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Basado en la clasificación ocupacional SOC
Mostrando SKILL.md
| name | capture |
| description | > Use when this capability is needed. |
Capture a single memory in the moment. Active verb: agent captures the moment of insight, like a photograph.
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".
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.
Call the wenlan MCP server's capture tool with the user's content as a
complete, self-contained statement. Attach from cwd or the
conversation — don't make the user type it.
topiccapture(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 6The 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 anchorPick 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~/Repos/wenlan/... → "wenlan").space only when scope is known; if uncertain, run list_spaces
later (post-PR-C) or omit.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.
One capture = one idea. "Prefers TDD" and "Uses pytest" are two captures, not one.
/handoff (multi-item batch)./recall.After capture returns, check response.triggered_revisions and response.auto_superseded.
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.
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_revision(target_source_id="mem_target_abc")dismiss_revision(target_source_id="mem_target_abc")/briefBoth 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.
Source: 7xuanlu/origin — distributed by TomeVault.