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/mahmoudimus/simba/remembernpx skills add mahmoudimus/simba --skill remembergit clone --depth 1 https://github.com/mahmoudimus/simbaWhat 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 | $0.00032 | $0.00838 |
| Opus 5 | $0.00016 | $0.00419 |
| Sonnet 5 | $0.00006 | $0.00168 |
| Haiku 4.5 | $0.00003 | $0.00084 |
Grade A, and why
remember scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/remember - Save Session to Memory
Summarize the current work session and save it to persistent memory for future context.
Instructions
When the user invokes /remember, you should:
1. Check for Activity Log
First, check if there's an activity log from this session:
REPO_ROOT=$(git rev-parse --show-toplevel 2>/dev/null || echo "$PWD")
ACTIVITY_FILE="$REPO_ROOT/.simba/search/activity.log"
if [ -f "$ACTIVITY_FILE" ]; then
cat "$ACTIVITY_FILE"
fi
2. Analyze the Session
Review what was accomplished in this conversation:
- What files were read, created, or modified?
- What was the main goal or task?
- What key decisions were made?
- Were there any important patterns or learnings?
3. Generate Summary
Create a concise summary (2-3 sentences) that captures:
- What was done (the main accomplishment)
- Why it matters (the purpose or problem solved)
- Key details (important files, patterns, or decisions)
4. Extract Metadata
Identify:
- Files touched: List of file paths worked on
- Topics: 3-5 keywords for searchability (e.g., "auth, jwt, middleware, security")
- Tools used: Main tools used (Read, Write, Edit, Bash, etc.)
5. Save to Memory
Use the simba.search CLI to save:
# Initialize if needed
uv run python -m simba.search init
# Save session
uv run python -m simba.search add-session \
"YOUR_SUMMARY_HERE" \
'["file1.ts", "file2.ts"]' \
'["Read", "Edit", "Bash"]' \
"topic1, topic2, topic3"
6. Optionally Add Knowledge or Facts
If during the session you learned something important about the codebase that should be remembered:
For code area knowledge:
uv run python -m simba.search add-knowledge \
"src/auth" \
"Authentication module using JWT tokens with refresh token rotation" \
"Tokens expire in 15min, refresh tokens in 7 days"
For project facts:
uv run python -m simba.search add-fact \
"Uses PostgreSQL with Prisma ORM" "architecture"
uv run python -m simba.search add-fact \
"All API routes require authentication except /health" "convention"
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 · 125 lines · 32 tokens per session scan A 60d204afac95
remember is a skill published in the GitHub repository mahmoudimus/simba (6 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 838 once invoked, about $0.0002 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 2026-08-31.
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