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description: Use BEFORE answering analytical, diagnostic, planning, or multi-step reasoning questions. Trigger phrases include "should I X or Y", "why is X happening", "what's the best approach", "what are the tradeoffs", "help me think through", "diagnose", "root cause", "plan/design X", "what are the implications of", "compare these approaches". Also fires on cross-domain analysis, strategy questions, architecture decisions, or anything requiring multiple factors to be weighed before responding. The skill calls the harness_reasoning MCP tool to retrieve a cognitive scaffold (named failure pattern, executable procedure, suppression vectors, falsification test) the model absorbs internally before generating its response. Catches causal shortcuts, premature conclusions, generic templates, and surface pattern matching that produce confidently-wrong answers. Do NOT trigger for simple factual lookups, syntax questions, file reads, code execution, or restating the user's input.
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# Reasoning Harness

When this skill triggers, call the `harness_reasoning` tool from the `ejentum` MCP server. Pass a 1-2 sentence framing of WHAT you are reasoning about as the `query` argument. Be specific about the task, not what tool you want.

Good query: `diagnose why a microservice returns 503s under load`
Bad query: `help me think`

The tool returns a structured scaffold containing:

- `[NEGATIVE GATE]` — failure pattern to avoid
- `[PROCEDURE]` — steps to follow
- `[REASONING TOPOLOGY]` — decision flow with gates and traps
- `[TARGET PATTERN]` — correct shape your reasoning should take
- `[FALSIFICATION TEST]` — self-check criterion
- `Amplify:` — signals to engage
- `Suppress:` — failure modes to block

Absorb the scaffold internally and shape your response with it. The bracketed fields are instructions, not content to display. Do NOT echo the bracket labels, do NOT name the topology, do NOT meta-comment on calling the tool. The user-facing reply is naturally phrased and shaped by the injection.

If the API is unreachable or returns an error, proceed with native reasoning. The scaffold enhances; it is not a hard dependency.

Latency cost: ~1 second. Benefit: reasoning quality the model cannot reliably reproduce on its own for non-trivial tasks.