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 skills add ahmedtawfeeq1/loredex --skill loredex-routegit clone --depth 1 https://github.com/ahmedtawfeeq1/loredexWrote 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/skills/ahmedtawfeeq1/loredex/loredex-route)<a href="https://agentmods.dev/skills/ahmedtawfeeq1/loredex/loredex-route"><img src="https://agentmods.dev/badge/skills/ahmedtawfeeq1/loredex/loredex-route/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ahmedtawfeeq1/loredex/loredex-route"><img src="https://agentmods.dev/badge/skills/ahmedtawfeeq1/loredex/loredex-route.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00058 | $0.00248 |
| Opus 5.5 | $0.00023 | $0.00099 |
| Sonnet 5.5 | $0.00012 | $0.00050 |
| Haiku 4.5 | $0.00006 | $0.00025 |
Grade A, and why
loredex-route 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 1mo 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
/loredex-route
npx -y loredex@latest route [--from <dir>] [--strict] [--no-llm]
This is what the Claude Code Stop hook already runs automatically (with --strict) after
every session — you rarely need to call it by hand. Reach for it explicitly when:
- The user is on an agent without a hook (Cursor, Codex, etc.) and wants a pass right now.
- Files are sitting in the dex
_inbox/or a project'sdocs/unrouted. - They want to force classification of unlabeled files (omit
--strictso the LLM/heuristic classifier runs, not just frontmatter-complete files).
Run --dry-run first if the user hasn't already reviewed what's pending — route writes
immediately otherwise.
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.
- 1mo ago First seen · 22 lines · 58 tokens per session scan A 8ffa906d5439
loredex-route is a skill published in the GitHub repository ahmedtawfeeq1/loredex (2 stars, last pushed 3mo ago), licensed MIT. It adds 58 tokens to every session and 248 once invoked, about $0.0002 per session on Opus 5.5. 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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debrief
Use at the end of a session (or midway through a long one) to deliberately capture what was learned — lessons, decisions, workflows, state changes, and facts — into the knowledge base. The nightly harvest is a safety net for lessons; unless the host opted into KBHARVESTFACTS, /debrief is the only thing that records…
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Use before starting any non-trivial task to search the knowledge base for context, and after completing work to capture findings. Teaches the token-efficient retrieval pattern and self-learning loop.