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 skills/aiming-lab/simplemem/simplemem-skillnpx skills add aiming-lab/SimpleMem --skill simplemem-skillgit clone --depth 1 https://github.com/aiming-lab/SimpleMemWhat 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 | $0.00058 | $0.00845 |
| Opus 5 | $0.00029 | $0.00423 |
| Sonnet 5 | $0.00012 | $0.00169 |
| Haiku 4.5 | $0.00006 | $0.00085 |
Grade A, and why
simplemem-skill 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 2d 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SimpleMem Skill
Persistent conversational memory across sessions.
Proactive Usage
Save memories when discovering valuable dialogue:
- Important decisions or commitments made in conversation
- Complex information that may be referenced later
- Context from long discussions worth preserving
- Solutions to problems that took effort to uncover
Check memories before:
- Answering questions about past conversations
- Resuming work from previous sessions
- Building on earlier discussion topics
Quick Start
# Add a dialogue
python ~/.claude/skills/simplemem-skill/scripts/cli_persistent_memory.py add --speaker "Alice" --content "Meet Bob tomorrow at 2pm"
# Query memories
python ~/.claude/skills/simplemem-skill/scripts/cli_persistent_memory.py query --question "When should Alice meet Bob?"
Operations
Save
Add single dialogue:
python ~/.claude/skills/simplemem-skill/scripts/cli_persistent_memory.py add --speaker "User" --content "Your message here"
With timestamp (ISO 8601):
python ~/.claude/skills/simplemem-skill/scripts/cli_persistent_memory.py add --speaker "Alice" --content "Message" --timestamp "2026-01-17T14:00:00Z"
Query
Semantic query with answer:
python ~/.claude/skills/simplemem-skill/scripts/cli_persistent_memory.py query --question "What did Alice say about meetings?"
With reflection for deeper analysis:
python ~/.claude/skills/simplemem-skill/scripts/cli_persistent_memory.py query --question "Your question" --enable-reflection
Raw retrieval:
python ~/.claude/skills/simplemem-skill/scripts/cli_persistent_memory.py retrieve --query "Alice meetings" --top-k 5
Maintain
View statistics:
python ~/.claude/skills/simplemem-skill/scripts/cli_persistent_memory.py stats
Clear all memories:
# Use with caution - irreversible
python ~/.claude/skills/simplemem-skill/scripts/cli_persistent_memory.py clear --yes
Batch Import
For importing conversation histories from JSONL files, see references/import-guide.md.
What ships with it
22 files 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.
- .gitignore 234 B
- references/architecture.md 2.6 KB
- references/cli-reference.md 2.8 KB
- references/import-guide.md 1.2 KB
- references/openrouter-guide.md 2.3 KB
- references/requesty-guide.md 2.5 KB
- requirements.txt 120 B
- scripts/cli_persistent_memory.py 9.1 KB runs code
- src/config.py.example 2.9 KB
- src/core/__init__.py 222 B runs code
- src/core/answer_generator.py 5.0 KB runs code
- src/core/hybrid_retriever.py 38 KB runs code
- src/core/memory_builder.py 17 KB runs code
- src/database/vector_store.py 9.7 KB runs code
- src/main.py 9.3 KB runs code
- src/models/__init__.py 110 B runs code
- src/models/memory_entry.py 2.8 KB runs code
- src/utils/__init__.py 137 B runs code
- src/utils/embedding.py 4.5 KB runs code
- src/utils/llm_client.py 3.6 KB runs code
- src/utils/openrouter.py 7.4 KB runs code
- src/utils/requesty.py 7.1 KB runs code
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.
- 2d ago First seen · 130 lines · 58 tokens per session scan A 2944c9d9a8e4
simplemem-skill is a skill published in the GitHub repository aiming-lab/SimpleMem (3,733 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 845 once invoked, about $0.0003 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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