A structured system for recording daily and weekly learning progress, including knowledge points, mistakes, study status, and future suggestions. It stores both readable summaries and linked graphs of learning information.

Use it to save daily learning summaries, track recurring errors and high-risk topics, review historical knowledge relationships, and generate weekly study plans.

It reduces the work of reconstructing what was studied, what went wrong, and what needs attention next. The records provide a consistent history for reviewing progress and planning study.

memory — Skill by MathClaw-ruc

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/mathclaw-ruc/mathclaw/memory

Any agent

npx skills add MathClaw-ruc/MathClaw --skill memory

Clone the repo

git clone --depth 1 https://github.com/MathClaw-ruc/MathClaw

Made for: Claude Code, Codex.

Per session 13 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 551 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00013	$0.00551
Opus 5	$0.00006	$0.00275
Sonnet 5	$0.00003	$0.00110
Haiku 4.5	$0.00001	$0.00055

Measured 2d ago against content hash 466b583d6179, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

mathclaw/skills/memory/SKILL.md · 57 lines

How it starts

The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Memory

Structure

memory/daily_memory/YYYY.M.D/YYYY_M_D.md - Human-readable daily summary for one day.
memory/daily_memory/YYYY.M.D/YYYY_M_D.json - Structured daily payload for code and downstream features.
memory/weekly_memory/YYYY_M_D_to_YYYY_M_D/YYYY_M_D_to_YYYY_M_D.md - Weekly study plan generated from the last 7 daily summaries.
memory/graphs/knowledge_graph.json - Knowledge-point graph.
memory/graphs/error_graph.json - Error-pattern graph.
memory/MEMORY.md - Auto-generated compatibility snapshot loaded into prompt context.
memory/HISTORY.md - Append-only audit log of consolidation events.

Read Order

Prefer these sources in order:

Daily .json when you need structured fields such as date, high_risk_knowledge_points, high_frequency_error_types, learning_status_summary, and tomorrow_study_suggestions.
Daily .md when you need a human-facing summary.
Graph JSON files when you need relationships, ranking, or historical nodes.
Weekly .md when you need the next-week learning plan.
HISTORY.md only when you need an audit trail of what was archived.

Graph Semantics

Knowledge graph:

Nodes are knowledge points.
Important fields include time_points, risk, mastery, importance, last_seen, and display_size.
Relations include prerequisite, similar, contains, and related links.

Error graph:

Nodes are error patterns.
Important fields include error_count, severity, repeated, last_seen, and display_size.
Relations include corresponding knowledge point, similar error, and correction suggestions.

Dynamic Adjustment Rules

Low-frequency, low-risk, long-inactive nodes can be archived.
Similar concepts should be merged upstream before adding too many nodes.
Example items only keep recent representative samples; older examples are dropped automatically.
Frontend views should size nodes by importance or severity, not raw node count.

Update Policy

Do not manually edit MEMORY.md unless you are intentionally adjusting the compatibility snapshot.
Prefer updating the structured daily JSON or graph JSON when building new features.
If both Markdown and JSON exist for the same day, trust the JSON as the machine-readable source.

Read the full file on GitHub · 57 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2d ago First seen · 57 lines · 13 tokens per session scan A 466b583d6179

Subscribe to this mod's changes

memory is a skill published in the GitHub repository MathClaw-ruc/MathClaw (372 stars, last pushed 4mo ago), licensed MIT. It adds 13 tokens to every session and 551 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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