🛠️ Ejentum Reasoning Harness
AIがより正確な回答を生成できるよう、推論
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▶ 【衝撃】最強のAIエージェント「Claude Code」の最新機能・使い方・プログラミングをAIで効率化する超実践術を解説! ↗
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📜 元の英語説明(参考)
MCP server exposing four cognitive harness modes (reasoning, code, anti-deception, memory). Each call returns an engineered scaffold (failure pattern, procedure, suppression vectors, falsification test) the agent ingests before generating.
🇯🇵 日本人クリエイター向け解説
AIがより正確な回答を生成できるよう、推論
※ jpskill.com 編集部が日本のビジネス現場向けに補足した解説です。Skill本体の挙動とは独立した参考情報です。
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🎯 このSkillでできること
下記の説明文を読むと、このSkillがあなたに何をしてくれるかが分かります。Claudeにこの分野の依頼をすると、自動で発動します。
📦 インストール方法 (3ステップ)
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- 3. 展開してできたフォルダを、ホームフォルダの
.claude/skills/に置く- · macOS / Linux:
~/.claude/skills/ - · Windows:
%USERPROFILE%\.claude\skills\
- · macOS / Linux:
Claude Code を再起動すれば完了。「このSkillを使って…」と話しかけなくても、関連する依頼で自動的に呼び出されます。
詳しい使い方ガイドを見る →- 最終更新
- 2026-05-17
- 取得日時
- 2026-05-17
- 同梱ファイル
- 1
💬 こう話しかけるだけ — サンプルプロンプト
- › Ejentum Reasoning Harness を使って、最小構成のサンプルコードを示して
- › Ejentum Reasoning Harness の主な使い方と注意点を教えて
- › Ejentum Reasoning Harness を既存プロジェクトに組み込む方法を教えて
これをClaude Code に貼るだけで、このSkillが自動発動します。
📖 Claude が読む原文 SKILL.md(中身を展開)
この本文は AI(Claude)が読むための原文(英語または中国語)です。日本語訳は順次追加中。
Ejentum Reasoning Harness
The Ejentum Reasoning Harness is a library of 679 cognitive operations engineered in natural language, organized across four harnesses (reasoning, code, anti-deception, memory) and exposed as MCP tools the agent can call when the task matches their trigger conditions. It targets four mechanism failures common in long agentic chains: attention decay (losing the original task), reasoning decay (compounding errors), sycophantic collapse (agreeing with the user's frame instead of evaluating it), and hallucination drift (asserting unsupported claims with confidence).
Each harness call retrieves a task-matched scaffold rather than serving a fixed template: a named failure pattern, an executable procedure, suppression vectors that block specific shortcuts, and a falsification test the agent uses for self-verification. The agent ingests the scaffold and writes from it, rather than from raw chain-of-thought. The harness is invoked on demand (by the agent or via an explicit prompt like Use harness_anti_deception, then answer:...); it does not auto-run on every turn.
When to Use This Skill
- Use
harness_reasoningbefore answering analytical, diagnostic, planning, or multi-step questions ("why is X happening", "what's the best approach", "what are the tradeoffs", root-cause analysis, architecture decisions). - Use
harness_codebefore generating, refactoring, reviewing, or debugging code; before architectural changes, algorithm or data-structure choices, dependency-upgrade evaluation. - Use
harness_anti_deceptionwhen the prompt pressures the agent to validate, certify, or soften an honest assessment; manufactured urgency; authority appeals; setups where the obvious helpful answer would compromise honesty. - Use
harness_memoryonly when sharpening an observation already formed about cross-turn drift or behavioral patterns; never call with an empty mind.
Skip the harness for simple factual lookups, syntax questions, file reads, code execution, or tasks the agent can confidently complete in 1-2 steps from native capability.
How It Works
Step 1: Install the MCP server
The server is published to npm. Most MCP-speaking clients support stdio installation via npx:
npx -y ejentum-mcp
Add to your client's MCP server config (Claude Code .mcp.json, Cursor / Cline / Windsurf MCP settings, Codex CLI config, or Antigravity / VS Code mcp.json):
{
"mcpServers": {
"ejentum": {
"command": "npx",
"args": ["-y", "ejentum-mcp"],
"env": {
"EJENTUM_API_KEY": "${EJENTUM_API_KEY}"
}
}
}
}
Get a free API key (100 calls, no card required) at ejentum.com/pricing.
Step 2: Route to the right harness
Each harness has different trigger conditions (see "When to Use" above). Most clients with MCP support will route to the appropriate tool when the user's prompt matches the trigger conditions documented in the tool descriptions. For cold-install reproducibility, the agent can also call a specific harness explicitly: Use harness_anti_deception, then answer: ....
Step 3: Absorb the returned scaffold
The scaffold contains five labeled fields the agent should treat as internal-reasoning instructions, not output content:
[NEGATIVE GATE]/[CODE FAILURE]/[DECEPTION PATTERN]/[PERCEPTION FAILURE]: the failure pattern to avoid[PROCEDURE]: step-by-step procedure for an honest response[REASONING TOPOLOGY]: control-flow graph the agent steps through internally[TARGET PATTERN]: example of the corrected response shape[FALSIFICATION TEST]/[VERIFICATION]/[INTEGRITY CHECK]/[PERCEPTION CHECK]: the test to apply post-draft
The agent's user-facing reply should be in its native voice, with no echoed bracket names, no procedural vocabulary, and no meta-commentary about the harness.
Examples
Example 1: Anti-deception on a sunk-cost prompt
Prompt:
Use harness_anti_deception, then answer:
We've spent three months on the GraphQL gateway. It's mostly done.
Should we keep going or pivot to REST?
Without the harness, agents often anchor on the past investment ("sunk cost is real here, the hardest learning curve is behind you"). With the harness, the response separates past spending from prospective evaluation: "the three months already spent are gone regardless of what you choose now. The relevant question is how much work remains versus how much value GraphQL will deliver from this point forward."
Example 2: Code review with passing tests
Prompt:
Use harness_code: I refactored get_user to return None instead of raising on missing users.
All tests still pass. Should I merge?
The harness scaffolds a procedure that flags "tests pass" as a tool-shortcut signal rather than a correctness signal, surfaces the call-sites that handle exceptions vs None values, and recommends adding behavior-verifying tests before the merge.
Best Practices
- ✅ Call one harness per turn; the right harness for the prompt's shape
- ✅ Treat bracketed scaffold fields as internal-only; never echo them in the user-facing reply
- ✅ Apply the falsification test to the draft before responding
- ❌ Do not stack three or more harnesses in a single turn; attention competition degrades the first call
- ❌ Do not call harness_memory without observing first; it sharpens an existing observation, not creates one
- ❌ Do not treat the API as a hard dependency; on a 5-second timeout, fall back to native capability gracefully
Limitations
- The harness shapes the substance of reasoning; it does not guarantee a correct answer. Domain expertise and source verification still apply.
- 5-second timeout typical; clients should fall back to native capability if the API is unreachable.
- The scaffold is a procedure, not a knowledge base. It does not retrieve facts, only structured reasoning patterns.
Security & Safety Notes
- The MCP server makes outbound HTTPS requests to the Ejentum Logic API gateway (Zuplo-hosted).
- Authentication uses a Bearer token in the
EJENTUM_API_KEYenvironment variable. The token must be stored in environment variables or an MCP client's secret-handling mechanism, never committed to source. - The server does not execute shell commands or read filesystem paths beyond reading its own env. It is a pure HTTP-proxy MCP server.
- Free tier rate-limited at 100 calls; paid tiers documented at ejentum.com/pricing.