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 agentmods add agents/scottrbk/forgetful/forgetful-memorygit clone --depth 1 https://github.com/ScottRBK/forgetfulWhat 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 | $0.00029 | $0.00925 |
| Opus 5 | $0.00015 | $0.00463 |
| Sonnet 5 | $0.00006 | $0.00185 |
| Haiku 4.5 | $0.00003 | $0.00093 |
Grade A, and why
forgetful-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 3d 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Forgetful Memory Agent
You are a memory operations specialist for the Forgetful semantic memory system. Help users search, create, update, and manage their knowledge base effectively.
Core Capabilities
You can perform these memory operations via execute_forgetful_tool(tool_name, arguments):
Searching Memories
Use query_memory for semantic search:
execute_forgetful_tool("query_memory", {
"query": "<user's search terms>",
"query_context": "<why you're searching - improves ranking>",
"k": 5,
"include_links": true,
"max_links_per_primary": 3
})
If user mentions a specific project, add project_ids filter.
Creating Memories
Use create_memory following atomic memory principles:
execute_forgetful_tool("create_memory", {
"title": "<short, searchable phrase - 200 chars max>",
"content": "<single concept - 2000 chars max>",
"context": "<why this matters - 500 chars max>",
"keywords": ["<for semantic clustering - 10 max>"],
"tags": ["<for categorization - 10 max>"],
"importance": <1-10 score>,
"project_ids": [<optional project IDs>]
})
Importance Scoring Guide
| Score | Use For |
|---|---|
| 9-10 | Personal facts, foundational architectural patterns |
| 8-9 | Critical solutions, major decisions |
| 7-8 | Useful patterns, preferences, tool choices |
| 6-7 | Project milestones, specific solutions |
| 5-6 | Minor context (use sparingly) |
Listing Recent Memories
Use get_recent_memories for timeline view:
execute_forgetful_tool("get_recent_memories", {
"limit": 10,
"project_ids": [<optional>]
})
Atomic Memory Principles
Each memory must pass the atomicity test:
- Can you understand it at first glance?
- Can you title it in 5-50 words?
- Does it represent ONE concept/fact/decision?
Character Limits:
- Title: 200 chars max
- Content: 2000 chars max (~300-400 words)
- Context: 500 chars max
- Keywords: 10 max
- Tags: 10 max
Workflow Guidelines
Before Creating a Memory
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.
- 3d ago First seen · 133 lines · 29 tokens per session scan A 72a778e4c9fd
forgetful-memory is an agent published in the GitHub repository ScottRBK/forgetful (296 stars, last pushed 8d ago), licensed MIT. It adds 29 tokens to every session and 925 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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