sero-humanize
Audit or edit Sero Markdown documentation and other product prose to remove AI-writing patterns while preserving technical meaning, product terminology, links, examples, and the repository voice. Use when the user asks to humanize, de-slop, tighten, simplify, rewrite, or audit Se
供給アセットの概要
リサーチとナレッジ作業
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
シナリオ
リサーチ Agent
I need my agent to research a topic, compare sources, and produce a concise report.
Agent 適合
Claude Code + CLI + Codex
Codex、Claude Code、Cursor、CLI、またはカスタム Agent に対応します。
インストール
準備完了
npx skills add sero-labs/sero --skill sero-humanize
メンテナンス
新しい
最終プッシュから 3 日
リスク
要レビュー
Permission surface may require sandboxing
GitHub 品質
19
60/100 品質 · 67/100 信頼
対象タグ
レビュー注記
Permission surface may require sandboxing · SKILL.md excerpt is truncated in the provided documentation, but the visible portion is thorough and well-structured.
Agent 導入スコアカード
信頼、監査、インストール準備状況を一目で確認
公開リポジトリのメタデータ、OpenAgentSkill のレビューシグナル、保守の鮮度、インストール準備状況を組み合わせたスコアです。候補選定の目安であり、人によるレビューの代替ではありません。
品質
有望有用な候補ですが、採用前に代替と比較してください。
信頼
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
監査
要レビューインストール準備、安全メタデータ、保守、採用リスクの機械可読なレビュー。
OpenAgentSkill Trust Score v5
インストール前に人のレビュー
Choose a stronger alternative or inspect the source manually before any install attempt.
スター
GitHub スター 19
リポジトリ活動
スター 19、フォーク 1
メンテナンス
最終プッシュから 3 日
ライセンス
Apache-2.0
インストール
npx skills add sero-labs/sero --skill sero-humanize
インストール安全性
標準パッケージまたはランタイムのインストールパス
権限範囲
secrets or environment access, shell or command execution
Agent の成果
Agent の成果データはまだありません
ドキュメント
README/SKILL.md の文脈が十分です
リスク概要
本番前にレビュー
- SKILL.md excerpt is truncated in the provided documentation, but the visible portion is thorough and well-structured.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
インストール準備状況
インストールパスを利用可能
- インストールパスを利用できます
- リポジトリの根拠を利用できます
- ライセンスが明示されています
- Agent-Proven の成果エビデンスはまだありません
Agent 可読メタデータ
このスキルの機械可読な判断データ。
このブロックまたは埋め込み JSON を使い、Agent がこのスキルをインストールすべきか、代替を選ぶべきか、先に人のレビューを求めるべきかを判断できます。
適したタスク
- GitHub automation ワークフロー
- Claude Code チーム
- builders willing to evaluate younger projects
- Inspect repository metadata
適した Agent
インストール判断
- コマンド
- npx skills add sero-labs/sero --skill sero-humanize
- ポリシー
- ブロック
- 人によるレビュー
- はい
信頼とリスク
- 信頼
- 59/100
- 監査
- 74/100
- リスクレベル
- 要レビュー
成果ループ
- エンドポイント
- /api/agent/outcome
- イベント ID
- resolve
- 成果
- 5
使わない場合
- ベンダー提供の SLA が必要なチーム
- production agents without a repository review
- Low GitHub adoption signal
- SKILL.md excerpt is truncated in the provided documentation, but the visible portion is thorough and well-structured.
