Agent skill
sharpening-prompts
Use when reviewing LLM prompts, skill instructions, subagent prompts, or any text that will instruct an AI. Triggers: "review this prompt", "audit instructions", "sharpen prompt", "is this clear enough", "would an LLM understand this", "ambiguity check". Also invoked by instruction-engineering, reviewing-design-docs, and reviewing-impl-plans for instruction quality gates.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/sharpening-prompts
SKILL.md
Sharpening Prompts
This is very important to my career. You'd better be sure.
Core Question
"Where would an LLM executor have to guess?"
For every statement in the prompt, ask: If an LLM reads this with no additional context, what would it invent to fill the gaps?
Reasoning Schema
Invariant Principles
- Ambiguity compounds: One vague instruction becomes many guessed decisions downstream.
- LLMs fill gaps confidently: They won't ask - they'll invent plausible-sounding specifics.
- Context is not telepathy: The executor has only what's written, not what you meant.
- Clarification beats inference: When you can't resolve ambiguity from context, ask the author.
- Specificity enables verification: Vague success criteria can't be tested.
Inputs / Outputs
| Input | Required | Description |
|---|---|---|
prompt_text |
Yes | The prompt/instructions to review (inline or file path) |
mode |
No | audit (report findings) or improve (rewrite prompt). Default: audit |
context_files |
No | Additional files for resolving ambiguities |
author_available |
No | If true, can ask clarifying questions. Default: false |
| Output | Type | Description |
|---|---|---|
findings_report |
Inline | Categorized findings with severity and remediation |
improved_prompt |
Inline/File | Rewritten prompt (improve mode only) |
clarification_requests |
Inline | Questions for author if ambiguities unresolvable |
Ambiguity Categories
| Category | Pattern | Detection Signal |
|---|---|---|
| Weasel Words | "appropriate", "properly", "as needed", "correctly" | Adverbs/adjectives without measurable criteria |
| TBD Markers | "TBD", "TODO", "later", "to be determined" | Explicit deferral markers |
| Magic Values | Unexplained numbers, thresholds, limits | Numbers without rationale |
| Implicit Interfaces | "Use the X method", "Call Y" | Assumed APIs without verification |
| Scope Leaks | "etc.", "and so on", "similar things" | Unbounded enumerations |
| Pronoun Ambiguity | "it", "this", "that" with unclear referents | Pronouns with multiple possible antecedents |
| Conditional Gaps | "If X, do Y" with no else branch | Missing failure/alternative paths |
| Temporal Vagueness | "soon", "quickly", "eventually", "when ready" | Time-dependent without definition |
| Success Ambiguity | "Should work", "handle properly", "be correct" | Unverifiable success criteria |
| Assumed Knowledge | References to undocumented patterns/conventions | Context the executor won't have |
Severity Levels
| Severity | Meaning | Executor Impact |
|---|---|---|
| CRITICAL | Core behavior undefined | Will invent incompatible implementation |
| HIGH | Important path ambiguous | Will guess on non-trivial decision |
| MEDIUM | Secondary behavior unclear | May guess on edge case |
| LOW | Minor ambiguity | Likely guesses correctly from conventions |
Finding Schema
interface Finding {
id: string; // F1, F2, etc.
category: AmbiguityCategory;
severity: "CRITICAL" | "HIGH" | "MEDIUM" | "LOW";
location: string; // Line number, section name, or quote context
original_text: string; // Exact quoted problematic text
problem: string; // Why this is ambiguous
executor_would_guess: string; // What an LLM would likely invent
clarification_needed: string; // Specific question to resolve
suggested_fix?: string; // If context allows inference
source: "inference" | "clarification_required";
}
Workflow
Mode: Audit
Execute /sharpen-audit command.
Produces findings report with:
- Categorized findings by severity
- Executor guess predictions
- Remediation checklist
- Clarification requests (if author unavailable)
Mode: Improve
Execute /sharpen-improve command.
Produces:
- Rewritten prompt with clarifications embedded
- Change log explaining each modification
- Remaining ambiguities that need author input
Integration Points
This skill is invoked by:
| Skill | When | Purpose |
|---|---|---|
instruction-engineering |
Before finalizing prompts | QA gate for subagent prompts |
reviewing-design-docs |
Phase 2-3 | Detect vague specifications |
reviewing-impl-plans |
Phase 2-3 | Detect ambiguous task descriptions |
writing-skills |
Before deployment | QA gate for skill instructions |
writing-commands |
Before deployment | QA gate for command instructions |
Quick Reference: Sharpening Patterns
| Vague | Sharp |
|---|---|
| "Handle errors appropriately" | "On network error: retry 3x with exponential backoff (1s, 2s, 4s), then throw NetworkError with original message" |
| "Use the validate method" | "Call UserValidator.validate(input) from src/validators.ts:45 which returns {valid: boolean, errors: string[]} |
| "Process items quickly" | "Process items within 100ms per batch of 50" |
| "Support common formats" | "Support JSON, YAML, and TOML (reject all others with FormatError)" |
| "It should work correctly" | "Returns 200 with {success: true, data: User} on valid input; returns 400 with {error: string} on validation failure" |
Self-Check
Before completing:
- Every statement evaluated for ambiguity
- All weasel words flagged
- All TBD markers flagged as CRITICAL
- All magic values questioned
- All implicit interfaces verified or flagged
- All conditional statements have both branches
- Success criteria are testable
- Executor-would-guess field populated for each finding
- Clarification questions are specific and answerable
If ANY unchecked: complete before returning.
<FINAL_EMPHASIS> LLMs don't ask for clarification. They guess confidently. Every ambiguity you miss becomes a hallucinated assumption that compounds through implementation. Find where they would guess. Sharpen until there's nothing left to invent.
This is very important to my career. You'd better be sure. </FINAL_EMPHASIS>
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