error, different decision, different project)
• You're creating an E/L/D entry for a new root cause, not an extension of an existing one

Rule: If in doubt, drill one level deeper before deciding to create a new root.

---

When to save?

Checkpoint mode matters. Check checkpointMode in hmem.config.json:

• "auto" (recommended): A background Haiku subagent handles checkpoints automatically every N exchanges. It reads recent O-entry exchanges, calls read_memory to avoid duplicates, and writes L/D/E entries + handoff via MCP tools. It also writes a rolling checkpoint summary ([CP] node tagged #checkpoint-summary) that compresses older exchanges for load_project. Skill-dialog exchanges are auto-tagged #skill-dialog and filtered from context injection. You do NOT need to write entries yourself unless the user explicitly asks you to save something specific.

• "remind": You will receive a CHECKPOINT reminder every N messages. When you see it, save key learnings yourself using write_memory / append_memory.

In both modes: Only save what is still valuable in 6 months.

Save	Don't save
New root cause + fix	Routine actions without learning value
Insight that changes future work	What's already in the codebase
Architecture decision + reasoning	Temporary debugging notes
Unexpected tool/API behavior	What's in the documentation

One write_memory call per category — entire hierarchy in one content string.

---

Updating Existing Memories

Use update_memory and append_memory to modify entries without deleting and recreating them.

update_memory — Fix outdated text

Updates the text of a single node. Children are not touched.

update_memory(id="L0003", content="Corrected L1 summary — new wording")
update_memory(id="L0003.2", content="Fixed L2 detail")
update_memory(id="D0010", content="New L1", links=["E0042"])  # also update links

Use when: the wording is wrong, outdated, or needs clarification.

append_memory — Add detail to existing entry

Appends new child nodes under an existing root or node. Existing children are preserved.

Content indentation is relative to the parent — 0 tabs = direct child of id. Body works the same as in write_memory — blank line separates title from body.

Schema enforcement: For entries with a defined schema (e.g., P, I), appending to the root (e.g., id="P0029") is blocked unless the content's first line matches a defined section name. Use case: adding a missing section (e.g. "Rules") to an entry created before that section existed. For arbitrary new content, target a specific section: append_memory(id="P0029.3", content="..."). For entries without a schema (L, D, E, etc. by default), root appends are always allowed.

append_memory(
  id="L0003",
  content="New finding discovered later\n\nDetailed explanation of what was found and why it matters.\nThis can span multiple lines.\n\tSub-detail about it"
)
# → adds L0003.N (L2 with title + body) and L0003.N.1 (L3)
# ↑ only works if L has no schema defined; use L0003.N for schema-constrained entries

append_memory(
  id="P0029.3",
  content="New detail in the Usage section"
)
# → adds P0029.3.M (L3 under section .3) — correct way for schema-constrained entries

append_memory(
  id="L0003.2",
  content="Extra note under L0003.2"
)
# → adds L0003.2.M (L3)

Use when: you have new context to add without replacing what's there.

When to use which

Situation	Tool
L1 wording is wrong/outdated	update_memory
A sub-node has wrong detail	update_memory
You have new info to add	append_memory
Entry is completely wrong	mark obsolete with [✓newId], then write_memory for the correction

---

Access Count (Automatic + Time-Weighted)

Access counts are managed automatically — every read_memory and append_memory call bumps the accessed entries. The ranking uses time-weighted scoring (access_count / log2(age_in_days + 2)) so newer entries with fewer accesses can outrank stale old ones. Entries with the highest weighted scores get [★] markers and expanded treatment in bulk reads. To explicitly mark an entry as important, use favorite: true on write_memory or update_memory.

---

Bulk Tag Operations

Apply tags to multiple entries at once, or rename a tag everywhere:

# Add #bugfix to all E-prefix entries
tag_bulk(filter={prefix: "E"}, add_tags=["#bugfix"])

# Add tag to entries matching a search term
tag_bulk(filter={search: "FTS5"}, add_tags=["#search", "#sqlite"])

# Remove #old from all entries that have it
tag_bulk(filter={tag: "#old"}, remove_tags=["#old"])

# Add and remove simultaneously
tag_bulk(filter={prefix: "L", tag: "#draft"}, add_tags=["#stable"], remove_tags=["#draft"])

# Rename a tag everywhere
tag_rename(old_tag="#hmem-store", new_tag="#hmem")

Use tag_bulk when adding a new systematic tag to an existing category, or cleaning up after a tagging convention change. tag_rename handles typos or renames across the entire memory.

