Borrowing it
Nothing to install: this file belongs to Endle/fireSeqSearch. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Endle/fireSeqSearch/master/CLAUDE.mdgit clone --depth 1 https://github.com/Endle/fireSeqSearchWrote 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/instructions/endle/fireseqsearch/claude-md)<a href="https://agentmods.dev/instructions/endle/fireseqsearch/claude-md"><img src="https://agentmods.dev/badge/instructions/endle/fireseqsearch/claude-md/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/instructions/endle/fireseqsearch/claude-md"><img src="https://agentmods.dev/badge/instructions/endle/fireseqsearch/claude-md.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 | $0.02621 | $0.02621 |
| Opus 5.5 | $0.01048 | $0.01048 |
| Sonnet 5.5 | $0.00524 | $0.00524 |
| Haiku 4.5 | $0.00262 | $0.00262 |
Grade A, and why
fireSeqSearch CLAUDE.md 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 2mo 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fireSeqSearch
Local search server + browser extension that appends hits from your Logseq/Obsidian notebook to Google search results. Architecture is LLM-first: dense semantic retrieval over the user's notes plus per-page LLM-generated summaries, with the goal of making search results explain themselves at a glance.
Surface area
/query/:term— semantic search, sub-second. Returns rankedPageHits (title,logseq_uri,score,chunk_id,top_snippet,summary,summary_status).POST /ask— deliberate Q&A. Body{question, k?}; SSEmeta(sources +confidence) →delta*→done({cited, invalid, chars, answered, confidence}) orerror. Cited[N]markers validated against the retrieved set; anything invented lands indone.invalid.POST /reindex— manual rescan trigger./server_info— config + indexer/summarizer counts + crate version + capabilities. Addon hard-floors onMIN_BACKEND_VERSIONand soft-gates UI oncapabilities.POST /highlight— dormant; kept as scaffolding for a future "explain this card" action.
Locked decisions — don't relitigate without strong evidence
Retrieval
- Embedding:
bge-m3(1024-dim, multilingual, 8K context), Q4_K_M GGUF. Chosen to take retrieval quality off the debug list. - Index: flat in-memory
Vec<(ChunkId, [f32; 1024])>, brute-force cosine. No ANN, no vector DB. ~10K chunks fits under 50ms. - Storage: SQLite (
notes+chunks, raw f32 LE BLOBs). Storage, not a search index — all rows hydrate into the in-memory vec on startup. - Change detection: mtime as fast filter, Blake3
content_hashas truth. - Dual signal:
note_score = max(best_chunk · query, summary · query). Pages whose gist matches rise even when no individual chunk does. CHUNKER_VERSIONinstore.rs— bump it when chunker logic changes. Stale chunks otherwise hash-match and skip re-embedding forever.
Chunking (flavour-aware via --notebook obsidian)
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.
- 2mo ago First seen · 181 lines · 2,621 tokens per session scan A 614302737743
fireSeqSearch CLAUDE.md is an instructions file published in the GitHub repository Endle/fireSeqSearch (108 stars, last pushed 2mo ago), licensed MIT. It adds 2,621 tokens to every session, about $0.0105 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 2026-08-30.
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