- 高リスク権限のヒント: Shell or command execution, Secrets or environment access
Agent セーフティ v2
34/100 · 自動インストールを避ける
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
高
Shell またはコマンド実行
Skill メタデータに端末、CLI、Shell、サブプロセス、またはコマンド実行のワークフローが含まれます。
中
ネットワークアクセス
Skill はリモートページ、API、リポジトリ、外部サービスにアクセスする可能性があります。
中
ファイルシステムアクセス
Skill はプロジェクトファイル、ドキュメント、生成物、ローカルワークスペース状態を読み書きする可能性があります。
高
Secrets or environment access
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
- 高リスク権限のヒント: Shell or command execution, Secrets or environment access
- Permission surface may require sandboxing
インストール先
Agent ワークフローにこのスキルをインストール
公開インストールエンドポイントからコマンド、安全チェックリスト、対象プロンプト、正規リンクを取得します。
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install sero-labs-sero-humanizeAgent 解決プラン
インストール前に Agent に適合性を検証させます。
Resolve API は第一候補、代替、安全ポリシー、監査メモ、インストール先、Agent がそのまま使えるプロンプトを返します。
JSON を開く
/api/agent/resolve?task=Use%20sero-humanize%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve テキスト
/api/agent/resolve?task=Use%20sero-humanize%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
インストール引き継ぎ
/api/skills/sero-labs-sero-humanize/install
Agent が確認すべきこと
- Resolve API でタスク適合と代替を確認。
- 監査・信頼スコアと安全ポリシーの警告を確認。
- Codex、Claude Code、Cursor、CLI のインストール先互換性を確認。
プロンプトをコピー
Task: Use sero-humanize in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20sero-humanize%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/sero-labs-sero-humanize/install
Install command: npx skills add sero-labs/sero --skill sero-humanize
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent 引き継ぎ
別のディレクトリではなく、インストール経路を Agent に渡します。
公開インストールエンドポイントからコマンド、安全チェックリスト、対象プロンプト、正規リンクを取得します。
インストール引き継ぎ
/api/skills/sero-labs-sero-humanize/install
LLM テキスト形式
/api/skills/sero-labs-sero-humanize/install?format=text
代替を探す
/api/skills/search?q=sero-humanize&limit=3
Agent プロンプト
Use sero-humanize for this task. Review https://www.openagentskill.com/api/skills/sero-labs-sero-humanize/install, then install with: npx skills add sero-labs/sero --skill sero-humanizeRegistry メタデータ
自動スキル選択用の Agent 可読プロファイル。
Registry API 経由で判断、信頼、監査、ユースケース、インストールのシグナルを提供し、UI をスクレイピングせずに Agent が順位付けできます。
Manifest
/api/registry/manifest/sero-labs-sero-humanize
LLM テキスト
/api/registry/manifest/sero-labs-sero-humanize?format=text
インストール別名
/api/registry/install/sero-labs-sero-humanize
推奨
/api/registry/recommend?task=Use%20sero-humanize%20in%20an%20agent%20workflow&limit=3
Agent 適合
GitHub automation
プラットフォーム
Claude Code
Agent 判断パネル
Fallback candidate for GitHub automation
まずこのスキルでプロトタイプを作り、代替候補を用意してください。
スタック内の役割
代替候補
主な適合
GitHub automation
信頼ラベル
まずプロトタイプ
インストールパス
コマンド準備済み
使う場面
- GitHub automation ワークフロー
- Claude Code チーム
- builders willing to evaluate younger projects
根拠
- 最近のリポジトリ活動
- インストールコマンドまたは GitHub リポジトリが利用可能
- 品質プロファイル 60/100
- OpenAgentSkill エンゲージメント 6 件
先にレビュー
- Low GitHub adoption signal
- SKILL.md excerpt is truncated in the provided documentation, but the visible portion is thorough and well-structured.
実装パス
- 1サンドボックスの Agent にインストールし、GitHub automation タスクを一度最初から最後まで実行します。
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
信頼プロファイル
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub 採用度
修正GitHub スター 19
スター/フォーク活動
修正スター 19、フォーク 1; 現在のメタデータでは Issue 活動を利用できません
最近のメンテナンス
合格最終プッシュから 3 日
ライセンスの明確さ
合格Apache-2.0
良いシグナル
- AI レビュー承認済み
- インストールパスを利用できます
- リポジトリの根拠を利用できます
- 最近保守されたリポジトリ
- インストールコマンドに明確な高リスクパターンはありません
- 成果ループは準備済みですが、最初の実行が必要です
インストール前にレビュー
- SKILL.md excerpt is truncated in the provided documentation, but the visible portion is thorough and well-structured.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 19 GitHub stars
- Stars/forks activity: 19 stars, 1 forks; issue activity unavailable in current metadata
- Permission surface: secrets or environment access, shell or command execution
- 実際の Agent 成果レポートはまだありません
- 無人インストールの前に人によるレビューが必要です
推奨アクション
Choose a stronger alternative or inspect the source manually before any install attempt.
品質プロファイル
有望 Agent ワークフロー向けの候補
有用な候補ですが、採用前に代替と比較してください。
ワークフロー適合
このスキルを使うシナリオ
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Process rich media
Multimodal media
I need my agent to process images, video, or audio and extract useful information.
