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Run npx skillmds@latest add frumu-ai/productivity-memory in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Productivity Memory free to use?

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Who published Productivity Memory?

frumu-ai (@frumu-ai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Productivity Memory by frumu-ai — Agent Skill

Name: Productivity Memory
Rating: 4.2 (5 reviews)
Author: frumu-ai

Productivity Memory

Two-tier memory system that makes the AI a true workplace collaborator. Decodes shorthand, acronyms, nicknames, and internal language so the AI understands requests like a colleague would. Uses CONTEXT.md for working memory and a memory/ directory for the full knowledge base.

frumu-ai Updated Sep 10, 2026

File contents

Memory Management

Memory makes the AI your workplace collaborator - someone who speaks your internal language.

The Goal

Transform shorthand into understanding:

User: "ask todd to do the PSR for oracle"
              ↓ AI Decodes
"Ask Todd Martinez (Finance lead) to prepare the Pipeline Status Report
 for the Oracle Systems deal ($2.3M, closing Q2)"

Without memory, that request is meaningless. With memory, the AI knows:

todd → Todd Martinez, Finance lead, prefers Slack
PSR → Pipeline Status Report (weekly sales doc)
oracle → Oracle Systems deal, not the company

Architecture

CONTEXT.md           ← Hot cache (~30 people, common terms)
memory/
  glossary.md      ← Full decoder ring (everything)
  people/          ← Complete profiles
  projects/        ← Project details
  context/         ← Company, teams, tools

CONTEXT.md (Hot Cache):

Top ~30 people you interact with most
~30 most common acronyms/terms
Active projects (5-15)
Your preferences
Goal: Cover 90% of daily decoding needs

memory/glossary.md (Full Glossary):

Complete decoder ring - everyone, every term
Searched when something isn't in CONTEXT.md
Can grow indefinitely

memory/people/, projects/, context/:

Rich detail when needed for execution
Full profiles, history, context

Lookup Flow

User: "ask todd about the PSR for phoenix"

1. Check CONTEXT.md (hot cache)
   → Todd? ✓ Todd Martinez, Finance
   → PSR? ✓ Pipeline Status Report
   → Phoenix? ✓ DB migration project

2. If not found → search memory/glossary.md
   → Full glossary has everyone/everything

3. If still not found → ask user
   → "What does X mean? I'll remember it."

This tiered approach keeps CONTEXT.md lean (~100 lines) while supporting unlimited scale in memory/.

File Locations

Working memory: CONTEXT.md in current working directory
Deep memory: memory/ subdirectory

Working Memory Format (CONTEXT.md)

Use tables for compactness. Target ~50-80 lines total.

# Memory

## Me

[Name], [Role] on [Team]. [One sentence about what I do.]

## People

| Who       | Role                               |
| --------- | ---------------------------------- |
| **Todd**  | Todd Martinez, Finance lead        |
| **Sarah** | Sarah Chen, Engineering (Platform) |
| **Greg**  | Greg Wilson, Sales                 |

→ Full list: memory/glossary.md, profiles: memory/people/

## Terms

| Term    | Meaning                  |
| ------- | ------------------------ |
| PSR     | Pipeline Status Report   |
| P0      | Drop everything priority |
| standup | Daily 9am sync           |

---name: productivity-memorydescription: Two-tier memory system that makes the AI a true workplace collaborator. Decodes shorthand, acronyms, nicknames, and internal language so the AI understands requests like a colleague would. Uses CONTEXT.md for working memory and a memory/ directory for the full knowledge base.---# Memory ManagementMemory makes the AI your workplace collaborator - someone who speaks your internal language.## The GoalTransform shorthand into understanding:```User: "ask todd to do the PSR for oracle" ↓ AI Decodes"Ask Todd Martinez (Finance lead) to prepare the Pipeline Status Report for the Oracle Systems deal ($2.3M, closing Q2)"```Without memory, that request is meaningless. With memory, the AI knows:- **todd** → Todd Martinez, Finance lead, prefers Slack- **PSR** → Pipeline Status Report (weekly sales doc)- **oracle** → Oracle Systems deal, not the company## Architecture```CONTEXT.md ← Hot cache (~30 people, common terms)memory/ glossary.md ← Full decoder ring (everything) people/ ← Complete profiles projects/ ← Project details context/ ← Company, teams, tools```**CONTEXT.md (Hot Cache):**- Top ~30 people you interact with most- ~30 most common acronyms/terms- Active projects (5-15)- Your preferences- **Goal: Cover 90% of daily decoding needs****memory/glossary.md (Full Glossary):**- Complete decoder ring - everyone, every term- Searched when something isn't in CONTEXT.md- Can grow indefinitely**memory/people/, projects/, context/:**- Rich detail when needed for execution- Full profiles, history, context## Lookup Flow```User: "ask todd about the PSR for phoenix"1. Check CONTEXT.md (hot cache) → Todd? ✓ Todd Martinez, Finance → PSR? ✓ Pipeline Status Report → Phoenix? ✓ DB migration project2. If not found → search memory/glossary.md → Full glossary has everyone/everything3. If still not found → ask user → "What does X mean? I'll remember it."```This tiered approach keeps CONTEXT.md lean (~100 lines) while supporting unlimited scale in memory/.## File Locations- **Working memory:** `CONTEXT.md` in current working directory- **Deep memory:** `memory/` subdirectory## Working Memory Format (CONTEXT.md)Use tables for compactness. Target ~50-80 lines total.```markdown# Memory## Me[Name], [Role] on [Team]. [One sentence about what I do.]## People| Who | Role || --------- | ---------------------------------- || **Todd** | Todd Martinez, Finance lead || **Sarah** | Sarah Chen, Engineering (Platform) || **Greg** | Greg Wilson, Sales |→ Full list: memory/glossary.md, profiles: memory/people/## Terms| Term | Meaning || ------- | ------------------------ || PSR | Pipeline Status Report || P0 | Drop everything priority || standup | Daily 9am sync |```

Frequently asked questions