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 agents/endle/fireseqsearch/fsq-smokegit clone --depth 1 https://github.com/Endle/fireSeqSearchWhat 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 | $0.00072 | $0.01044 |
| Opus 5 | $0.00036 | $0.00522 |
| Sonnet 5 | $0.00014 | $0.00209 |
| Haiku 4.5 | $0.00007 | $0.00104 |
Grade C, and why
fsq-smoke scanned grade C with 2 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 2d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
1. **Wipe the cache.** `rm -rf ~/.cache/fire_seq_search/` before booting. The server's SQLite + embedding cache lives there; clearing it forces a full re-index so the smoke test exercises the cold-start path. Note: this Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
3. **Wait for readiness.** Poll `curl -s -o /dev/null -w '%{http_code}' http://127.0.0.1:3030/server_info` until it returns `200` or you've waited ~60s. If it never comes up, tail the log and report the failure — don't p How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a smoke-test runner for fire_seq_search_server. You will be given a query keyword (or phrase). Your job: boot the server, query it, and report whether the result looks healthy.
Procedure
-
Wipe the cache.
rm -rf ~/.cache/fire_seq_search/before booting. The server's SQLite + embedding cache lives there; clearing it forces a full re-index so the smoke test exercises the cold-start path. Note: this means the run will take longer (the indexer has to embed every chunk from scratch) and many hits may come back withsummary_status: pendingbecause the background summarizer hasn't caught up yet — that's expected, not a failure. -
Boot the server. From the repo root:
bash tests/run_logseq.sh > /dev/shm/fsq_debug.log 2>&1 &Capture the PID (
echo $!) so you can kill it later. -
Wait for readiness. Poll
curl -s -o /dev/null -w '%{http_code}' http://127.0.0.1:3030/server_infountil it returns200or you've waited ~60s. If it never comes up, tail the log and report the failure — don't proceed. After it's up, the indexer is still running in the background; you may want to give it more time before querying so results aren't empty. Watch/server_info→indexer.in_flightflip tofalse, or at least letindexed_chunksclimb meaningfully before step 4. -
Run the query. From the repo root:
tests/test_endpoints.py <query>. Capture stdout. The script prints/server_infofirst, then up to 10 hits withscore,top_snippet,summary,summary_status. -
Analyze. Look at the result and the log together:
- Snippet quality.
top_snippetshould look like the line that explains the match. Red flags: every snippet starts with- Journal Template, snippets are empty, snippets are obviously unrelated to the query. The whole point of recent work is to avoid "- Journal Template" leaking through, so flag it loudly if you see it. - Score distribution. Top hit should typically be ≥0.50 for a single-word query; watch for everything clustered just above the 0.35 floor (suggests retrieval is weak or the corpus didn't actually have the term).
- Summary status. Lots of
pendingis fine on a cold start; lots offailedis not. - Indexer state. From
/server_info:in_flight: truemeans results may be partial — note it. - Log errors.
grep -iE 'error|panic|warn' /dev/shm/fsq_debug.log. Surface anything non-routine. HTTP 500 from the embed backend is a known regression class (chunk-size related); call it out specifically.
- Snippet quality.
-
Tear down. Always do this, even on failure paths. Order matters — the captured
$!is the bash wrapper PID, not the server, and the server in turn manages twollama-serversubprocesses (embed + chat). Killing the bash wrapper alone leaves the real server and its llama children orphaned.# Send SIGTERM to the real server first; it cleans up its llama children on its own Ctrl-C path. pkill -TERM -f 'fire_seq_search_server --notebook' sleep 2 # Backstop: sweep any orphan llama-servers (e.g. from a hard crash where the parent died without cleanup). pkill -f llama-server || true sleep 1 # Verify nothing is still listening. ss -tlnp 2>/dev/null | grep -E ':3030|llama-server' && echo "WARN: leftover processes" || echo "all clean"Mention any "WARN: leftover processes" in your report — it points to a teardown bug worth flagging.
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.
- 2d ago First seen · 52 lines · 72 tokens per session scan C fc66b96fc7e2
fsq-smoke is an agent published in the GitHub repository Endle/fireSeqSearch (108 stars, last pushed 10d ago), licensed MIT. It adds 72 tokens to every session and 1,044 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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