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 rjmurillo/ai-agents --skill using-forgetful-memorygit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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/rjmurillo/ai-agents/using-forgetful-memory)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/using-forgetful-memory"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/using-forgetful-memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/using-forgetful-memory"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/using-forgetful-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00117 | $0.02025 |
| Opus 5.5 | $0.00047 | $0.00810 |
| Sonnet 5.5 | $0.00023 | $0.00405 |
| Haiku 4.5 | $0.00012 | $0.00202 |
Grade A, and why
using-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 1mo 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using Forgetful Memory
Forgetful is a semantic memory system using Zettelkasten (atomic note) principles. This skill guides effective memory usage.
When to Query Memory
Query memory proactively when:
- Starting work on a project (check for existing context)
- User references past work, decisions, or discussions
- Encountering a problem that may have been solved before
- Implementing patterns that may already be documented
- Needing context about preferences or approaches
Use execute_forgetful_tool("query_memory", {...}) with:
query: Natural language search termsquery_context: Why you're searching (improves ranking)include_links: true (to see connected knowledge)
Getting Recent Memories for a Project
To see what's been recorded recently for a specific project:
execute_forgetful_tool("get_recent_memories", {
"limit": 10,
"project_ids": [PROJECT_ID]
})
This is useful when:
- Starting a session on a project you haven't worked on recently
- Reviewing what was captured in previous conversations
- Getting a quick overview of project knowledge
When to Create Memory
Create memories for knowledge worth preserving:
- Important decisions with rationale (importance 8-9)
- Technical patterns or approaches (importance 7-8)
- Architectural choices (importance 9-10)
- Preferences and workflows (importance 8-9)
- Project milestones (importance 6-7)
- Solutions to non-trivial problems (importance 7-8)
Do NOT create memories for:
- Temporary context (current file paths, transient issues)
- Common knowledge available elsewhere
- Trivial or throwaway information
- Content that changes frequently
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?
Constraints
| Field | Limit | Guidance |
|---|---|---|
| Title | 200 chars | Short, searchable phrase |
| Content | 2000 chars | Single concept (~300-400 words) |
| Context | 500 chars | WHY this matters |
| Keywords | 10 max | For semantic clustering |
| Tags | 10 max | For categorization |
What ships with it
1 file 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.
- 1mo ago First seen · 273 lines · 117 tokens per session scan A ce4f835fb8ce
using-forgetful-memory is a skill published in the GitHub repository rjmurillo/ai-agents (49 stars, last pushed today), licensed MIT. It adds 117 tokens to every session and 2,025 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
handoff
Create a structured session handoff when context is running low or work is pausing. Deep context mining, self-validation, multi-file splitting. Captures everything the next session needs.
abmind-runtime-operations
Inspect and maintain abmind memory, persona files, sleep, backups, encryption, and runtime health.
abmind_recall_workaround
Fallback when abmind recall / the abmindrecall tool fails — read memories directly from the SQLite DB.
memory-search
Search persistent memory for facts, decisions, and past conversations.
read-messages
Read back raw conversation messages from the abmind messages table (session list + message dump).
topic-save
Save and manage topic-specific knowledge files from conversations.