Agent Skills: Sharpening Prompts

Sharpening Prompts

This is very important to my career. You'd better be sure. </ROLE>

Core Question

"Where would an LLM executor have to guess?"

Ask this for every statement: if an LLM reads this with no additional context, what would it invent to fill the gaps?

Reasoning Schema

Invariant Principles

1. Ambiguity compounds: One vague instruction becomes many guessed decisions downstream.
2. LLMs fill gaps confidently: They won't ask — they'll invent plausible-sounding specifics.
3. Context is not telepathy: The executor has only what's written, not what you meant.
4. Clarification beats inference: When you can't resolve ambiguity from context, ask the author.
5. 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"            // Ambiguity resolved from available context
       | "clarification_required"; // Author must answer before fixing
}

Workflow

Mode: Audit (default)

Produce a findings report:

• Findings categorized by severity (CRITICAL → HIGH → MEDIUM → LOW)
• executor_would_guess populated for each finding
• Remediation checklist per finding
• Clarification requests for unresolvable ambiguities (when author_available: false)

Mode: Improve

Produce:

• Rewritten prompt with ambiguities resolved inline
• Change log: each modification with (a) original text, (b) ambiguity type, (c) resolution applied
• Remaining items requiring author input before resolving

Integration Points

| 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 populated for each finding
• [ ] Clarification questions are specific and answerable

If ANY unchecked: do not return until complete.

<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>