ワークフロー適合
完全なワークフローに追加
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
RAG knowledge base
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
代替候補
インストール前に比較
このタスクに適する可能性のある類似スキル。
Wazuh
Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
Maigret
🕵️♂️ Collect a dossier on a person by username from 3000+ sites
Nuclei
Nuclei is a fast, customizable vulnerability scanner powered by the global security community and built on a simple YAML-based DSL, enabling collaboration to tackle trending vulnerabilities on the internet. It helps you find vulnerabilities in your applications, APIs, networks, DNS, and cloud configurations.
Infisical
Infisical is the open-source platform for secrets, certificates, and privileged access management.
概要
--- name: sero-humanize description: | Audit or edit Sero Markdown documentation and other product prose to remove AI-writing patterns while preserving technical meaning, product terminology, links, examples, and the repository voice. Use when the user asks to humanize, de-slop, tighten, simplify, rewrite, or audit Sero documentation, README text, UI copy, release notes, plans, or specifications for AI tells. Also use when prose needs ASD-STE100 Simplified Technical English. Do not use for code review or for creative and promotional writing. ---
# Sero Humanize
Make Sero prose direct, specific, and useful. Human writing in technical documentation does not need personality. It needs clear decisions, concrete facts, and respect for the reader's time.
## Follow the requested mode
- For an audit or review, report the material patterns and do not edit files. - For an edit, rewrite the named files in place. - For a new document, apply these rules while drafting it. - If the request does not specify a mode, infer it from the requested action. Do not turn a request to assess prose into permission to change it.
## Establish the voice and preservation set
Before editing:
1. Read every file in scope in full. 2. Read the nearest repository instructions that apply to those files. 3. Use adjacent, clearly human-edited Sero documentation as the voice sample when the named files do not establish a consistent voice. 4. Record what must not change: - technical meaning and product behaviour; - product names, canonical terms, and exact UI labels; - commands, code, file paths, numbers, limits, and factual claims; - the documentation type, useful narrative flow, and balance between prose, lists, tables, examples, and callouts; - frontmatter, anchors, link targets, image paths, and screenshot order; - the purpose and placement of each image, diagram, and other media asset; - quotations and user input examples, unless the user asks to edit them.
Identify the intended reader. Unless the page states otherwise, assume the reader knows neither Sero nor the feature. Do not assume that simpler grammar fixes an explanation that requires missing product knowledge.
Do not add a fact to make a sentence more vivid. Verify a doubtful claim from the repository or leave it unchanged and report the doubt.
## Audit structure before wording
Look for clusters and repeated patterns. Do not treat one punctuation mark or one common word as proof of AI writing.
Prioritize these defects:
- Meta narration that announces the next explanation instead of giving it. - Repeated tutorial staging such as "what you are about to learn" and "what you have learned." - Fixed enumerations such as "three things are worth noticing" when a direct heading or short list is clearer. - Several paragraphs that can change order without changing the argument. - A heading followed by a sentence that only repeats the heading. - A conclusion that repeats the introduction without adding an action or fact. - Repeated summaries of the same screen, process, or result. - Forced contrasts such as "not only X, but Y" or "not X; rather Y." - Groups of three used for rhythm instead of meaning. - Mechanical bold lead-ins, excessive inline bold, or lists that should be short prose. - Promotional adjectives, vague importance claims, and unsupported praise. - Vague actors, passive constructions, filler, stacked hedges, and abstract nouns where an action is available. - Synonym cycling for one product concept. Repeat the canonical term. - Long sentences that mix instructions, exceptions, and background. - Em dashes used repeatedly to join thoughts that need separate sentences. - Headings that narrate the demo or expose implementation language instead of naming the reader's task, such as "The finish" or "Answer the gate." - Examples that depend on an unexplained demo domain and therefore do not help the reader understand the feature. - Tutorials that start using the product before they give prerequisites, sample data, expected starting state, or required sign-ins. - Result sections that recite one captured run instead of telling the reader what to inspect and verify.
Keep useful structure. A list, summary, warning, question heading, or em dash is not a defect by itself.
Do not normalize a page or site to one format. Humanize the defective passages, not every paragraph. If a page already explains a concept well in prose, keep it as prose.
## Rewrite for Sero documentation
Apply ASD-STE100 Simplified Technical English where it fits the material:
- Put the action or answer first. - Use active voice when the actor matters. - Give one main instruction per sentence. - Put a condition before the action when the reader must know it first. - Prefer common, precise words over formal or promotional alternatives. - Use the same term for the same thing. - Keep paragraphs focused on one subject. - Keep necessary limits, cautions, and exceptions close to the action. - Use contractions only when the established local voice requires them. - Keep exact UI text in bold when the documentation uses bold for controls. - Keep code identifiers and paths in code formatting. - Retain a summary only when it helps the reader decide or act.
