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Search erinra for stored memories
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Basé sur la classification professionnelle SOC
| name | recall |
| description | Search erinra for stored memories |
| argument-hint | [search query] |
Search erinra for previously stored memories.
Check that mcp__erinra__search is available in your tools. If not, tell the user:
Erinra MCP server is not connected. Run
claude mcp add erinra -- erinra serve -s userand restart Claude Code.
/decaf-memory:recall [search query]
Examples:
/recall HttpClient timeout — Find memories about HttpClient timeouts/recall user preferences — Retrieve stored user preferences/recall polly retry — Find patterns about Polly retry policiesSearch using mcp__erinra__search with the user's query:
mcp__erinra__search({
query: "[user's search terms]",
limit: 10
})
Display results showing:
If no results, suggest:
/decaf-memory:remember to store something newUse filters to narrow results:
mcp__erinra__search({
query: "[search terms]",
projects: ["project-name"], // Filter by project (OR across projects)
type: "pattern", // Filter by exact type
tags: ["dotnet", "async"], // Filter by tags (AND — must have all)
include_archived: false, // Default: false
limit: 10
})
Narrow by when memories were created or last updated:
mcp__erinra__search({
query: "[search terms]",
created_max_age_days: 30, // Created within the last 30 days
updated_after: "2025-01-01T00:00:00Z" // Updated after a specific date
})
Available time filters: created_after, created_before, updated_after, updated_before, created_max_age_days, created_min_age_days, updated_max_age_days, updated_min_age_days.
To browse memories without a search query, use mcp__erinra__list instead:
mcp__erinra__list({
projects: ["project-name"],
type: "decision",
limit: 20
})
This supports the same filters as search but returns paginated results with a total count.
Search results may truncate long content. To get the full text of a memory:
mcp__erinra__get({ ids: ["[memory-id]"] })
Search results include outgoing and incoming links. To follow a link and see the related memory:
mcp__erinra__get({ ids: ["[linked-memory-id]"] })
Run parallel code review agents and consolidate findings into a unified report
Capture a follow-up idea or task as a work-item draft without interrupting current work. Use to quickly jot down something you think of mid-task so it gets tracked. Works with nibs, GitHub, Azure DevOps, or a Markdown fallback.
Take an under-specified work item and make it actionable — resolve its open questions through a short interview grounded in the code, and give it acceptance criteria. Use on a captured draft, or any item too vague to start on.
Reconcile what was built against what was planned, record decisions and deviations, close the item (a single phase or a whole plan), and file follow-ups for deferred work. Use after finishing a phase or plan to keep planned and actual from drifting apart.
Orchestrate execution of MULTIPLE nibs in one run. Selects a queue, understands the nibs collectively (including how they fit together), then chooses the best execution mechanism per cluster — single series agent, parallel fan-out, scripted workflow, or agent team — and dispatches with ONE approval gate. Use when the user wants to work several nibs together (in parallel or series) rather than one at a time. Complements /decaf-build:auto-dev and /decaf-build:auto-tdd (which handle a single nib).
Direct development with automated review. Plans implementation, executes via subagent, then auto-reviews. Use for work that isn't test-driven (UI, config, styling, infrastructure, scaffolding).