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fieldflow-cli

Use FieldFlow to inspect and reduce noisy JSON CLI output before it reaches model context. Trigger for read-only external CLI tasks likely to return large structured output, especially logs, list, describe, get, read, query, search, metrics, or status commands from tools like gcloud, gh, kubectl, aws, or similar CLIs that can emit JSON. Prefer `fieldflow-cli inspect` first, then rerun with explicit `--field` selectors. Do not use for tiny local commands, text-only commands, or mutating commands unless explicitly asked.

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ダウンロード .skill 標準形式。system_prompt と model_config を収録し、任意の Agent で利用可能
.skill ファイルの system_prompt フィールドの実際の内容。
name fieldflow-cli description Use FieldFlow to inspect and reduce noisy JSON CLI output before it reaches model context. Trigger for read-only external CLI tasks likely to return large structured output, especially logs, list, describe, get, read, query, search, metrics, or status commands from tools like gcloud, gh, kubectl, aws, or similar CLIs that can emit JSON. Prefer `fieldflow-cli inspect` first, then rerun with explicit `--field` selectors. Do not use for tiny local commands, text-only commands, or mutating commands unless explicitly asked. FieldFlow CLI Use this skill to keep large JSON CLI output out of model context. Qualify The Command Use fieldflow-cli only when all of these are true: The command is read-only. The command is external or service-facing, not a tiny local shell command. The command can emit JSON on stdout. The expected output is likely large enough that raw output would pollute context. Do not use this skill for commands like pwd , date , ls , git status , rg , or any mutating command such as deploy , apply , delete , or create . Inspect First Run fieldflow-cli inspect before choosing selectors unless you already have a manifest for the exact same wrapped command. fieldflow-cli inspect --sample-items 100 -- <wrapped command > The inspect step writes a compact field catalog under .fieldflow/inspect/ and prints the manifest to stdout. Treat that manifest as the source of truth for valid selectors. The manifest is deterministic and intentionally small: path types It does not store raw command output. Pick Minimal Fields Choose the smallest field set that answers the user’s question. Prefer fields like: timestamps severity or status identifiers or names URLs concise message fields latency, count, or state fields Avoid broad selectors such as [] or whole nested objects unless the task truly needs them. Run The Reduced Command After choosing selectors, rerun the command through fieldflow-cli . fieldflow-cli \ --field "[].timestamp" \ --field "[].severity" \ --field "[].jsonPayload.message" \ -- \ <wrapped command > If the result is too narrow, broaden the selectors and rerun the reduced call. Do not fall back to raw output unless the user explicitly asks for it. JSON Output Rules Prefer the CLI’s native JSON mode: gcloud : --format=json kubectl : -o json gh : --json ... aws : JSON is already standard, or use --output json when needed If the command cannot emit JSON, do not use this skill. Gcloud Example For noisy Cloud Run request or error logs: fieldflow-cli inspect --sample-items 100 -- \ gcloud logging read \ 'resource.type="cloud_run_revision" AND resource.labels.service_name="program-api-service" AND severity>=ERROR' \ --project=train-3328b \ --freshness=24h \ -- limit =2000 \ --format=json Then reduce to the smallest useful fields, for example: fieldflow-cli \ --field "[].timestamp" \ --field "[].severity" \ --field "[].httpRequest.requestMethod" \ --field "[].httpRequest.requestUrl" \ --field "[].httpRequest.status" \ --field "[].httpRequest.latency" \ -- \ gcloud logging read \ 'resource.type="cloud_run_revision" AND resource.labels.service_name="program-api-service" AND severity>=ERROR' \ --project=train-3328b \ --freshness=24h \ -- limit =2000 \ --format=json
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フィールド 説明
formatフォーマット識別子(skill/v1)
skill_idスキル固有 ID
nameスキル名
versionバージョン
description説明
categoryカテゴリ(配列)
trigger_wordsトリガーワード
tagsタグ
sourceソース
source_urlソース URL(本ページ)
exported_atエクスポート日時(ダウンロード毎)
system_promptシステムプロンプト本文
model_configモデル設定:provider / model / temperature / max_tokens / top_p
examplesサンプル
install_guide各プラットフォームの導入説明(Coze / Dify / Claude / カスタム)
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.skill 標準形式。system_prompt と model_config を収録し、任意の Agent で利用可能 ダウンロード
.skillpro 拡張形式。scripts / tools / dependencies / hooks を含む ダウンロード
.json 純粋な JSON 出力。system_prompt とモデル設定のみ ダウンロード
Coze frontmatter 付き Markdown。Coze へのインポート用 ダウンロード
Dify Dify DSL。アプリ作成後にそのままインポート ダウンロード

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