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/trafficguard/typedai/kbgit clone --depth 1 https://github.com/TrafficGuard/typedaiWrote 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/trafficguard/typedai/kb)<a href="https://agentmods.dev/commands/trafficguard/typedai/kb"><img src="https://agentmods.dev/badge/commands/trafficguard/typedai/kb.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 | $0.00000 | $0.00186 |
| Opus 5 | $0.00000 | $0.00093 |
| Sonnet 5 | $0.00000 | $0.00037 |
| Haiku 4.5 | $0.00000 | $0.00019 |
Grade A, and why
kb scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Extract knowledge from the current conversation session to build the project knowledge base.
Carefully ultrathink reviewing in detail every message from the start of the conversation, taking notes on what was learnt along the way, such as:
- Application design/features
- File system paths and project structure
- Why certain decisions were made
- Fixed incorrect code/decisions
- Project-level conventions, patterns, and designs
Focus on knowledge that can be provided to other developers and AI agents, both specific to the applications and general principles. Do NOT include details that are only specific to a single file - this is about capturing project-level knowledge.
After reviewing, save the knowledge report to a timestamped file:
mkdir -p kb && echo "Saving to kb/$(date '+%Y-%m-%d--%H-%M').md"
Write the knowledge report to that file, then confirm what was saved.
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 · 18 lines · 0 tokens per session scan A 647f0fa8a89b
kb is a command published in the GitHub repository TrafficGuard/typedai (1,192 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 186 tokens. 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.
Other commands, from other repositories
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mempalace-status
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ingest
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fire-debug
Systematic debugging with persistent state, skills library integration, and WARRIOR validation.
reflect
Reflect on recent work — log lessons to the journal and propose distilled rule updates for review.
knowledge-transfer
Interview a person to capture their operating know-how (tacit knowledge) and write it into the workspace as the company brain: processes, rules with source, glossary, decisions and open questions. For onboarding, a key person leaving, or standardizing a process.