/capture

Capture a single memory in the moment. Active verb: agent captures the
moment of insight, like a photograph.

Argument parsing

The /capture skill accepts one optional inline token of the form
space:<name> anywhere in the argument string. Extract it before
treating the rest as content:

raw_args="<the full argument string passed to /capture>"
space_arg="$(printf '%s\n' "$raw_args" | grep -oE 'space:[A-Za-z0-9_-]+' | head -1 | cut -d: -f2)"
content="$(printf '%s\n' "$raw_args" | sed -E 's/[[:space:]]*space:[A-Za-z0-9_-]+[[:space:]]*/ /g' | sed -E 's/^[[:space:]]+|[[:space:]]+$//g')"

If space_arg is non-empty, pass it to the resolver as --arg "$space_arg".

Resolve the active space

Call the bundled resolver:

resolved="$("$CLAUDE_PLUGIN_ROOT/bin/resolve-space.sh" --cwd "$PWD" \
    ${space_arg:+--arg "$space_arg"} 2>/dev/null)"
space="$(printf '%s\n' "$resolved" | cut -f1)"
source_layer="$(printf '%s\n' "$resolved" | cut -f2)"

Pass space="$space" to the capture MCP tool only when space is
non-empty. Before every capture, also print:

Resolved space: <space> (from <source-layer>)

If space is empty, print:

Resolved space: none (unscoped)

Unknown spaces are not auto-created. Register a new space first with
wenlan spaces add <space>, or omit space to store uncategorized.

How to invoke

Call the wenlan MCP server's capture tool with the user's content as a
complete, self-contained statement. Attach topic from cwd or the
conversation — don't make the user type it.

capture(content="<args, written as a full sentence with WHY>",
        memory_type="<picked from the 6 types>",
        entity="<primary entity name, if any>",
        space=<resolved if non-empty>)

memory_type — agent picks one of 6

The daemon classifies when a local model or API key is configured. In
local memory mode it does not, so the agent picks the type from the content itself. Use this
mapping:

Type	Use for
identity	Durable facts about the user (role, company, language preference)
preference	"I prefer X because Y" — a habit, a correction, a stylistic choice
decision	"Going with A over B because C" — a specific choice with rationale
lesson	Root cause found, workaround discovered, technical insight earned
gotcha	Sharp edge, surprising behavior, a thing to watch out for
fact	Durable info about people, projects, tools — anchor to entity when possible

If two types fit, pick the one closest to why the memory matters. A
decision also implies a preference, but decision is more specific.

entity — extract the anchor

Pick the single most important named thing in the content: a person,
project, tool, place. Use the exact name. Example: "Alice prefers TDD
because…" → entity="Alice". If the content has no named anchor,
omit entity.

topic / space inference

• cwd inside a repo → repo name (e.g. ~/Repos/wenlan/... → "wenlan").
• Outside any repo → most recent topic from the conversation, or omit.
• Pass space only when scope is known; if uncertain, run list_spaces
  later (post-PR-C) or omit.

Multiple entities or relations

The MCP capture tool takes a single primary entity. For additional
entities or relations, use the dedicated MCP tools. If the content
names more than one entity, capture the memory first, then for each
additional entity:

create_entity(name="<entity>", entity_type="<person|project|tool|place>")

For a relation between two entities:

create_relation(from_entity="<a>", to_entity="<b>", relation_type="<verb>")

Skip these calls when the daemon has an LLM — its post-ingest enrichment
covers extraction.

What to capture

• Decisions: "Going with approach A because B"
• Preferences: "Prefers TDD because catches regressions early"
• Corrections: "Actually it's C, not D"
• Identity / project facts: "Works on Wenlan, a local memory daemon for AI tools"

What NOT to capture

• System prompts, boot logs, heartbeats
• Transient task state ("currently working on...")
• Tool output, command results, architecture dumps
• Single-word acknowledgments
• Things the user can trivially re-derive (file paths, recent git history)
• Agent operating rules — "always X" / "never Y" directives about how the
  agent should behave. Those belong in CLAUDE.md / AGENTS.md / MEMORY.md (the
  obey tier), not Wenlan. Capture the user's preference ("prefers TDD
  because…"), not the agent-facing rule ("always run TDD first").

Atomic ideas

One capture = one idea. "Prefers TDD" and "Uses pytest" are two captures, not
one.

When to use

• User explicitly says "remember this", "save that", "capture this".
• User states a durable preference / decision / correction proactively (no
  ask required — that's the floor, not the trigger).

When NOT to use

• End of session bulk store → use /handoff (multi-item batch).
• Pulling memories back out → use /recall.

Post-capture contradiction signal

After capture returns, check response.triggered_revisions and response.auto_superseded.

auto_superseded (no action needed)

If auto_superseded is non-empty, the daemon already resolved the contradiction. Surface it as informational:

Note: auto-superseded mem_X. Wenlan replaced a prior protected memory because
trust=high and similarity > 0.9. No action needed.

No accept/dismiss call required. The revision was applied automatically.

triggered_revisions (human review needed)

If triggered_revisions is non-empty (and auto_superseded is empty), render an inline block to the user:

Stored mem_new.

This capture topic-matches a protected memory now flagged for revision:
  - mem_target_abc

Action: accept (replace original content) | dismiss (drop the revision) | leave (decide later)

Inline verb map:

• accept: accept_revision(target_source_id="mem_target_abc")
• dismiss: dismiss_revision(target_source_id="mem_target_abc")
• leave: no call; surfaces again in next /brief

Both fields can technically be non-empty in a single response (multiple protected matches), but in practice only one fires per capture: auto_superseded fires when trust=full and similarity > 0.9, triggered_revisions fires otherwise.

If neither field is non-empty, the capture stored cleanly with no conflicts.

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Source: 7xuanlu/origin — distributed by TomeVault.