Convert MemoryLane natural-language questions into structured TASK JSON payloads.
npx skills add https://github.com/itsmarsss/memento --skill wiki-ask
Please help me install this Agent Skill. Skill: wiki-ask Source: https://github.com/itsmarsss/memento/tree/main/obsidian-wiki/.skills/wiki-ask Command: npx skills add https://github.com/itsmarsss/memento --skill wiki-ask
It turns a natural-language question about the MemoryLane vault into a frontend-renderable, machine-readable TASK object instead of a human-only prose answer.
q_<8-hex>) so the UI can dedupe and consistently render results.Run wiki-ask with your question text to get a single JSON object containing moments, entities, session metadata, and an optional synthesis insight.
High-intent search queries and answers about installing and using this skill.
To convert natural language vault queries into structured JSON, you need a retrieval mechanism that parses questions and emits a schema-compliant payload. This skill resolves MemoryLane questions into a machine-readable TASK JSON object containing moments, entities, and citations for direct frontend tab rendering.
Retrieving memory vault data with deterministic task IDs requires computing a stable question hash during the query process. This skill generates a unique 8-character hexadecimal identifier (`q_<8-hex>`) for each query, enabling the UI to deduplicate and consistently render structured retrieval results.
Semantic search for backend route usage in Obsidian vault querying involves spawning a retrieval process that returns machine-readable answers. This skill executes vault retrieval to compute activated node IDs and subassembly tags, emitting exactly one parseable JSON object matching the required frontend schema.
Yes, you can use structured JSON payloads for interactive right-panel display by including component-oriented response data. This skill returns a rich payload containing session metadata, synthesis insights, and block summaries, satisfying the requirements for rendering interactive right-panel components.
Hashing natural language questions for frontend tab rendering is limited by the deterministic nature of the hash, meaning identical questions yield identical IDs. This approach relies on exact string matching to deduplicate UI elements, so paraphrased questions will generate new unique task IDs instead of reusing existing ones.
zachmayerRoutes inputs to daily memory logs, Obsidian vault notes, or GitHub issues.
redfox-dataSearch Douyin viral content by keyword and date into a structured table.
dhaneshInitialize constraint manifold scaffolds with JSON and Markdown artifacts.
taracodlabsRead, search, and create Markdown notes within Obsidian vaults.