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-reranker-stagegit 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-reranker-stage)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-reranker-stage"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-reranker-stage.svg" alt="Measured on agentmods" 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 | $0.00100 | $0.05438 |
| Opus 5 | $0.00050 | $0.02719 |
| Sonnet 5 | $0.00020 | $0.01088 |
| Haiku 4.5 | $0.00010 | $0.00544 |
Grade A, and why
agentsop-reranker-stage 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 4d 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 — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reranker Stage · SOP
Third-person analytical view of how a mature RAG pipeline thinks about the reranker. The skill is for an LLM agent that writes / reviews / debugs retrieval code — it teaches the cross-framework reranking discipline, not one vendor's API. For the per-framework API, descend to
[[llamaindex]](node postprocessors) or[[agentsop-hybrid-retrieval]](the recall stage that feeds the reranker).
This is the C4 gap skill in the Phase-D enhance pass. The reranker SOP
existed only buried inside [[llamaindex]] (OP-03 AddReranker, Stage 3 step 7,
anti-pattern A6). It is the highest-ROI single addition to a naive RAG
pipeline, so it earns a standalone overlay.
1 · 何时激活 (Activation Rules)
Activate when any holds:
- A RAG pipeline's answer quality has plateaued after the cheap knobs
(prompt, embedding model, chunk size) are exhausted —
[[llamaindex]]Stage 3 lists reranking as the last optimization step, deliberately. - Diagnostics show the relevant document is in top-k but buried — high
hit-rate, low MRR, wrong top-1. This is LlamaIndex failure modes #1 / #10
([[llamaindex]]
OP-03). - The LLM context window is under pressure — too many marginal chunks inflate cost, latency, and "lost-in-the-middle" degradation. A reranker lets you retrieve 50 and feed 5.
- A user asks where to add a reranker, how to tune N vs k, or API vs local.
Do not activate (boundary — see §6):
- Recall is the bottleneck: the right doc is not in top-N at all. A
reranker can only reorder what retrieval already found — fix retrieval,
hybrid (
[[agentsop-hybrid-retrieval]]), or chunking first. - top-k is already small (≤5) and answers are correct — no plateau.
- A hard sub-100ms path where the extra round-trip is unaffordable and quality is already acceptable.
2 · 核心心智模型 (Core Mental Model)
The one sentence
Retrieve wide for recall with a cheap bi-encoder; rerank narrow for precision with an expensive cross-encoder that sees query + document together — something the bi-encoder structurally could not do.
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.
- 4d ago Changed · -19 lines · -282 tokens per session 753b2f85cb34
- 8d ago First seen · 398 lines · 382 tokens per session scan A 93ddb1c8665a
agentsop-reranker-stage is a skill published in the GitHub repository agentsope/SkillAlchemy (370 stars, last pushed 6d ago), licensed MIT. It adds 100 tokens to every session and 5,438 once invoked, about $0.0005 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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