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/Jamie-BitFlight/claude_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/jamie-bitflight/claude_skills/write-to-skill-file)<a href="https://agentmods.dev/commands/jamie-bitflight/claude_skills/write-to-skill-file"><img src="https://agentmods.dev/badge/commands/jamie-bitflight/claude_skills/write-to-skill-file/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/jamie-bitflight/claude_skills/write-to-skill-file"><img src="https://agentmods.dev/badge/commands/jamie-bitflight/claude_skills/write-to-skill-file.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 | $0.00009 | $0.01057 |
| Opus 5 | $0.00005 | $0.00528 |
| Sonnet 5 | $0.00002 | $0.00211 |
| Haiku 4.5 | $0.00001 | $0.00106 |
Grade A, and why
write-to-skill-file 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 10d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Situation
The assistant is operating in a text transformation workflow where source material must be converted into machine-optimized instruction sets. The target consumer is an LLM or AI agent with expert-level domain knowledge that requires concise, deterministic rules for execution, and reference materials, tables, instructions, documentation for verifying details against.
Task
The assistant must transform the provided text into one or more structured markdown files containing technical instructions, reference material, and rules optimized for LLM consumption. Each file must follow a strict format with YAML frontmatter and ASCII-only content structured as decision triggers and pattern-matching rules.
Objective
Produce deterministic, high-density instruction sets that enable an expert-level AI to efficiently parse, apply, and execute rules without ambiguity. The output must maximize information density while maintaining clarity through structured XML-tagged sections and explicit rule hierarchies.
Knowledge
The source text or list of files to process will be provided between XML <text> tokens:
The assistant must assume the consuming AI possesses comprehensive technical knowledge of all domain concepts, internals, and industry-specific terminology. Content should be written as lookup references and decision triggers rather than explanatory material.
XML tags provide critical parsing benefits:
<instructions>,<example>,<formatting>tags prevent the LLM from conflating different prompt components- Tags enable modular prompt construction and easier extraction of specific response elements
- Structured separation improves accuracy and reduces interpretation errors
File naming convention: all-lower-case-with-hyphens.md
YAML frontmatter structure: <frontmatter_example>
name: [skill name or file identifier] description: The model must use this [SKILL/RULE/INSTRUCTION] when: ACTION when TRIGGER to OUTCOME. [Additional trigger examples within 600 characters] version: "1.0.0" last_updated: "<ISO 8601 date and time>" [optional_metadata_key]: [value for disambiguation or context]
</frontmatter_example>
Standard glob patterns must be unquoted (e.g., .js, src/**/.{ts,js})
Instructions
The assistant must apply the following transformation rules in order:
-
The assistant must begin content with a directive instructing how to read and apply all subsequent rules.
-
The assistant must maximize information density by employing technical jargon, domain-specific terminology, dense vocabulary, equations, and industry-standard nomenclature appropriate to the source material's subject matter.
-
The assistant must rephrase all content for maximum accuracy and specificity, eliminating vague guidance and ambiguous phrasing.
-
The assistant must write using declarative phrasing with "The model must" construction for all imperative instructions.
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The assistant must produce flat ASCII text without markdown formatting for bold, italic, or stylistic emphasis. Headings for hierarchy and lists are permitted.
-
The assistant must structure content into explicit sections: Table of Contents (when applicable), and References (when applicable), identity, intent, task rules, issue handling, triggers.
-
The assistant must preserve or expand any structured examples present in source text, wrapping them in appropriate XML tags (
<example>,<positive_example>,<negative_example>). -
The assistant must use XML tags (
<instructions>,<context>,<constraints>,<formatting>,<example>) to separate distinct prompt components and improve parseability. -
The assistant must format rules as ACTION TRIGGER OUTCOME patterns optimized for decision-making rather than education.
-
The assistant must establish clear priority levels between conflicting rules and instructions.
-
The assistant must target under 500 lines per file, splitting significant concepts into multiple composable rules when necessary.
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.
- 10d ago First seen · 100 lines · 9 tokens per session scan A 4ed508c4fe86
write-to-skill-file is a command published in the GitHub repository Jamie-BitFlight/claude_skills (66 stars, last pushed today), licensed MIT. It adds 9 tokens to every session and 1,057 once invoked, about $0.0000 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.