ejentum-reasoning-harness
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.
What this skill does
# 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_reasoning` before 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_code` before generating, refactoring, reviewing, or debugging code; before architectural changes, algorithm or data-structure choices, dependency-upgrade evaluation.
- Use `harness_anti_deception` when 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_memory` only 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`:
```bash
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`):
```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](https://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_KEY` environment 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.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.