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/xsovad06/sova/extract-knowledgegit clone --depth 1 https://github.com/xsovad06/sovaWhat 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 | $0.00016 | $0.01002 |
| Opus 5 | $0.00008 | $0.00501 |
| Sonnet 5 | $0.00003 | $0.00200 |
| Haiku 4.5 | $0.00002 | $0.00100 |
Grade A, and why
extract-knowledge 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 yesterday.
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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extract Knowledge
Review this session's work and extract reusable knowledge into the project's knowledge system.
Knowledge Architecture
This project uses a four-tier knowledge system:
Tier 0: ~/.claude/shared-knowledge/ (cross-project patterns)
Reusable patterns that apply across multiple repositories. Loaded by agent startup scripts.
Tier 1: .claude/rules/*.md (canonical, always loaded via CLAUDE.md)
Stable patterns loaded into every session via CLAUDE.md. Primary destination for confirmed conventions.
Tier 2: .claude/agent-memory/ (agent learnings, loaded by morning agent)
Lessons learned from development and review cycles. Destination for agent-specific patterns that improve autonomous development quality.
MEMORY.md-- Index filecookbook.md-- Topical knowledge by domain, common mistakes with occurrence counts
Tier 3: User auto-memory (~/.claude/projects/.../memory/)
User preferences, workflow conventions, and project state.
Steps
1. Identify What Changed
Run git diff and git diff --cached to see current changes. Also review the conversation for patterns, gotchas, decisions, and fixes that came up during the session.
2. Categorize Findings
For each finding, determine the right destination:
| Finding type | Destination |
|---|---|
| Domain-specific patterns (security, performance, etc.) | .claude/rules/<domain>.md |
| ORM/framework gotchas, review lessons, recurring mistakes | .claude/agent-memory/cookbook.md (under matching domain section) |
| Agent workflow or project pattern changes | .claude/agent-memory/MEMORY.md |
| User preferences, workflow, collaboration style | User auto-memory |
3. Check for Duplicates
Before writing anything:
- Read the target file
- Check for existing entries about the same topic
- Update existing entries rather than adding duplicates
- Remove entries that are now outdated or wrong
4. Write Knowledge
For .claude/rules/*.md (Tier 1):
- Follow the existing structure and formatting of the target file. Write concise entries with bold labels, then the lesson. These are always loaded -- keep entries actionable and specific to this project.
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.
- yesterday First seen · 110 lines · 16 tokens per session scan A c6c4c27d29c7
extract-knowledge is a command published in the GitHub repository xsovad06/sova (2 stars, last pushed 2d ago), licensed Apache-2.0. It adds 16 tokens to every session and 1,002 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 2026-08-31.
Other commands, from other repositories
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retro
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dashboard
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learn
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quiz
Quiz yourself on a topic from your learning plan with adaptive difficulty and mixed question formats.