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 commands/mu4farooqi/superwiser/seedgit clone --depth 1 https://github.com/mu4farooqi/superwiserWrote 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/commands/mu4farooqi/superwiser/seed)<a href="https://agentmods.dev/commands/mu4farooqi/superwiser/seed"><img src="https://agentmods.dev/badge/commands/mu4farooqi/superwiser/seed.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00006 | $0.00298 |
| Opus 5 | $0.00003 | $0.00149 |
| Sonnet 5 | $0.00001 | $0.00060 |
| Haiku 4.5 | $0.00001 | $0.00030 |
Grade A, and why
seed 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 4d 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.
What it actually says
Seed the Superwiser rule database from your past Claude Code conversations.
Instructions
Step 1: Show transcript preview
Call seed_preview MCP tool from superwiser.
Present the results to the user like this:
Found [count] transcripts spanning [oldest_date] to [newest_date].
How would you like to seed?
1. Latest N transcripts (e.g., "latest 5")
2. All after a date (e.g., "after 2025-06-01")
3. All [count] transcripts
Step 2: Wait for user choice
The user will respond with one of:
- "latest N" (e.g., "latest 5", "latest 10")
- "after YYYY-MM-DD" (e.g., "after 2025-06-01")
- "all"
Step 3: Call seed_from_history with their filter
Based on the user's choice:
- For "latest N":
seed_from_history(latest_n=N) - For "after DATE":
seed_from_history(after_date="YYYY-MM-DD") - For "all":
seed_from_history()(no arguments)
Step 4: Report results
Tell the user:
- How many prompts were queued
- That processing happens in the background
- They can use
/superwiser:listto check progress
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.
- 4d ago First seen · 43 lines · 6 tokens per session scan A 71545edc277f
seed is a command published in the GitHub repository mu4farooqi/superwiser (6 stars, last pushed 7mo ago), licensed MIT. It adds 6 tokens to every session and 298 once invoked, about $0.0000 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.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
session-end
I'll summarize this coding session and update the memory system with our accomplishments.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.