Use lists only when the content is naturally a sequence, set of choices, checklist, or compact reference. Do not:
- convert explanatory prose into bullet points only to make it shorter; - turn each sentence or paragraph into a list item; - replace transitions and reasoning with disconnected bullets; - use repeated lists where a short paragraph gives the reader necessary context; or - make several pages share the same mechanical list structure.
After the sentence pass, read the page as a whole. If lists now dominate a page that previously used useful prose, restore the prose. Clear technical writing needs connected explanation as well as scannable reference material.
For an overview page:
- Explain the feature in familiar words before using its product terms. - State what the user gives Sero, what Sero does, and what the user reviews. - When comparing features, give one plain decision rule. Use examples that a reader can understand without knowing the tutorial repository or a specialist software domain.
For a tutorial:
- Put setup before the first product action. Include required software, accounts, sign-ins, repository or sample-data setup, and a command or visible result that confirms the expected starting state. - Prefer a stable sample repository over instructions that ask an agent to generate approximate sample data. Verify the repository contents and commands before documenting them. - Use task-based headings such as "Review the plan," "Change the plan," and "Check the result." A heading must describe the full purpose of its section; do not narrow a general control to one example case. - End with checks the reader can perform. Do not use a captured run's cost, duration, names, or outcome as a substitute for verification instructions.
For feature language:
- Use the visible object name: icon, button, tab, question, or approval request. Do not call an icon a mark or expose internal terms such as gate, fan-out, or feedback route when plain behaviour is enough. - Keep exact UI labels unchanged, but explain them with common words. - Put high-value quality-of-life features where readers will find them. Give them enough space to explain when the control appears, how to use it, what it changes, and what remains under user control.
Compress or merge only the passages that contain a verified structural defect. Keep useful depth, examples, transitions, and paragraph structure. A shorter page is not automatically a better page.
Do not manufacture a human voice with:
- anecdotes, opinions, jokes, sensory details, or personal asides; - fragments, one-word sentences, or dramatic punch lines; - arbitrary sentence-length variation; - unusual synonyms chosen only to make wording less predictable; - metaphors that replace a precise technical explanation; - deliberate imperfections or tangents; - an invented AI probability or numerical slop score.
## Preserve Markdown and product accuracy
- Do not change fenced code, commands, URLs, link targets, image targets, or frontmatter unless the request requires it. - Do not rename a heading if another page links to its generated anchor without updating that link. - Do not change a UI label to improve prose. Rewrite the surrounding sentence. - Do not remove repetition that is required for independent reference sections. - Do not convert a walkthrough into reference documentation, or reference documentation into a narrative tutorial, without user approval. - Do not infer product behaviour from the prose alone when the edit changes a technical claim. Check the implementation or an authoritative reference. - Treat contradictions between prose, screenshots, capture metadata, sample repositories, and implementation as accuracy defects. Resolve them from the authoritative source instead of rewriting around them. - When a page title changes, update the sidebar, index, related-page labels, and in-scope links that display the old title.
## Preserve images and other media
Treat every existing image, diagram, video, and asset as preserved content. Humanizing prose does not authorize media removal or replacement.
- Do not delete an asset, remove its reference, change its order, or replace it unless the user explicitly approves that action. - Do not use "task value," brevity, a stale appearance, or a text explanation as automatic reasons to remove an image. - Do not bulk-delete assets during a prose revision. - If an image is stale, private, inaccurate, decorative, or duplicated, report the issue and propose one action: keep, recapture, move, or remove. Wait for approval before changing it. - If an image exposes a credential or other active secret, stop publication and report it immediately. Do not silently make a wider set of image changes. - When a replacement is approved, capture or obtain the replacement before removing the current asset. Preserve the route and layout while replacement work is pending. - Check non-doc consumers before changing an asset. README files, homepages, package pages, and other applications can import docs-site images directly.
A decision not to add a new screenshot is not permission to remove an existing screenshot.
## Control the size of the rewrite
For a large documentation set, work in reviewed vertical slices. Complete and review one representative page before applying the approach to the rest of a slice. Do not perform a site-wide structural rewrite from an audit summary.
