Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/marcusrbrown/systematicnpx agentmods add skills/marcusrbrown/systematic/writing-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/skills/marcusrbrown/systematic/writing-skills)<a href="https://agentmods.dev/skills/marcusrbrown/systematic/writing-skills"><img src="https://agentmods.dev/badge/skills/marcusrbrown/systematic/writing-skills.svg" alt="Measured on agentmods" 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 | $0.00020 | $0.06143 |
| Opus 5 | $0.00010 | $0.03071 |
| Sonnet 5 | $0.00004 | $0.01229 |
| Haiku 4.5 | $0.00002 | $0.00614 |
Grade A, and why
writing-skills 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 7d 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.
This is a copy
77% identical to writing-skills — 194 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 758 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Skills
Overview
Writing skills IS Test-Driven Development applied to process documentation.
Personal skills live in ~/.agents/skills/
You write test cases (pressure scenarios with subagents), watch them fail (baseline behavior), write the skill (documentation), watch tests pass (agents comply), and refactor (close loopholes).
Core principle: If you didn't watch an agent fail without the skill, you don't know if the skill teaches the right thing.
REQUIRED BACKGROUND: You MUST understand test-driven-development before using this skill. That skill defines the fundamental RED-GREEN-REFACTOR cycle. This skill adapts TDD to documentation.
Official guidance: For Anthropic's official skill authoring best practices, see references/anthropic-best-practices-distilled.md. This document provides additional patterns and guidelines that complement the TDD-focused approach in this skill.
What is a Skill?
A skill is a reference guide for proven techniques, patterns, or tools. Skills help future Claude instances find and apply effective approaches.
Skills are: Reusable techniques, patterns, tools, reference guides
Skills are NOT: Narratives about how you solved a problem once
TDD Mapping for Skills
| TDD Concept | Skill Creation |
|---|---|
| Test case | Pressure scenario with subagent |
| Production code | Skill document (SKILL.md) |
| Test fails (RED) | Agent violates rule without skill (baseline) |
| Test passes (GREEN) | Agent complies with skill present |
| Refactor | Close loopholes while maintaining compliance |
| Write test first | Run baseline scenario BEFORE writing skill |
| Watch it fail | Document exact rationalizations agent uses |
| Minimal code | Write skill addressing those specific violations |
| Watch it pass | Verify agent now complies |
| Refactor cycle | Find new rationalizations → plug → re-verify |
The entire skill creation process follows RED-GREEN-REFACTOR.
What ships with it
7 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.
- references/anthropic-best-practices-distilled.md 5.9 KB
- references/examples/skill-testing-walkthrough.md 5.3 KB
- references/foundation-conventions.md 8.5 KB
- references/graphviz-conventions.dot 5.8 KB
- references/persuasion-principles.md 5.8 KB
- references/testing-skills-with-subagents.md 12 KB
- scripts/render-graphs.js 5.0 KB runs code
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.
- 7d ago First seen · 758 lines · 20 tokens per session scan A ee48b1e33bfa
writing-skills is a skill published in the GitHub repository marcusrbrown/systematic (24 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 6,143 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 77% identical to writing-skills, differing in 194 lines, and is treated as a copy.
Other skills, from other repositories
plan-protocol
Guidelines for creating and managing implementation plans with citations.
plan-review
Criteria for reviewing implementation plans against quality standards.
code-review
Comprehensive code review methodology with severity classification and confidence thresholds.
code-philosophy
Internal logic and data flow philosophy (The 5 Laws of Elegant Defense). Understand deeply to ensure code guides data naturally and prevents errors.
frontend-philosophy
Visual & UI philosophy (The 5 Pillars of Intentional UI). Understand deeply to avoid "AI slop" and create distinctive, memorable interfaces.
opencode-ensemble
Use when coordinating multiple coding agents, delegating independent software work, managing OpenCode Ensemble teams, choosing teammate roles or models, reviewing teammate output, or deciding whether parallel execution is appropriate.