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Results for “gate constants”

5 skills · sorted by relevance

Returns the frozen gate constants — question, header, option labels and descriptions, digest field-list, and preview content — for every catalog gate. Invoked by the team lead fresh on arrival at each gate and on pulse re-emissions, in the same turn as the render. The transport contract, three renders, partition rule, and authoring rubric live in swarm:workflow-rules.

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DheerG/swarms85Updated Jul 23, 2026

有馬記念分析システムのアーキテクチャ/レイヤー境界レビュー。docs/ARCHITECTURE.md のレイヤー構成(commands / domain / repositories / models / features / constants / database / utils / types)に対し、レイヤーディレクトリの意味と配置、参照系(queries)と更新系(aggregates)の分離、DB 接続の注入可能性とテスト容易性、リッチドメインモデルの維持(commands 肥大・ドメイン貧血症)、重み・閾値の constants 単一定義、ファイル配置と命名語彙の一致、不要な抽象化、種別分岐のポリモーフィズム化、生成引数の凝集(5個以上は分割シグナル)を検証。純粋性・依存方向の逆流・DB 接続の生成箇所は lint が error で担保するため扱わない。Use when user says 'アーキテクチャレビュー', 'レイヤー境界チェック', 'review architecture', or '依存方向の確認'.

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sogengineer/arima-analy4Updated Sep 21, 2026

Returns the universal governance spec for swarm team runs — hard rules, briefing templates, gate presentation contract, launch mechanics, and pulse setup. The canonical source: invoked by launch.md at Step 1 and by user-authored shortcut commands that cannot read launch.md directly. Per-gate constants live in swarm:gate-presentation.

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DheerG/swarms85Updated Jul 23, 2026

client-reverse

FreeSummary

Investigate API requests that fail to replay because of client-generated signatures or anti-bot tokens. Start with captured traffic, identify the blocking inputs or signing function, and validate a reproducible request for further security testing.

  • Investigate signature errors in replayed API requests
  • Trace signing functions and their inputs
  • Validate a reproduced signer against browser output
awarexone/Agentic-Bug-Hunter5.4KUpdated Oct 10, 2026

Check physical units, propagate measurement uncertainty through calculations, and format results with meaningful precision. Local tools also inspect Python code for unit and uncertainty mistakes and assess whether calculated magnitudes are physically plausible.

  • Convert units while carrying measurement uncertainty
  • Build an uncertainty budget from calibration data
  • Audit Python calculations for unit and uncertainty errors
K-Dense-AI/scientific-agent-skills53KUpdated Oct 19, 2026