Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add desilokesh1/antigravity-fullstack-hq --skill prompt-engineeringgit clone --depth 1 https://github.com/desilokesh1/antigravity-fullstack-hqWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/desilokesh1/antigravity-fullstack-hq/prompt-engineering)<a href="https://agentmods.dev/skills/desilokesh1/antigravity-fullstack-hq/prompt-engineering"><img src="https://agentmods.dev/badge/skills/desilokesh1/antigravity-fullstack-hq/prompt-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/desilokesh1/antigravity-fullstack-hq/prompt-engineering"><img src="https://agentmods.dev/badge/skills/desilokesh1/antigravity-fullstack-hq/prompt-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.00533 |
| Opus 5 | $0.00017 | $0.00267 |
| Sonnet 5 | $0.00007 | $0.00107 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
prompt-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering
Workflow
User Request
|
+-- "Create a prompt" --> EXPLORATION PHASE
+-- "Optimize this prompt" --> OPTIMIZATION PHASE
+-- "Fix this issue" --> ANALYSIS PHASE
Phase 1: Exploration
Before creating any prompt, understand:
- What task will this prompt accomplish?
- Who will use it?
- What does success look like?
- What are the constraints?
Phase 2: Analysis
Task Classification
| Dimension | Options |
|---|---|
| Complexity | Simple vs multi-step |
| Output | Creative vs analytical vs structured |
| Stakes | High vs experimental |
Strategy Selection
| Task Type | Approach |
|---|---|
| Simple | Direct instructions |
| Complex | Chain-of-thought |
| Creative | Role setting |
| Structured | Format specs + examples |
Phase 3: Implementation
Version 1 - Minimal
- Core instructions only
- Test basic functionality
Version 2 - Enhanced
- Add examples
- Clarify ambiguities
- Add constraints
Version 3+ - Optimized
- Refine wording
- Remove redundancy
Key Techniques
Role Setting
As an experienced code reviewer, analyze...
Chain-of-Thought
Think step-by-step:
1. First, identify...
2. Then, analyze...
3. Finally, conclude...
Few-Shot Learning
Example 1:
Input: "Great product"
Output: { "sentiment": "positive" }
Now analyze: "It was okay"
Explicit Constraints
- Limit to 3 paragraphs
- Focus on technical aspects only
- Do not include pricing
Prompt Template
## Context
[Background information]
## Role (Optional)
You are a [ROLE] with expertise in [DOMAIN].
## Task
[Clear instruction]
## Constraints
- Constraint 1
- Constraint 2
## Output Format
[Format specification]
## Examples (Optional)
[Input/Output examples]
Common Mistakes
| Mistake | Fix |
|---|---|
| Vague instructions | Be specific |
| No examples | Add 1-2 examples |
| Too many rules | Simplify |
| No format spec | Define output structure |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 127 lines · 35 tokens per session scan A 1933f208cc2e
prompt-engineering is a skill published in the GitHub repository desilokesh1/antigravity-fullstack-hq (2 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 533 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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