Skills llm-application-dev-prompt-optimize
📦

llm-application-dev-prompt-optimize

Content revision r1 Safe ⚙️ External commands

Optimize LLM Application Prompts

Weak prompts create inconsistent LLM behavior, higher token costs, and difficult testing. This skill turns rough instructions into structured prompts with evaluation, safety, and deployment guidance.

Supports: Claude Codex Code(CC)
🥉 78 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "llm-application-dev-prompt-optimize" from https://skillstore.io/skills/sickn33-llm-application-dev-prompt-optimize.md and its manifest at https://skillstore.io/api/skills/sickn33-llm-application-dev-prompt-optimize/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/sickn33-llm-application-dev-prompt-optimize.md Signed manifest GET /api/skills/sickn33-llm-application-dev-prompt-optimize/manifest Signed lockfile GET /api/skills/sickn33-llm-application-dev-prompt-optimize/lockfile

Test it

Using "llm-application-dev-prompt-optimize". A short support prompt that says, "Answer customer questions about our product."

Expected outcome:

An improved support prompt with role, product context, diagnostic steps, escalation rules, response length limits, and a clear response format.

Using "llm-application-dev-prompt-optimize". A data analysis prompt without validation steps or confidence reporting.

Expected outcome:

A structured analyst prompt with data checks, trend analysis, segment review, confidence levels, recommendations, and testing guidance.

Using "llm-application-dev-prompt-optimize". A team wants to deploy a new RAG prompt into production.

Expected outcome:

A prompt package with source-use instructions, citation rules, gap handling, evaluation cases, rollout stages, and monitoring metrics.

Security Audit

Safe
v4 • 7/7/2026 Open versioned report

All six static findings are false positives. The SKILL.md findings are Markdown file references, and the playbook findings are prompt optimization examples or checklists. No semantic evidence of malicious intent, prompt injection, or data exfiltration was found.

2
Files scanned
630
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

⚙️ External commands (2)
SKILL.md:33 SKILL.md:37
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
Share & cite this report

Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.

Open versioned report
Security Assessment

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https://skillstore.io/skills/sickn33-llm-application-dev-prompt-optimize/audits/4?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). llm-application-dev-prompt-optimize security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-llm-application-dev-prompt-optimize/audits/4

BibTeX citation

@techreport{sickn33-sickn33-llm-application-dev-prompt-optimize-2026, author = {sickn33}, title = {llm-application-dev-prompt-optimize security audit report (audit version 4)}, institution = {Skillstore}, year = {2026}, number = {4}, url = {https://skillstore.io/skills/sickn33-llm-application-dev-prompt-optimize/audits/4}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "llm-application-dev-prompt-optimize security audit report (audit version 4)" version: "unspecified" type: report authors: - name: "sickn33" date-released: "2026-07-07" url: "https://skillstore.io/skills/sickn33-llm-application-dev-prompt-optimize/audits/4" identifiers: - type: other value: "skillstore:sickn33-llm-application-dev-prompt-optimize:audit:4" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
85
Maintainability
87
Content
69
Community
83
Spec Compliance

What You Can Build

Improve Product Prompts

Convert rough feature prompts into structured instructions with objectives, examples, output formats, and quality checks.

Prepare LLM App Prompts

Build production prompts for support, data analysis, RAG, and code generation workflows.

Standardize Prompt Testing

Create evaluation plans with edge cases, adversarial cases, scoring criteria, and deployment monitoring.

Try These Prompts

Quick Prompt Review 
Review this prompt for clarity, missing context, ambiguity, and output format issues. Then provide a stronger version and explain the changes. Prompt: {prompt}
Model-Specific Rewrite 
Optimize this prompt for {model}. Preserve the task goal, add clear sections, improve instruction order, and recommend parameters. Prompt: {prompt}
Few-Shot Production Prompt 
Create a production prompt for {task}. Include role, context, steps, constraints, output format, three examples, error handling, and success criteria.
Prompt System Evaluation Plan 
Design an evaluation plan for this LLM workflow: {workflow}. Include test categories, scoring rubric, safety checks, rollout plan, monitoring metrics, and iteration rules.

Best Practices

  • Start with the task goal, constraints, required inputs, and exact output format.
  • Test prompts with normal, edge, adversarial, and out-of-scope examples before deployment.
  • Track prompt versions, model settings, success rates, cost, latency, and user feedback.

Avoid

  • Do not rely on vague instructions like "be helpful" for production workflows.
  • Do not add reasoning steps when a concise deterministic answer is enough.
  • Do not assume one optimized prompt will perform equally across all models.

Frequently Asked Questions

What does this skill optimize?
It optimizes LLM prompts for clarity, structure, reasoning, examples, safety, evaluation, and deployment readiness.
Does it work with Claude, Codex, and Claude Code?
Yes. The report marks support for Claude, Codex, and Claude Code, and the playbook includes model-specific guidance.
Can it create prompts for RAG systems?
Yes. It includes a RAG pattern with document context, relevance checks, synthesis rules, citations, and gap reporting.
Does it run tests automatically?
No. It designs test cases, rubrics, and monitoring plans, but you must run evaluations in your own environment.
Is it only for developers?
No. Developers, product managers, support teams, analysts, and AI quality teams can use the guidance.
What input should I provide?
Provide the current prompt, target model, task goal, sample inputs, desired output format, constraints, and success criteria.

Developer Details

Author

sickn33

License

MIT

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/llm-application-dev-prompt-optimize

Ref

816c62b2546ddb1c6a0453e7c781b5e095117819

Maintenance freshness

7/18/2026

Usage

9 downloads · 128 views

File structure

📁 resources/

📄 implementation-playbook.md

📄 SKILL.md

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