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.
git clone --depth 1 https://github.com/Amey-Thakur/AI-SKILLSWrote 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/commands/amey-thakur/ai-skills/prompt-optimization)<a href="https://agentmods.dev/commands/amey-thakur/ai-skills/prompt-optimization"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/prompt-optimization/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/commands/amey-thakur/ai-skills/prompt-optimization"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/prompt-optimization.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 | $0.00014 | $0.00224 |
| Opus 5 | $0.00007 | $0.00112 |
| Sonnet 5 | $0.00003 | $0.00045 |
| Haiku 4.5 | $0.00001 | $0.00022 |
Grade A, and why
prompt-optimization 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 3d 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.
What it actually says
You were invoked as a slash command. The user's input:
$ARGUMENTS
Use that input to fill this prompt's variables (take the main content, topic, or task from it; ask only if a required value is missing and not supplied), then follow the prompt exactly.
Improve this prompt:
{prompt}
Observed failures: {failures}
Use prompt-iteration, prompt-structure, and prompt-constraints.
Produce:
- A diagnosis of why the failures happen: structure, ambiguity, missing constraint, or a model limitation.
- The revised prompt.
- What changed and which failure each change addresses.
- Test cases including the failures, for regression.
- What to check next if the revision does not work.
Rules: change for a stated reason, not by rewriting wholesale. Prefer removing instructions to adding them. State plainly where the failure is a model limitation no prompt fixes. Keep the case set so the change is verifiable.
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.
- 3d ago First seen · 36 lines · 14 tokens per session scan A cb1e98ad94fd
prompt-optimization is a command published in the GitHub repository Amey-Thakur/AI-SKILLS (7 stars, last pushed 4d ago), licensed MIT. It adds 14 tokens to every session and 224 once invoked, about $0.0001 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 2026-09-06.
Other commands, from other repositories
verify-work
Execute a work item's testing-plan.md after implementation — run the planned activities, record results, commit the evidence (verification phase).
regression-test
Write a failing regression test, fix the bug, verify, and check for similar issues.
merge-check
Pre-merge quality gate with parallel verification. Runs build, archive, test, and lint checks.
complete-feature
Complete a feature with full validation across build, tests, lint, and patterns. Runs the complete-feature skill.
evaluate
Evaluate a skill across model tiers using blind testing.
checklist
Generate a custom checklist for the current feature based on user requirements.