ChatGPT Plugin

CurveMora

Your learning memory schedule

Brand: CurveMora

Category: Pending
Primary Subcategory: Pending

Integration details

Description

CurveMora connects your learning records and memory schedule to conversation. See what is due today, find a record by its title or source title, inspect memory state and requested repetition history, understand scheduling reasons, and explore upcoming workload and completed activity. Preview a new record or a break before saving anything. CurveMora’s deterministic scheduler remains authoritative for due dates. This initial version only reads data and creates previews; it does not create, change, or delete learning data. Access is limited to the CurveMora account that authorizes the connection, which can be revoked in CurveMora.

Integration type: Plugin
Verification status: Not applicable
Platform: ChatGPT
Category: Pending
Primary Subcategory: Pending
Secondary Subcategories: None listed
Brand: CurveMora
Access: Account required
First tracked: 2026-11-13
Tool count: 8
Market: US

AgentDiscoChatGPT Plugin Discovery Score

ChatGPT organic discovery is live

Not discoverable in ChatGPT yet

ChatGPT now suggests Plugins on its own when they match a user's request, but not every Plugin is being surfaced yet.We haven’t seen CurveMora suggested yet. Sign up and we’ll track it, and your Discoverability Score will appear here once ChatGPT starts surfacing it.

ChatGPT recommends Airtable when a user asks it to update a CRM. — ChatGPT surfaces Airtable because it matches the user's request.

Primary Subcategory

Competitive lineup

No Primary Subcategory assigned yetOnce CurveMora has a Primary Subcategory, the Plugins and Connectors it competes with will appear here with their scores.

Tools

8 tools agents can invoke

Use when the user asks why one known active CurveMora record is due on a particular date or how its next repetition date was derived. Requires an item ID and returns the deterministic scheduling trace; use get_agenda or get_calendar to discover scheduled items first.
explain_schedule
Use when the user asks what to study or review today or over the next few days. Returns the actionable CurveMora plan, backlog, breaks, and capacity from today through a bounded horizon; use get_calendar for an explicit date range and get_metrics for historical statistics.
get_agenda
Use when the user asks for CurveMora scheduling over an explicit local-date range, including committed and optionally projected repetitions. Use get_agenda for today's or near-term actionable plan rather than an arbitrary date range.
get_calendar
Use when the user asks about completed CurveMora learning activity, review counts, time spent, retention indicators, streaks, or trends for a day/week/month/year. This reports factual historical metrics; use get_agenda or get_calendar for what is scheduled next.
get_metrics
Use when a specific CurveMora item ID is already known and the user wants its memory state, hierarchy, due state, or optional notes/history. Private review notes and parsed conversational learning context are returned only when includeHistoryNotes=true. Do not use this to discover records by text; use search_records first when the item ID is unknown.
get_record
Use when the user is considering time off and wants to see how a proposed CurveMora Break would redistribute scheduled repetitions. This is read-only and persists nothing; it previews impact only.
preview_break
Use when the user wants to see how a proposed new CurveMora learning record would be scheduled before saving it. Validates the prospective capture and returns a deterministic review preview without persisting any record; do not treat the preview as a completed save.
preview_capture
Use when the user wants to locate a CurveMora record by record title or source title and an item ID is not yet known. Returns compact matches only; it does not search notes or review history. Use get_record after selecting a specific result.
search_records

FAQs

CurveMora ChatGPT Plugin FAQ

How the directory, categories and Discoverability Score work.

Read the methodology

How do I improve CurveMora's ChatGPT Plugin discoverability?

The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.

Where is this profile measured?

This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.