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 Mathews-Tom/armory --skill prompt-labgit clone --depth 1 https://github.com/Mathews-Tom/armoryWrote 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/mathews-tom/armory/prompt-lab)<a href="https://agentmods.dev/skills/mathews-tom/armory/prompt-lab"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/prompt-lab.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 185 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00074 | $0.01785 |
| Opus 5 | $0.00037 | $0.00892 |
| Sonnet 5 | $0.00015 | $0.00357 |
| Haiku 4.5 | $0.00007 | $0.00178 |
Grade A, and why
prompt-lab 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 2d 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Lab
Replaces trial-and-error prompt engineering with structured methodology: objective definition, current prompt analysis, variant generation (instruction clarity, example strategies, output format specification), evaluation rubric design, test case creation, and failure mode identification.
Reference Files
| File | Contents | Load When |
|---|---|---|
references/prompt-patterns.md |
Prompt structure catalog: zero-shot, few-shot, CoT, persona, structured output | Always |
references/evaluation-metrics.md |
Quality metrics (accuracy, format compliance, completeness), rubric design | Evaluation needed |
references/failure-modes.md |
Common prompt failure taxonomy, detection strategies, mitigations | Failure analysis requested |
references/output-constraints.md |
Techniques for constraining LLM output format, JSON mode, schema enforcement | Format control needed |
Prerequisites
- Clear objective: what should the prompt accomplish?
- Target model (GPT-4, Claude, open-source) — prompting techniques vary by model
- Current prompt (if improving) or task description (if creating)
Workflow
Phase 1: Define Objective
- Task specification — What should the LLM produce? Be specific: "Classify customer support tickets into 5 categories" not "Handle support tickets."
- Success criteria — How do you know the output is correct? Define measurable criteria before writing any prompt.
- Failure modes — What does a bad output look like? Missing information? Wrong format? Hallucinated content? Refusal to answer?
Phase 2: Analyze Current Prompt
If an existing prompt is provided:
- Structure assessment — Is the instruction clear? Are examples provided? Is the output format specified?
- Ambiguity detection — Where could the model misinterpret the instruction?
- Missing components — What's not specified that should be? (output format, tone, length constraints, edge case handling)
- Failure mode mapping — Which known failure patterns (see
references/failure-modes.md) apply to this prompt?
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 200 lines · 74 tokens per session scan A 9b65de51aa20
prompt-lab is a skill published in the GitHub repository Mathews-Tom/armory (316 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 1,785 once invoked, about $0.0004 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 2026-09-05.
Other skills, from other repositories
agent-platform-prompt-management
Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.
gemini-api-dev
Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. Covers SDK usage and best…
prompt-analysis
Analyze AI prompting patterns and acceptance rates.
prompt-engineer
Prompt engineering expert for chain-of-thought, few-shot learning, evaluation, and LLM optimization.
prompt-engineering
Master the art and science of crafting effective prompts for large language models. Covers foundational patterns, advanced techniques like chain-of-thought and role prompting, structured output formats, and practical strategies for iterative refinement.
prompt-version-management
Manage prompt versions, run A/B tests across agent prompts, track performance regressions, and safely roll out prompt changes in production. Covers prompt diffing, semantic versioning, canary releases, and automated evaluation.