Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Jessinra/Lorekeepernpx agentmods add skills/jessinra/lorekeeper/lorekeeper-devWrote 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/jessinra/lorekeeper/lorekeeper-dev)<a href="https://agentmods.dev/skills/jessinra/lorekeeper/lorekeeper-dev"><img src="https://agentmods.dev/badge/skills/jessinra/lorekeeper/lorekeeper-dev/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/jessinra/lorekeeper/lorekeeper-dev"><img src="https://agentmods.dev/badge/skills/jessinra/lorekeeper/lorekeeper-dev.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 | $0.00076 | $0.01570 |
| Opus 5.5 | $0.00030 | $0.00628 |
| Sonnet 5.5 | $0.00015 | $0.00314 |
| Haiku 4.5 | $0.00008 | $0.00157 |
Grade A, and why
lorekeeper-dev 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.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lorekeeper Dev
Practices and conventions for developing the Lorekeeper MCP server.
Architecture
Two vector store backends (switch via LORE_VECTOR_STORE):
- LanceDB (default) — concurrent multi-process, no lock files.
LanceDBEngineinlancedb_engine.py. Uses sentence-transformers directly, no Mem0 for vectors. - Chroma (fallback,
LORE_VECTOR_STORE=chroma) — Mem0-backedChromaDBEngineinchromadb_engine.py. Single-process only. - SQLite sidecar — memory metadata (
score,confidence,soft_deleted,usage_count), allMemoryLinkrows, BM25 index source. Shared by both backends.
Engine factory: engine_factory.py → build_engine(). Orchestrator: MemoryService in orchestrator.py.
Canonical identity: lore_id UUID lives in the vector store's metadata. All app logic uses lore_id.
Hybrid Scoring Formula
combined = 0.45·semantic + 0.30·keyword + 0.15·(score/10) + 0.10·log_usage_norm
Weights are env-configurable (LORE_W_*). Dedup threshold: 0.6·semantic + 0.4·keyword >= 0.85.
Known Quirks
Chroma distance vs. similarity (critical): Mem0 v2 score_and_rank receives Chroma cosine distances but treats them as similarities. Fix: bypass mem0 pipeline — embed directly with SentenceTransformer, query directly, return score = 1.0 - distance.
LanceDB: Always returns cosine distance (lower=better). normalize_score() converts to similarity: 1.0 - distance.
infer=False on every mem0.add() call — text stored verbatim, no LLM extraction.
stdout reserved for MCP protocol — all logging to stderr via structlog.
Tooling
uv run pytest # run tests
uv run pytest tests/ -x -q # fail-fast
uv run ruff check src tests # lint (Python)
uv run ruff check src tests --fix # auto-fix lint
uv run mypy src # type check (run before push)
npx @biomejs/biome check src/lorekeeper/dashboard/static/js/ # lint (JS)
uv run lorekeeper # start server
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 155 lines · 76 tokens per session scan A a3468508bad8
lorekeeper-dev is a skill published in the GitHub repository Jessinra/Lorekeeper (5 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 76 tokens to every session and 1,570 once invoked, about $0.0003 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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