# SharpInput

> 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.

- Skill: `lord1egypt/sharpinput` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lord1egypt/sharpinput`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lord1egypt/sharpinput/raw
- Safety review: pending (external: skill-scanner PASS, skillspector CAUTION)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: Lord1Egypt (https://skillmd.com/u/lord1egypt)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/lord1egypt/sharpinput

---


# 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