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Create an evidence-grounded framing document from transcripts, call notes, stakeholder notes, or conversation records.
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
Improve, audit, simplify, rewrite, or migrate prompts, tool descriptions, agent instructions, and prompt stacks for GPT-5.6 Sol or the GPT-5.6 family. Use for outcome-first prompt design, autonomy boundaries, tool routing, PTC, grounding, verbosity, reasoning effort, and prompt evals.
Apply a deep, design-minded engineering workflow for complex work that needs careful planning, iteration, and simplification.
Use when preparing or running a Claude CLI agentic engineering review through claude -p for repository audits, architecture analysis, system design, security review, large refactors, specs, RFCs, or evidence-grounded long-form engineering analysis.
Use when preparing or running a ZCode GLM-5.2 agentic builder workflow through zcode --prompt for fast implementation, iterative code generation, focused fixes, and validation on an existing repository.
Apply adversarial opposite-analysis to plans, specs, architecture, code changes, and claims. Use when the user asks for adversarial review, opposing analysis, contrarian review, red-team reasoning, or Z.ai and MiniMax cross-checks through the Ralph MCP router.
Review and adjudicate Bugbot, Cursor, Seer, and similar automated PR feedback with local evidence before accepting, fixing, or dismissing findings.
| name | framing-doc |
| description | Create an evidence-grounded framing document from transcripts, call notes, stakeholder notes, or conversation records. |
| user-invocable | true |
| argument-hint | [transcript paths, notes, topic area, or frame request] |
Use this skill when the user has transcripts, VTT files, call notes, stakeholder notes, or conversation records and wants a frame that captures what problem is worth solving and why this one should be chosen over alternatives.
This is a Codex-safe adaptation of the upstream framing-doc shaping skill. It has no external command, model, deployment, credential, or network dependency.
Establish:
If source material may contain RED data, ask for sanitized input or keep the work local-only. Do not store transcripts in memory or .local-notes.
Produce a frame document with:
---
shaping: true
---
# [Topic] - Frame
## Source
### [Speaker] ([Date])
> "Verbatim quote..."
[Brief connective context where needed.]
---
## Pre-work: [Topic] Options Landscape
| Option | What it does | Who benefits | Signal strength |
| ------------- | ------------ | ------------ | --------------- |
| **A. [Name]** | ... | ... | ... |
**Why A now:** [Evidence-based argument.]
---
## Problem
- [Pain or broken condition, traceable to source]
## Outcome
- [High-level success state, not solution-specific]
---
## Less about
- [What this is not trying to solve]
## More about
- [What kind of solution actually fits]
Include Less about / More about only when the source material shows a meaningful boundary or a likely wrong direction.
The Source section is ground truth. Everything else is interpretation.
After writing each Problem or Outcome bullet, ask: who said this, and where?
Survey options that surfaced in the conversations. For each option, capture:
Make the case for the option to pursue now:
Do not invent a roadmap for the other options. The frame only claims which problem comes first and why.