apex
無料Engineering lead — hand Apex any task and it routes internally. New features, planning, reviews, status, orientation, or system takeovers.
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新
無料GitHub で公開
embed-search
Optimize similarity search — ANN index tuning, hybrid search, reranking, query expansion. Use when asked to "improve similarity search", "tune the ANN index", or "add hybrid search".
含まれるファイル（1）
- SKILL.md1.8 KB
SKILL.md（原文）
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Embed Search
You are Embed — the Embeddings Engineer on the AI Operations Team.
Steps
Step 0: Baseline Current Quality
Measure current retrieval quality (recall@k, NDCG, or user-facing proxy) and latency on representative queries.
Step 1: Tune the Index
Adjust ANN index parameters (e.g. ef_search, nlist/nprobe) to trade off recall against latency, and evaluate hybrid (lexical + vector) search where pure vector search misses exact-match queries.
Step 2: Add Reranking or Query Expansion
If precision at the top of the results is still weak, add a reranking stage or query expansion, and re-measure.
Key Rules
- Follow the output format defined in docs/output-kit.md
- Every tuning change needs a before/after quality number — don't ship a parameter change on intuition
- Hybrid search is usually the fix for 'exact term not found' complaints — check that failure mode before reaching for a bigger model
- Reranking adds latency — confirm the latency budget allows it before recommending it
Output Format
A before/after quality and latency comparison, plus the specific index/reranking/query-expansion changes recommended.
Delivery
If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.
レビュー
まだレビューはありません。使ってみた感想をお寄せください。
同じリポジトリのスキル
概要と使いどころ
Session postmortem from local transcripts — why a run repeated work, ignored the plan, took too long, or cost more than expected. Use when asked "why did that take so long", "why was that so expensive", "what went wrong in that session", "why did the agent redo that", or when preparing a bug report about agent behavior.
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新
Inspect and tune the skill-manifest gate — which of the 421 tonone skills keep their description in this project's context, and what that costs in tokens. Use when asked "why can't Claude see this skill", "show the skill gate", "how many tokens do my skills cost", "trim the skill catalogue", or "undo the skill gate".
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新
Plan and scope a project — discovery, challenge assumptions, present XS-XXL depth options with token and cost estimates. Use when asked to "plan this", "scope this", "how should we build X", or when a new project/feature request comes in.
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新
Scope the tonone agent roster for this project — install a curated subset of agents instead of the full 100-agent bundle. Use when "cut down the agent list", "profile for this project", "too many agents", "only need the engineering core", or after apex-stats shows a roster that's mostly unused.
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新
Engineering lead reconnaissance — inventory the project before planning. Use when asked to "understand this project", "orient me on this codebase", "what's the state of the repo", "what's in progress", or before starting work on an unfamiliar codebase.
日本語の概要は準備中です。原文の説明を表示しています。
tonone-ai/tonone762026年10月5日 更新