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 hyunjae-labs/lore --skill lore-searchgit clone --depth 1 https://github.com/hyunjae-labs/loreWrote 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/hyunjae-labs/lore/lore-search)<a href="https://agentmods.dev/skills/hyunjae-labs/lore/lore-search"><img src="https://agentmods.dev/badge/skills/hyunjae-labs/lore/lore-search/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/hyunjae-labs/lore/lore-search"><img src="https://agentmods.dev/badge/skills/hyunjae-labs/lore/lore-search.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 | $0.00147 | $0.01437 |
| Opus 5 | $0.00073 | $0.00718 |
| Sonnet 5 | $0.00029 | $0.00287 |
| Haiku 4.5 | $0.00015 | $0.00144 |
Grade A, and why
lore-search 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 10d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lore Search
You're searching a hybrid BM25 + semantic index of past Claude Code and OpenAI Codex CLI conversation turns. The index contains multilingual content organized as session chunks with metadata (project, branch, timestamp, model, intent).
Projects come from two sources:
- Claude Code: dirName like
-Users-foo-01-projects-my-app(mirrors~/.claude/projects/) - Codex CLI: dirName like
codex--Users-foo-01-projects-my-app(grouped bycwdfrom each session'ssession_metaline)
Sessions for the same cwd are split across the two dirNames — search both if you want full coverage, or filter to one to scope by agent.
Step 1: Clarify Intent Before Searching
Do not search immediately. The user's request often contains implicit scope that, if clarified, dramatically improves results. Ask about any of these that aren't obvious from context:
- Which project? If you know the current project, confirm whether to search just this project or across all indexed projects.
- When? Ask if the user remembers roughly when the work happened — convert to
before/afterfilters. - Which branch? If relevant to the work context.
- How broad? A single conversation vs. a broad topic across many sessions.
If the user's intent is already crystal clear (e.g., they name a specific feature and project), skip clarification and search directly.
Step 2: Formulate Queries
Dual-Format Strategy
Every query must serve BOTH retrieval engines simultaneously. BM25 needs exact token matches; semantic search needs dense meaning.
Structure: [keyword anchors] + [semantic phrase]
Bad: "CircuitBreaker" (BM25-only, no semantic signal)
Bad: "the safety system that stops trading" (semantic-only, no keyword anchor)
Good: "CircuitBreaker daily loss limit HALF_OPEN recovery mechanism"
Keyword anchors — terms that actually appeared in conversations:
- File/class/variable names:
backtest_ab_comparison.py,StackingEnsemble - Error messages:
SIGSEGV,horizon mismatch - Tool/library names:
XGBoost,LightGBM,Optuna
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.
- 10d ago First seen · 128 lines · 147 tokens per session scan A 598c9e77ebcb
lore-search is a skill published in the GitHub repository hyunjae-labs/lore (8 stars, last pushed 4mo ago), licensed MIT. It adds 147 tokens to every session and 1,437 once invoked, about $0.0007 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.
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