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 agentmods add commands/akiojin/unity-mcp-server/speckit.specifygit clone --depth 1 https://github.com/akiojin/unity-mcp-serverWrote 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/commands/akiojin/unity-mcp-server/speckit.specify)<a href="https://agentmods.dev/commands/akiojin/unity-mcp-server/speckit.specify"><img src="https://agentmods.dev/badge/commands/akiojin/unity-mcp-server/speckit.specify.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00031 | $0.00774 |
| Opus 5 | $0.00015 | $0.00387 |
| Sonnet 5 | $0.00006 | $0.00155 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
speckit.specify 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.
What it actually says
ユーザー入力
$ARGUMENTS
空でない場合、続行する前にユーザー入力を考慮する必要があります。
概要
/speckit.specify の後にユーザーが入力したテキストが要件の説明です。$ARGUMENTS が文字通り表示されていても、この会話で常に利用可能であると仮定してください。ユーザーが空のコマンドを提供した場合を除き、繰り返しを求めないでください。
実行手順
-
リポジトリルートから
.specify/scripts/bash/create-new-feature.sh --json "$ARGUMENTS"を実行し、JSON出力から以下を取得します（パスはすべて絶対パス）:FEATURE_ID（例:SPEC-a1b2c3d4）FEATURE_DIRSPEC_FILESPECS_README（specs/specs.md。要件一覧はスクリプトが自動更新します）
-
.specify/templates/spec-template.mdを読み込み、必須セクションと見出し構造を理解します。 -
SPEC_FILEに、テンプレート構造を保ったまま仕様を書きます:- 「何を」「なぜ」を中心に書く（実装方法・技術スタック・API・コード構造は書かない）
- ユーザーストーリーは優先度順（P1→P2→P3）で、独立してテスト可能にする
- 要件はテスト可能な文で書く（曖昧語を避ける）
-
不明点がある場合:
- コンテキストと一般的な業界標準で合理的に補完する
- 複数の解釈がありスコープやUXに大きく影響する場合のみ、
[要明確化: 質問]を付ける [要明確化]は最大3つまで
-
仕様品質チェックリストを作成します（ファイル書き込みあり）:
FEATURE_DIR/checklists/requirements.mdを作成.specify/templates/checklist-template.mdをベースに、少なくとも以下の観点を含める:- 実装詳細が混入していない
- 必須セクションが埋まっている
- 要件がテスト可能で曖昧さがない
- ユーザーストーリーが独立して検証できる
- 成功基準が測定可能で技術非依存
-
最後に、ユーザーへ以下を返します:
FEATURE_IDSPEC_FILEのパスspecs/specs.mdが更新されている旨- 次ステップ（
/speckit.plan）への案内
注意
- SpeckitはGitブランチを作成しません（要件は
specs/SPEC-xxxxxxxx/で管理します）。
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 First seen · 55 lines · 31 tokens per session scan A 6f0b92dc0c33
speckit.specify is a command published in the GitHub repository akiojin/unity-mcp-server (34 stars, last pushed 4d ago), licensed MIT. It adds 31 tokens to every session and 774 once invoked, about $0.0002 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.