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.
npx skills add yugash007/edu-agent-skills --skill learning-memorygit clone --depth 1 https://github.com/yugash007/edu-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yugash007/edu-agent-skills/learning-memory)<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/learning-memory"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/learning-memory.svg" alt="Measured on agentmods" height="20"></a>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 5.1 | $0.00019 | $0.00725 |
| Opus 5 | $0.00010 | $0.00362 |
| Sonnet 5 | $0.00004 | $0.00145 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
learning-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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Maintain a structured learner profile across sessions so agents start at the right depth, avoid re-teaching covered material, and prioritize known weak areas.
Activation
- New session begins and prior context may exist. Learner references prior sessions or covered topics. Session ends and state should be preserved. Agent needs to personalize without re-asking onboarding questions.
- Skip if: one-off session with no continuity desired, no persistent storage available, or narrow stateless task.
- Routing: run at session start (restore) and end (save). Feed weak areas to
weak-area-tracker. Profile drivesfind-your-levelfor returning learners with uncertain level.
Inputs
- Prior session summary/profile, current session transcript, concepts + outcomes, learner self-reports, error patterns from assessment skills.
Profile Schema (compact)
learner_profile:
level: beginner | intermediate | advanced
stated_goal: "<goal>"
learning_style: code-first | concept-first | mixed
weak_areas: [{topic, type, last_seen, correction_attempted}]
covered_topics: [{topic, confidence: low|medium|high, last_confirmed}]
active_checkpoint: "<last concept/milestone in progress>"
session_count: N
last_session: "<ISO date>"
Workflow
Session Start (Restore)
- Retrieve — Check for prior profile. If exists: summarize and confirm accuracy with learner. If none: run
find-your-level. - Staleness Check — Last session 2+ weeks ago → flag for light review. Goal changed → update target.
- Inject — Feed relevant profile data (level, weak areas, last checkpoint) into current session. Don't re-explain confirmed material unless requested.
Session End (Save)
- Extract — Record: concepts covered, understanding outcomes, new weak areas, self-reported confidence.
- Update — Merge into profile. Promote "in-progress" → "covered" when confirmed. Add new weak areas.
- Handoff — Output compact summary: where to resume, what to skip, top 1–2 priorities for next session.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 71 lines · 19 tokens per session scan A fa812a902afa
learning-memory is a skill published in the GitHub repository yugash007/edu-agent-skills (7 stars, last pushed 3mo ago), licensed MIT. It adds 19 tokens to every session and 725 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 2026-08-31.
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