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spaced-recall

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更新时间2026年7月5日 23:31

Schedule spaced repetition over a memory palace so it lasts, and export to Anki. Use when the user says "spaced repetition", "when should I review", "schedule my reviews", "keep this long-term", "export to Anki", "FSRS", or "SM-2". Computes next-due dates from palace-walk grades, writes the recall block of palace.json, and drives durable retention against the forgetting curve.

安装

用 Codex 或 Claude 帮你安装复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。

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frankxai

frankxai/mind-palace-agent-skills

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在 Manus 中运行

spaced-recall

The durability step. Building encodes; spacing keeps it. Review each locus just before it would fade — not daily, not never.

When to use this

The user wants a palace to survive weeks and months, asks when to review, or wants their loci in a spaced-repetition tool (Anki). Pairs with palace-walk, which supplies the grades.

Instructions

Pick the scheduler:
- FSRS (Free Spaced Repetition Scheduler) — modern, evidence-led, models per-card stability and difficulty. Default. Targets a desired retention (e.g. 0.9) and spaces accordingly.
- SM-2 — the classic Anki algorithm; simpler ease-factor model. Use when matching legacy Anki decks or when the user explicitly wants it.
Read the grades. Take per-locus results from palace-walk (got/partial/missed, mapped to the scheduler's grade scale). A missed is a lapse and shortens the next interval sharply.
Compute next-due. Update each locus's recall block: stability, difficulty, reps, lapses, lastReviewed, and the new due (ISO date). New loci start with a short first interval; well-recalled loci stretch out.
Surface today's queue. When asked "what's due", list only loci whose due ≤ today, in route order, and hand them to palace-walk. Never front-load the whole palace — spacing is the point.
Export to Anki when asked — see references/anki-export.md: one note per locus (front = station/cue, back = target + image), preserving FSRS/SM-2 state where possible.
Respect the no-guilt rule. A skipped review is silent; just recompute from the real lastReviewed. No streak penalties, no nagging.

Refusals

Don't schedule reviews more often than the algorithm warrants ("review everything daily" wastes effort and isn't spacing).
Don't fabricate recall state for loci that were never walked — schedule only what's been tested.

References

references/anki-export.md — the Anki export format.
spec/MEMORY-PALACE-METHOD.md §4 (forgetting curve, spacing, FSRS/SM-2).

Built on SIP · mind-palace-agent-skills · Memory Palace Method v0.1

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frankxai/mind-palace-agent-skills

Give an AI agent persistent, navigable memory across sessions using a memory-palace structure. Use when building agent memory, when the user says "remember this across sessions", "give the agent long-term memory", "persistent context", "knowledge graph for my agent", or for coding agents, Claude cowork, and ChatGPT projects that must retain state. Reads and writes palace.json as a durable spatial knowledge store the agent reloads each session.

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frankxai/mind-palace-agent-skills

Train mental-imagery vividness and scene construction so memory-palace images are stronger. Use when the user says "my mental pictures are weak/blurry", "I can't visualize", "train my imagination", "help me see images more clearly", or struggles to encode loci. Drills based on imagery research — multisensory detail, manipulation, and scene construction — to make encoding work better.

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frankxai/mind-palace-agent-skills

Turn each target into a vivid, memorable image and place it at a station. Use when the user says "encode these items", "make images for my palace", "help me remember names and faces", "turn this list into pictures", or after memory-palace-architect lays out the rooms. Applies bizarreness, multisensory, exaggeration, motion, and dual-coding; fills the loci[] of palace.json.

2026-07-050

memory-palace-architect

frankxai/mind-palace-agent-skills

Design a memory palace — choose a real or imagined locus, lay a fixed non-crossing route, and space the stations. Use when the user says "design a memory palace", "pick a place to memorize in", "lay out my palace", "map a route for N items", or needs a structure before encoding. Writes the rooms/stations skeleton of palace.json for loci-encoder to fill.

2026-07-050

number-memory

frankxai/mind-palace-agent-skills

Memorize digits, dates, phone numbers, and playing cards by converting numbers into images. Use when the user says "memorize this number", "remember these dates", "how do I memorize a deck of cards", "PAO system", "Major System", or "Dominic System". Supplies the encoding for numeric targets, then hands the resulting images to loci-encoder for placement.

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palace-foundations

frankxai/mind-palace-agent-skills

Teach the method of loci and route the user to the right memory-palace skill. Use when someone says "teach me the memory palace", "how does the method of loci work", "help me memorize X", "I want to remember a list/speech/deck", or is new to spatial memory. The start-here skill that explains the four-level model then dispatches to architect, encoder, number-memory, walk, or spaced-recall.

2026-07-050

name	spaced-recall
description	Schedule spaced repetition over a memory palace so it lasts, and export to Anki. Use when the user says "spaced repetition", "when should I review", "schedule my reviews", "keep this long-term", "export to Anki", "FSRS", or "SM-2". Computes next-due dates from palace-walk grades, writes the recall block of palace.json, and drives durable retention against the forgetting curve.

spaced-recall

The durability step. Building encodes; spacing keeps it. Review each locus just before it would fade — not daily, not never.

When to use this

The user wants a palace to survive weeks and months, asks when to review, or wants their loci in a spaced-repetition tool (Anki). Pairs with palace-walk, which supplies the grades.

Instructions

Pick the scheduler:
- FSRS (Free Spaced Repetition Scheduler) — modern, evidence-led, models per-card stability and difficulty. Default. Targets a desired retention (e.g. 0.9) and spaces accordingly.
- SM-2 — the classic Anki algorithm; simpler ease-factor model. Use when matching legacy Anki decks or when the user explicitly wants it.
Read the grades. Take per-locus results from palace-walk (got/partial/missed, mapped to the scheduler's grade scale). A missed is a lapse and shortens the next interval sharply.
Compute next-due. Update each locus's recall block: stability, difficulty, reps, lapses, lastReviewed, and the new due (ISO date). New loci start with a short first interval; well-recalled loci stretch out.
Surface today's queue. When asked "what's due", list only loci whose due ≤ today, in route order, and hand them to palace-walk. Never front-load the whole palace — spacing is the point.
Export to Anki when asked — see references/anki-export.md: one note per locus (front = station/cue, back = target + image), preserving FSRS/SM-2 state where possible.
Respect the no-guilt rule. A skipped review is silent; just recompute from the real lastReviewed. No streak penalties, no nagging.

Refusals

Don't schedule reviews more often than the algorithm warrants ("review everything daily" wastes effort and isn't spacing).
Don't fabricate recall state for loci that were never walked — schedule only what's been tested.

References

references/anki-export.md — the Anki export format.
spec/MEMORY-PALACE-METHOD.md §4 (forgetting curve, spacing, FSRS/SM-2).

Built on SIP · mind-palace-agent-skills · Memory Palace Method v0.1