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 agentsope/SkillAlchemy --skill agentsop-hybrid-retrievalgit clone --depth 1 https://github.com/agentsope/SkillAlchemyWrote 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/agentsope/skillalchemy/agentsop-hybrid-retrieval)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-hybrid-retrieval"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-hybrid-retrieval/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/agentsope/skillalchemy/agentsop-hybrid-retrieval"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-hybrid-retrieval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00173 | $0.06724 |
| Opus 5 | $0.00086 | $0.03362 |
| Sonnet 5 | $0.00035 | $0.01345 |
| Haiku 4.5 | $0.00017 | $0.00672 |
Grade A, and why
agentsop-hybrid-retrieval 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 — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hybrid Retrieval · Dense + Sparse SOP
Third-person operating model for a coder agent that owns retrieval recall on a corpus where both meaning and exact tokens matter. The audience is the LLM agent writing or reviewing retrieval code — not an end user.
One sentence: Dense captures meaning, sparse captures exact tokens; hybrid wins when both matter — but only fuse them when traffic actually carries exact-match queries, and tune the blend per query type or hybrid loses to dense.
1. 何时激活 (Activation Rules)
Activate this skill when any of the following holds:
- The corpus contains exact-match tokens that a query may reference verbatim:
error codes (
ERR_SSL_PROTOCOL), SKUs / part numbers (A1-2293-X), API or function names (as_query_engine), proper nouns, legal/medical citations (42 U.S.C. § 1983), version strings, rare jargon, ticket IDs. - A bug report says "I searched the exact code/name/string and got nothing", or "the right document exists but dense retrieval ranks it below fuzzy near-misses".
- PR review surfaces a retriever serving lexical-identity traffic but wired
dense-only (
index.as_retriever(...)/similarity_search(...)with no sparse leg). - You are tuning recall and have already exhausted the cheap dense knobs (prompt, embedding model, chunk size) per the [[llamaindex]] optimization ladder — hybrid is the next rung.
- The user mentions hybrid search, BM25, sparse retrieval, RRF,
QueryFusionRetriever,EnsembleRetriever, or vector-store-native hybrid (Qdrant/Weaviate/Pinecone).
Do not activate when:
- Traffic is purely semantic ("what does X mean?", "summarize the policy") with a lexical-identity share <5% — adding BM25 doubles index footprint for no gain.
- The corpus has no stable identifiers and no query ever quotes an exact string.
- No dense baseline + eval loop exists yet. Hybrid is a Stage-3 optimization ([[llamaindex]] Stage 3 step 4): baseline and measure before fusing.
What ships with it
3 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.
- 10d ago First seen · 509 lines · 173 tokens per session scan A 0f5f15aa2ade
agentsop-hybrid-retrieval is a skill published in the GitHub repository agentsope/SkillAlchemy (385 stars, last pushed 7d ago), licensed MIT. It adds 173 tokens to every session and 6,724 once invoked, about $0.0009 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 2026-08-30.
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