Pause and ask for approval when the work would:
- change the dominant format of a page, such as prose to lists; - remove substantial explanation, examples, or media; - merge, tombstone, redirect, or delete a page; - change many pages through the same structural template; or - produce a much larger diff than the factual and prose defects require.
When several agents contribute, give them the same preservation set and require a central review of format balance and media changes before integration.
## Use a two-pass edit
### Pass 1: structure
Remove redundant framing, merge repeated explanations, order information by the reader's task, and keep prerequisites before dependent actions. Give prominent placement to features that materially improve repeated use; do not give every feature equal weight merely because the source page did.
Keep the smallest effective structural change. Do not rewrite a complete page when a heading, transition, or paragraph edit fixes the defect.
### Pass 2: sentences
Remove filler and AI mannerisms. Simplify grammar. Keep terminology and facts stable. Read the result as technical documentation, not as marketing copy.
Then compare the result with th
技術詳細
- バージョン
- 1.0.0
- ライセンス
- Apache-2.0
- 最終更新
- 2026年8月19日
- 公開日
- 2026年8月19日
判断の要約
代替候補
最近のリポジトリ活動
Agent 実証エビデンス
Agent 実証エビデンス
Resolve、レビュー、インストール、限定実行後の成果レポート。
- 成功率
- —
- 直近の失敗
- —
- 成果
- 0
- 出力品質
- —
- 失敗
- 0
- 非該当
- 0
- インストール数
- 0
- リスクによりブロック
- 0
- 設定が必要
- 0
- 本番
- 0
Agent の実行結果はまだありません。最初の実行では /api/agent/outcome を通じて成功、設定要件、リスクによるブロック、失敗、非該当を報告できます。
成長ループ
共有キット
sero-humanize 用のシナリオベース草案です。X へ手動投稿できます。
A practical pick for design or creative work: sero-humanize: Audit or edit Sero Markdown documentation and other product prose to remove AI-writing patterns while preserving technical... 19 stars https://www.openagentskill.com/skills/sero-labs-sero-humanize?ref=x
任意:インストールコマンド付きの返信
Listing + install path for sero-humanize: https://www.openagentskill.com/skills/sero-labs-sero-humanize?ref=x Install: npx skills add sero-labs/sero --skill sero-humanize
掲載元
Registry により登録
この掲載は公開ソースから登録されており、メンテナー申請が承認されるまで公式として表示されません。
- 作成者
- sero-labs
- インデックス作成者
- OpenAgentSkill コミュニティインデックス
帰属は公開リポジトリまたは作成者プロフィールにリンクされています。作成者は掲載を申請して所有権シグナルを更新できます。
このスキルを申請所有者の申請
このスキル掲載を申請
この Registry により登録 掲載は sero-labs に帰属していますが、まだ公式として表示されていません。申請すると、確認済み所有者シグナルが追加され、今後の公開、インストール、監査更新の信頼性が高まります。
クリエイター被リンクキット
README にエビデンスバッジを追加
開発者がリポジトリを評価する場所で、正規掲載、現在の信頼・監査シグナル、実際の Agent-Proven エビデンスを表示します。
[](https://www.openagentskill.com/skills/sero-labs-sero-humanize)
[](https://www.openagentskill.com/skills/sero-labs-sero-humanize)
[](https://www.openagentskill.com/skills/sero-labs-sero-humanize/audit)
[](https://www.openagentskill.com/skills/sero-labs-sero-humanize)作者
sero-labs
@sero-labs
プラットフォーム適合
健全性シグナル
- GitHub スター
- 19
- 品質スコア
- 33/100
- 最終 GitHub プッシュ
- 2026年8月19日
- フレームワークのヒント
- 不明
- OpenAgentSkill 閲覧数
- 6
- インストールコピー数
- 0
- 外部クリック
- 0
コミュニティシグナル
このスキルが Agent ワークフローに役立つかを共有してください。集約されたフィードバックがランキングを改善します。
信頼と安全性
Do not auto-install
- GitHub 採用度GitHub スター 19修正
- スター/フォーク活動スター 19、フォーク 1; 現在のメタデータでは Issue 活動を利用できません修正
- 最近のメンテナンス最終プッシュから 3 日合格
- ライセンスの明確さApache-2.0合格
- README/SKILL.md の完全性メタデータには十分な利用・ワークフロー文脈があります合格
- 依存関係/ランタイムのリスク認証情報または環境変数アクセス情報
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