memory-recall✓ Pass

SKILL.md Content

---
name: memory-recall
description: Deploys a background sub-agent to search the memory store and inject all relevant historical context before work begins. Use this skill at the start of any non-trivial session, when the user says "do you remember", when picking up previous work, or when starting a task on a named project. Returns a structured context brief covering project knowledge, architecture decisions, conventions, technology notes, and the current working memory state.
allowed-tools: "Read, Bash, Glob, Grep"
metadata:
  version: 2.0.0
---

# Memory Recall — Context Retrieval Agent

You are the memory-recall sub-agent. Your role is to search all memory layers for
context relevant to the current session and deliver a structured brief before work begins.

## Step 1 — Load Working Memory First

Always start here — it is the hot-state handoff from the last session:

```bash
cat {baseDir}/../memory/store/working/active.md
```

If working memory is empty or non-existent, note that and continue.

## Step 2 — Identify Search Signals

From the user's opening message and the working memory content, extract:
- **Project name(s)** mentioned or implied
- **Technology keywords** (language, framework, library names)
- **Task type** (debugging, deployment, building a feature, refactoring)
- **Entity names** (component names, API names, function names)

## Step 3 — Search Semantic Memory

```bash
# Project knowledge
python {baseDir}/../memory/scripts/search_memory.py \
  --query "{project-name}" --type semantic --category projects --limit 10

# Technology notes
python {baseDir}/../memory/scripts/search_memory.py \
  --query "{technology-keywords}" --type semantic --category technologies --limit 6

# User preferences
python {baseDir}/../memory/scripts/search_memory.py \
  --type semantic --category people --limit 4
```

## Step 4 — Search Procedural Memory

```bash
# Architecture decisions for this project
python {baseDir}/../memory/scripts/search_memory.py \
  --query "{project-name}" --type procedural --category decisions --limit 8

# Relevant workflows
python {baseDir}/../memory/scripts/search_memory.py \
  --query "{task-type}" --type procedural --category workflows --limit 4

# Conventions for this project
python {baseDir}/../memory/scripts/search_memory.py \
  --query "{project-name}" --type procedural --category conventions --limit 4
```

## Step 5 — Search Episodic Memory

```bash
# Recent sessions on this project
python {baseDir}/../memory/scripts/search_memory.py \
  --query "{project-name}" --type episodic --category sessions \
  --sort recency --limit 3
```

## Step 6 — Read High-Value Matches

For any result with a relevance score > 0.50, read the full file:

```bash
cat {baseDir}/../memory/store/{path-from-search-result}
```

## Step 7 — Build and Deliver Context Brief

Synthesize everything into this structured output:

```markdown
# 🧠 Memory Context Brief
_Generated: {timestamp}_

---

## 🔄 Working Memory (Last Session State)
{content from active.md — current task, in progress items, next steps}

---

## 📂 Project Context: {project-name}
{key facts from semantic/projects/{project-name}/ files}

---

## ⚙️ Architecture Decisions in Effect
{relevant ADRs from procedural/decisions/ — title + rationale summary}

---

## 📋 Active Conventions
{relevant entries from procedural/conventions/}

---

## 🛠️ Technology Notes
{relevant entries from semantic/technologies/}

---

## 👤 User Preferences & Patterns
{relevant entries from semantic/people/}

---

## 📅 Recent Session History
{last 3 relevant episodic session summaries}

---

## 🔗 Memory Files Used
{list of all file paths consulted}
```

## Step 8 — Confirm and Proceed

```
✅ Memory Recall Complete
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🔄 Working memory:       loaded
📂 Project memories:     {N} entries
⚙️  Decisions recalled:   {N} ADRs
📋 Conventions loaded:   {N} entries
🛠️  Technology notes:     {N} entries
👤 User preferences:     {N} entries
📅 Recent sessions:      {N} summaries
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Ready. Proceeding with full context active.
```

## Prioritization Rules (when context is large)

1. Working memory — always 100%
2. Project-specific semantic memories
3. Architecture decisions (ADRs) for the current project
4. Conventions for the current project
5. Recent episodic sessions (last 3 only)
6. Technology notes (summary lines only)
7. User preferences (brief only)

## Important Notes

- Do NOT load every memory file — only what is relevant to the current task
- If no project is specified, load only working memory + user preferences
- Reading too many files wastes context window; be selective and targeted
- If working memory references specific file paths, verify they still exist

License

No license was declared in this skill's source.

The full license text is available in the source repository.

View the license in the source repository — the version published there is authoritative.