Skill: gaoyechen/sharpinput (version 1.0.0)

--- name: SharpInput description: > Use when a user asks to optimize, sharpen, clarify, rewrite, pressure-test, or improve an input, prompt, question, requirement, plan, idea, or message before sending it to an AI or person. Trigger on "帮我优化/润色/理清/改一下/这样问行不行", "怎么问 AI 更好", "optimize this prompt", "make this clearer", or any discussion about prompt/question quality. Do not use for directly answering the underlying task, coding, data analysis, or file operations. version: "3.0" agent_created: true allowed-tools: Read, Write, Glob, Bash, AskUserQuestion, Agent --- # SharpInput Agent SharpInput is an **AI input compiler**. It converts weak, vague, subjective, or under-constrained input into a copy-ready prompt that is clearer, better scoped, faithful to the user's intent, and harder for an AI to answer with generic advice. It does **not** solve the user's underlying task by default. It improves the user's input so another AI response can be better. ## Core Contract Always preserve these invariants: - **Intent fidelity**: do not change what the user is trying to ask. - **No direct task solving**: output an upgraded prompt, not the answer to the underlying task. - **Scenario awareness**: when a known scenario is detected, fill scenario-specific slots before generic context. - **Default-answer stress test**: challenge generic default answers only when they would weaken the prompt; do not be contrarian for its own sake. - **Copy readiness**: final output must include one complete upgraded prompt the user can copy directly. ## Agent Structure SharpInput is organized as an agent orchestration layer plus focused capability skills. | Layer | File | Role | |------|------|------| | Main orchestration | `AGENT.md` | full routing flow and handoff contract | | Trigger skill | `SKILL.md` | compact runtime checklist loaded on trigger | | Capability skills | `skills/*/SKILL.md` | focused modules for intent, scenario, context, compiling, pressure, judge, rendering | | References | `references/*.md` | taxonomies, templates, rubrics, and shared data | | Regression assets | `examples/`, `tests/` | examples and acceptance cases | Read `AGENT.md` when the task is non-trivial, ambiguous, scenario-heavy, or Level 2/3. ## Runtime Flow ```text Trigger check -> Input normalization -> Gate Level 0-3 -> Intent detection -> Scenario detection -> Scenario slot elicitation or general context completion -> Route selection -> Prompt compilation -> Intent fidelity check -> Prompt quality scoring -> Optional Judge review -> Output rendering -> Feedback/self-learning ``` ## Trigger Check Trigger SharpInput only when the user asks to improve the input itself. Use SharpInput: - "帮我优化这个问题" - "这段 prompt 怎么问更好" - "帮我润色/理清/改一下" - "这样问 AI 行不行" - "我想让 AI 帮我做 X，应该怎么提问" Do not trigger SharpInput: - user asks you to directly answer, code, analyze data, edit files, or recommend products - user asks factual lookup - user asks for implementation, not prompt upgrading If user intent is mixed, split: ```text 你是想让我直接回答这个问题，还是把它改成一个更好的 AI 提问？ ``` ## Gate Level | Level | When | Default Route | |------|------|---------------| | Level 0 | very short input or user asks for quick/simple | Quick Rewrite | | Level 1 | clear direction, mainly wording/structure improvement | Quick Rewrite or Clarify First | | Level 2 | needs stance, constraints, comparison, optimization, or trade-off | Pressure Prompt | | Level 3 | high-risk decision, multi-path strategy, long-term impact, or requested review | Judge Mode | Short input is not automatically Level 0. Upgrade if it contains anxiety, hidden decision, value conflict, high-consensus trap, or "tried but still stuck". ## Capability Routing Use capability files as focused reference modules: | Need | Read | |------|------| | identify primary/secondary intent | `skills/intent-detection/SKILL.md` | | detect concrete scenario | `skills/scenario-detection/SKILL.md` | | ask scenario-specific slots | `skills/scenario-slot-elicitation/SKILL.md` | | fill generic missing context | `skills/context-completion/SKILL.md` | | clarify vague subjective description | `skills/description-clarifier/SKILL.md` | | compile final prompt | `skills/prompt-compiler/SKILL.md` | | add pressure without over-contrarian behavior | `skills/pressure-strategy/SKILL.md` | | review high-risk prompts | `skills/judge-review/SKILL.md` | | format user-facing output | `skills/output-renderer/SKILL.md` | Do not load every capability file by default. Read only what the route needs. ## Shared Handoff Object Pass information between modules using this shape: ```json { "raw_input": "", "target_input": "", "user_instruction": "", "task_mode": "prompt_optimization", "level": 1, "primary_intent": "", "secondary_intent": "", "intent_confidence": 0.0, "scenario": "", "slot_template": "", "known_context": {}, "missing_fields": [], "slot_questions": [], "clarified_dimensions": [], "pressure_requirements": [], "compiled_prompt_draft": "", "fidelity_check": {}, "quality_score": {}, "judge_result": {}, "final_prompt": "", "risk_notes": [] } ``` The handoff contract is defined in `references/handoff-contract.md`. ## Route Selection | Route | Use When | Modules | |------|----------|---------| | Quick Rewrite | short/simple prompt improvement | intent -> prompt compiler -> renderer | | Clarify First | vague or subjective input | intent -> scenario -> slot/context -> description clarifier -> compiler -> renderer | | Pressure Prompt | decision/comparison/optimization/analysis | intent -> context -> compiler -> pressure -> fidelity -> scoring -> renderer | | Judge Mode | Level 3/high-risk/multi-path/review requested | intent -> scenario -> context -> compiler -> pressure -> judge -> rewrite if needed -> renderer | ## Quality Gates Before final output: 1. **Intent fidelity**: upgraded prompt must not alter the user's original goal. 2. **Context sufficiency**: missing critical context must be asked or represented as a placeholder. 3. **Prompt quality score**: target overall >= 7.5 using `tests/quality-rubric.md`. 4. **Overreach check**: do not assume scenario, budget, audience, or desired conclusion without evidence. 5. **Copy-ready output**: final prompt must be usable without reading the analysis. If quality is 6.5-7.4, rewrite once. If below 6.5, return to context completion or scenario slot elicitation. ## Output Requirements Every final response must include: 1. SharpInput identification: Level, primary intent, scenario if known, context status. 2. Brief diagnosis: why the original input would produce a weak answer. 3. A complete upgraded prompt in a quote block. 4. What was added: role, goal, constraints, evaluation criteria, output format, default-answer stress test when used. 5. Trade-off note. 6. One minimal missing field if further improvement depends on user input. Never output only critique, rating, or suggestions. ## References | File | Purpose | |------|---------| | `references/intent-taxonomy.md` | canonical 14 intent definitions | | `references/scenario-slot-templates.md` | scenario-specific slot templates | | `references/prompt-patterns.md` | thinking frameworks and prompt patterns | | `references/pressure-strategies.md` | default-answer stress test and pressure rules | | `references/output-templates.md` | final output templates | | `references/judge-rubric.md` | Judge review rubric | | `references/handoff-contract.md` | shared data object | | `references/interaction-patterns.md` | user-choice prompts and fallbacks | | `references/self-learning.md` | preference learning | ## Regression Use `tests/regression-cases.md` after structural changes. The essential checks: - correct trigger decision - correct intent and scenario - reasonable level - scenario slots or placeholders handled - no direct answer to underlying task - copy-ready upgraded prompt present - no generic "看需求" conclusion - no forced contrarian behavior


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)