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Prove and counter-prove a claim with parallel agents before concluding. Use for architecture claims, bug hypotheses, performance claims, refactor safety, review judgments, and "is this actually true?" questions.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Serious engineering work with a reviewed plan and adversarial review gates; the required first argument word picks the mode. `build` — features, root-caused fixes, cross-cutting changes (trivial ones skip planning, never the diff gate). `refactor` — consolidate one accreted component, behavior-preserving. `sweep` — unattended overnight cleanup of a named area, shipped as draft PRs. `experiment` — metric-driven experimentation: approved hypothesis tree, autonomous batch of runs, next-batch report. `followup` — work recorded out-of-scope findings with the user's decisions; no required argument. Invoke explicitly, e.g. `/tokenmaxxer refactor <component>`.
Guidelines for ML projects. Use when training or evaluating models, building ML pipelines, running experiments, or working with datasets/parquet files and CatBoost/PyTorch/Polars code.
Build full-stack web applications using Rust (Axum + SQLx) with server-rendered frontend patterns using HTMX + Alpine.js or DataStar, plus Neon (serverless PostgreSQL). Use when asked to create web apps, CRUD apps, dashboards, forms, or any stateful web application. Triggers on requests like "build a todo app", "create a voting app", "make a dashboard", "build a blog", etc.
Evidence-first debugging and root cause investigation. Use for triaging bugs, test failures, incidents, performance regressions, flaky behavior, integration failures, or unexplained behavior before proposing fixes.
Design, build, audit, and improve the harness around an LLM agent — its tools, loop, permissions, context, skills, and evals. Use when building, reviewing, or debugging any agentic system: an agent runtime or loop, agent-facing tools, an MCP server, an approval/permission flow, a context or memory strategy, an eval suite, or an existing agent that underperforms (flaky, wrong tool calls, retry loops, burning tokens) — even when the request just says "add a tool" or "make the agent do X" without naming a harness. Provider-neutral (OpenAI, Anthropic, MCP). Not for ordinary app features that don't change agent behavior, tool execution, context, permissions, or validation.
| name | dialectic |
| description | Prove and counter-prove a claim with parallel agents before concluding. Use for architecture claims, bug hypotheses, performance claims, refactor safety, review judgments, and "is this actually true?" questions. |
| argument-hint | <claim> |
| compatibility | external review tool (e.g. codex:codex-rescue or opencode) is recommended |
Run a dialectic check on: $ARGUMENTS
The goal is to reduce confirmation bias, not to generate debate theater.
Proportionality: if a single command or test can decide the claim, run it and skip the parallel agents entirely; report the evidence in the same output format.
codex:codex-rescue when available, usually as Antithesis. If no independent reviewer is available, use two separate subagents with opposing prompts:
Use this structure:
**Claim:** ...
**Thesis evidence:**
- ...
**Antithesis evidence:**
- ...
**Verified conclusion:** true | false | mixed | unknown
**Next action:** ...