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 athola/claude-night-market --skill skill-authoringgit clone --depth 1 https://github.com/athola/claude-night-marketWrote 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/athola/claude-night-market/skill-authoring)<a href="https://agentmods.dev/skills/athola/claude-night-market/skill-authoring"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/skill-authoring/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/athola/claude-night-market/skill-authoring"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/skill-authoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00022 | $0.02305 |
| Opus 5 | $0.00011 | $0.01153 |
| Sonnet 5 | $0.00004 | $0.00461 |
| Haiku 4.5 | $0.00002 | $0.00231 |
Grade A, and why
skill-authoring 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 12d 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Authoring Guide
When NOT To Use
- Scoring a skill that already exists (use
abstract:skills-eval) - Authoring a hook (use
abstract:hook-authoring) - Verifying the skill fires (use
abstract:subagent-testing)
Overview
Writing effective Claude Code skills requires Test-Driven Development (TDD) and persuasion principles from compliance research. We treat skill writing as process documentation that needs empirical validation rather than just theoretical instruction. Skills are behavioral interventions designed to change model behavior in measurable ways.
By using TDD, we ensure skills address actual failure modes identified through testing. Optimized descriptions improve discovery, while a modular structure supports progressive disclosure to manage token usage. It states intent, constraints and exit criteria, and leaves the path between them to the session doing the work. modules/persuasion-principles.md carries the strength budget that decides which of the three each statement is.
The Iron Law
NO SKILL WITHOUT A FAILING TEST FIRST
Every skill must begin with documented evidence of Claude failing without it. This validates that you are solving a real problem. No implementation should proceed without a failing test, and no completion claim should be accepted without evidence. Detailed enforcement patterns for adversarial verification and coverage gates are available in imbue:proof-of-work.
Skill Types
We categorize skills into three types: Technique skills for specific methods, Pattern skills for recurring solutions, and Reference skills for quick lookups and checklists. This helps organize interventions into the most effective format for the task.
Quick Start
Skill Analysis
```bash
Analyze skill complexity
python scripts/skill_analyzer.py
Estimate tokens
python scripts/token_estimator.py ```
Validation
```bash
Validate skill structure
python scripts/abstract_validator.py --check ```
What ships with it
14 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.
- modules/advanced-patterns.md 8.1 KB
- modules/authentication.md 6.7 KB
- modules/deployment-checklist.md 13 KB
- modules/description-writing.md 13 KB
- modules/error-handling.md 7.7 KB
- modules/examples.md 7.8 KB
- modules/graphviz-conventions.md 2.3 KB
- modules/persuasion-principles.md 4.9 KB
- modules/progressive-disclosure.md 12 KB
- modules/tdd-methodology.md 12 KB
- modules/testing-with-subagents.md 7.1 KB
- modules/troubleshooting.md 9.6 KB
- modules/validation.md 7.8 KB
- README.md 5.0 KB
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.
- 12d ago First seen · 204 lines · 22 tokens per session scan A 954fa29a5448
skill-authoring is a skill published in the GitHub repository athola/claude-night-market (337 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 2,305 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 2026-08-30.
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testing-patterns
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test-driven-development
Use when implementing any feature or bugfix, before writing implementation code.
test-driven-development
Test-driven development, or TDD, is a way to build software by writing a test that fails, adding the smallest code that makes it pass, and then cleaning up the code. These instructions require that process for features, bug fixes, refactors, and behavior changes.
old-coder
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refactoring-patterns
Systematic refactoring techniques, code smell elimination, pattern extraction, and legacy modernization.
common-tdd
Guides quality-first TDD for new behavior, bug fixes, and test changes. Selects the smallest test layer, proves a distinct regression risk, and runs bounded RED-GREEN-REFACTOR verification.