Borrowing it
Nothing to install: this file belongs to nklisch/claude-code-modes. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nklisch/claude-code-modes/main/.claude/skills/prompt-author/SKILL.mdgit clone --depth 1 https://github.com/nklisch/claude-code-modesWrote 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/nklisch/claude-code-modes/prompt-author)<a href="https://agentmods.dev/skills/nklisch/claude-code-modes/prompt-author"><img src="https://agentmods.dev/badge/skills/nklisch/claude-code-modes/prompt-author/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/nklisch/claude-code-modes/prompt-author"><img src="https://agentmods.dev/badge/skills/nklisch/claude-code-modes/prompt-author.svg" alt="Reviewed on agentmods" width="80" 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 Excessive Agency · line 70 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00077 | $0.01374 |
| Opus 5.5 | $0.00031 | $0.00550 |
| Sonnet 5.5 | $0.00015 | $0.00275 |
| Haiku 4.5 | $0.00008 | $0.00137 |
Grade A, and why
prompt-author 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 2mo 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Author
Create custom prompt content for claude-code-modes — bases, modifiers, or axis values — grounded in Anthropic's emotion research.
Before starting
Read these references:
- references/emotion-research.md — emotion vector findings and principles
- references/prompt-quality.md — 10 quality criteria
- references/base-format.md — manifest and fragment specs
- references/examples.md — chill base as worked example
Read the existing prompts to understand the current patterns:
prompts/base/— standard base fragmentsprompts/chill/— chill base fragments (emotion-research-informed)prompts/modifiers/— existing modifiersprompts/axis/— existing axis values
Phase 1: Determine what to create
AskUserQuestion: What type of prompt content?
- Base — a complete foundational prompt (directory + manifest + fragments)
- Modifier — a behavioral layer that composes on top of any base/preset
- Axis value — a custom value for agency, quality, or scope
Phase 2: Interview about desired behavior
Ask the user conversationally (not all at once):
For a base:
- What's the philosophy? (e.g., minimal, domain-specific, team-flavored)
- What behaviors matter most?
- What tone? (direct, warm, formal, casual)
- How does it differ from standard and chill?
- What fragments should it have? (consolidate like chill, or granular like standard?)
For a modifier:
- What behavior does this shape? (investigation, craftsmanship, caution, speed)
- When would someone use it? (debugging, reviewing, prototyping)
- What should the agent do differently with this modifier active?
- What should it explicitly NOT do?
For an axis value:
- Which axis? (agency, quality, scope)
- What behavior does this value represent?
- How does it differ from the existing built-in values?
Phase 3: Draft the content
Apply these principles from the emotion research (see references/emotion-research.md):
What ships with it
4 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.
- 2mo ago First seen · 127 lines · 77 tokens per session scan A d5989906ccb4
prompt-author is a skill published in the GitHub repository nklisch/claude-code-modes (117 stars, last pushed 29d ago), licensed MIT. It adds 77 tokens to every session and 1,374 once invoked, about $0.0003 per session on Opus 5.5. 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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