---

H-Prefix: User Skill Assessment

Actively track the user's expertise level across topics. This drives how you communicate — a coding expert doesn't need variable explanations, a beginner doesn't need jargon.

Structure

One H-entry per main topic, with sub-nodes per subtopic:

write_memory(prefix="H", content="User Skill: IT
	Coding — Advanced: writes TypeScript fluently, debugs SQLite schemas, understands async/MCP
	Terminal/CLI — Advanced: bash, git, systemctl, nvm, sqlite3 comfortable
	Networking — Intermediate: HTTP/DNS solid, asked about WebSocket details
	DevOps — Intermediate: systemd + nvm yes, Docker unfamiliar",
  tags=["#skill-assessment", "#it"])

Levels: 1-10 scale (see user-assessment skill for full details). 1-2 = no experience, 5-6 = intermediate, 9-10 = expert. Half-points allowed.

Always include evidence (observed behavior, not assumptions).

When to assess

• First interaction: Make initial assessment from vocabulary, questions, and tool usage
• Ongoing (every few exchanges): Watch for signals:
  • Upgrade signals: uses domain-specific terms correctly, solves problems independently, corrects the agent
  • Downgrade signals: "das verstehe ich nicht", "explain that", asks about basic concepts, misuses terms
• On /save: Review and update assessments if evidence accumulated

How to update

Reference the O-entry (automatic session log) where the skill change was observed:

# User demonstrated new skill — link to the exchange that proves it
append_memory(id="H0010", content="Docker — Intermediate: configured docker-compose independently (see O0042.15)")

# User's skill improved
update_memory(id="H0010.3", content="Networking — Advanced: configures DNS, TLS, reverse proxies (see O0042.23)")

# User struggled — downgrade with evidence
update_memory(id="H0010.4", content="DevOps — Beginner: asked what systemd is, needed step-by-step (see O0042.8)")

The O-entry reference lets future agents verify the assessment by reading the original conversation.

How to USE assessments

Before explaining anything technical, check the relevant H-entry:

• Beginner: Explain concepts, use analogies, avoid jargon, step-by-step
• Intermediate: Brief explanations, some jargon OK, link to docs for details
• Advanced: Direct technical language, skip basics, focus on trade-offs
• Expert: Peer-level discussion, challenge assumptions, discuss edge cases

Example: If H0010.1 says "Coding — Advanced", don't explain what a Map is. If H0010.4 says "DevOps — Beginner", explain what a systemd service does before configuring one.

Topics are open-ended

Not just IT — any domain the user works in:

• Music (theory, instruments, production)
• Mechanical (bikes, cars, tools)
• Business (accounting, marketing, management)
• Languages (German, English proficiency)

Create new H-entries as topics emerge naturally from conversation.

---

Language Consistency

Match the language of existing entries. Before writing, check what language the memory store uses (run read_memory() if unsure). If existing entries are in German, write in German. If English, write in English. Do not mix languages within a single store — it makes search and curation harder.

After Writing: Verify the Entry Captured the Insight

For L, D, and E entries with complex reasoning, do a quick read-back:

read_memory(id="X")

Ask: does this entry answer "why?" not just "what?"

Update if any of these are true:

• Entry is under 20 words with no body
• Content is "Lesson learned: X" with no explanation or evidence
• Entry would be incomprehensible to a future agent without this conversation context

Skip for: simple factual entries, routine appends, O-entry checkpoints.

---

Anti-Patterns

Wrong	Right
L1 too short: "Fixed bug"	Full sentence with root cause + blank line + body
Writing English when existing entries are German	Match the store's language
Tabs inside content text (e.g. code snippets)	Use spaces for indentation within content — tabs at line start always mean "go deeper in the hierarchy"
Mixed spaces and tabs for hierarchy	Stay consistent — either tabs or spaces as your depth marker
Everything flat, no indentation	Use hierarchy — L2/L3 for details
Save trivial things	Quality over quantity
Forget to write_memory	Always call BEFORE setting Status: Completed
Write to .hmem via sqlite3/SQL	ONLY use write_memory MCP tool — never raw SQL
MCP unavailable → skip saving	Reconnect MCP first (/mcp or restart tool)
update_memory(id="X", obsolete=true) without [✓ID]	Write correction first, then mark obsolete with [✓E0076] tag