active-recall
Weave retrieval checks into dialogue: resurface earlier material unprompted and track decay.
Skill by poorvith-mp
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About
Use cases: testing
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/active-recall
What's inside
- Skill name
active-recall- Description
- Weave retrieval checks into dialogue: resurface earlier material unprompted and track decay. Use when testing retention via Socratic active recall.
- License
- MIT
- Instructions
- 98 lines in SKILL.md · view on GitHub
Files (3)
- SKILL.md
- active-recall.skill
- routing.yaml · evals/
SKILL.md (first lines)
# active-recall
## Core Philosophy
Passive review—rereading textbooks, highlighting paragraphs, and rewatching recorded lectures—is a cognitive illusion. It creates the "fluency illusion," where familiarity with the material is mistaken for true mastery. Real, durable learning occurs exclusively during the effortful act of retrieval: pulling information out of biological memory without looking at notes. Active recall exploits the testing effect (Roediger & Karpicke) and the Ebbinghaus forgetting curve, transforming study sessions from passive consumption into high-yield cognitive retrieval workouts.
---
## 4-Step Active Recall Architecture
### Step 1: The Socratic Retrieval Mechanism
1. **Closing the Book (The Free-Recall Brain Dump)**:
- Read a complex concept for 15 minutes. Close the source material immediately.
- Take a blank sheet of paper and write down every concept, formula, mechanism, and connection you can recall without looking.
- Reopen notes in red pen: highlight what was missed, inaccurate, or poorly explained.
2. **The Socratic Self-Questioning Matrix**:
- Convert chapter subheadings into direct interrogative questions:
- *Passive*: "TCP 3-Way Handshake"
- *Active*: "Draw the exact packet flags exchanged between client and server to initiate a TCP connection, and explain what happens if SYN-ACK is dropped."
### Step 2: Decay Tracking & Ebbinghaus Timing
1. **The Forgetting Curve Math**:
$$R = e^{-\frac{t}{S}}$$
- Where $R$ is memory retention, $t$ is elapsed time, and $S$ is memory stability.
2. **Optimal Retrieval Intervals**:
- Schedule active recall sessions just as memory begins to decay:
- Session 1: 10 minutes post-learning (Free recall).
- Session 2: 24 hours later (Targeted problem solving).
- Session 3: Day 4 (Interleaved scenario testing).
- Session 4: Day 10 (Cumulative mock questions).
- Session 5: Day 30 (Long-term consolidation check).
### Step 3: Socratic Dialogue & Unprompted Interleaving
1. **Unprompted Dialogue Resurfacing**:
- During study or tutoring dialogue, interrupt the current topic to resurface foundational concepts learned 2 weeks prior:
- *"Before we implement this Red-Black tree deletion, tell me: why does a standard binary search tree degrade to $O(n)$ time?"*
2. **Interleaved Topic Mixing**:
- Never practice one topic in blocked repetition (e.g. 20 integration-by-parts problems). Mix integration by parts, substitution, and partial fractions unpredictably so the brain must first identify *which* tool to retrieve.
### Step 4: Cognitive Effort Calibration & Difficulty Tuning
1. **The Desirable Difficulty Principle (Bjork)**:
- If retrieval feels effortless, zero neural remodeling occurs.
- If retrieval fails completely (0% recall), cognitive overload has occurred; step down abstraction.
- Target an **80-85% success rate** on recall challenges to maximize neuroplastic adaptation.
---
## Deliverable Format: Active Recall Socratic Question Deck
```markdown
# Topic: Distributed Consensus (Raft Algorithm)
### Retrieval Prompt 1 (Mechanics):
- **Question**: What are the 3 distinct states a Raft node can occupy, and what trigger causes a Follower to transition into a Candidate?
- **Expected Retrieval**:
- States: Leader, Follower, Candidate.
- Trigger: Election timer expires without receiving an AppendEntries heartbeat from the Leader.
### Retrieval Prompt 2 (Failure Scenarios):
- **Question**: Suppose a network partition splits 5 nodes into [N1, N2] and [N3, N4, N5]. Which partition can commit client writes, and why?
- **Expected Retrieval**:
- The [N3, N4, N5] partition has a quorum majority (3 out of 5 nodes). The [N1, N2] partition can accept writes but cannot reach majority consensus, so those writes will not commit and will be rolled back upon rejoining.
### Retrieval Prompt 3 (Edge Case):
- **Question**: How does Raft prevent two candidates from indefinitely splitting votes in a tie?
- **Expected Retrieval**:
- Randomized election timeouts (e.g. 150ms - 300ms), ensuring one candidate times out and requests votes before its peers.
```
---
## Worked Example: Transforming a Medical Student's Pharmacology Study Loop
- **Problem**: A medical student spent 4 hours a night highlighting a 600-page pharmacology textbook, failing weekly drug classification quizzes with a 54% average.
- **Intervention**:
1. Replaced highlighting with a closed-book Socratic flashcard protocol.
2. Implemented the "Blank Sheet Protocol" immediately after each 30-minute reading block.
3. Practiced active symptom $\to$ drug mechanism diagnosis with interleaved case studies.
- **Outcome**: In 4 weeks, quiz scores jumped from 54% to 91%, while total daily study time decreased by 1.5 hours.
---
## Verification Checklist
- [ ] All reading blocks paired with immediate closed-book free recall.
- [ ] Questions formatted as open interrogatives rather than binary recognition cues.
- [ ] Retrieval sessions spaced across days 1, 4, 10, and 30.
- [ ] Related topics interleaved rather than studied in monolithic blocks.
- [ ] Memory decay monitored; missed topics re-queued within 24 hours.
---
## Anti-Patterns
- **Passive Highlighting / Re-reading**: Drawing colored marker lines over textbook pages and counting it as studying.
- **Looking at the Answer Early**: Peeking at the back of the card after 2 seconds of hesitation instead of forcing 30 seconds of effortful mental retrieval.
- **Blocked Categorical Practice**: Practicing 50 identical physics problems in a row where the formula is already known before reading the problem.From poorvith-mp/skills-education, MIT.
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Signals
- Stars
- 1
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Sep 12, 2026 (1 month ago)
- Last release
- Oct 2, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/poorvith-mp/skills-education
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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active-recall skill. Agent Mart. https://agentmart.dev/skills/poorvith-mp-active-recall (last updated 2026-10-24)