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 axiomantic/spellbook --skill sharpening-promptsgit clone --depth 1 https://github.com/axiomantic/spellbookWrote 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/axiomantic/spellbook/sharpening-prompts)<a href="https://agentmods.dev/skills/axiomantic/spellbook/sharpening-prompts"><img src="https://agentmods.dev/badge/skills/axiomantic/spellbook/sharpening-prompts/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/axiomantic/spellbook/sharpening-prompts"><img src="https://agentmods.dev/badge/skills/axiomantic/spellbook/sharpening-prompts.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 | $0.00090 | $0.01824 |
| Opus 5.5 | $0.00036 | $0.00730 |
| Sonnet 5.5 | $0.00018 | $0.00365 |
| Haiku 4.5 | $0.00009 | $0.00182 |
Grade A, and why
sharpening-prompts 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 1mo 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sharpening Prompts
This is very important to my career. You'd better be sure.
Core Question
"Where would an LLM executor have to guess?"
Ask this for every statement: if an LLM reads this with no additional context, what would it invent to fill the gaps?
Reasoning Schema
Invariant Principles
- Ambiguity compounds: One vague instruction becomes many guessed decisions downstream.
- LLMs fill gaps confidently: They won't ask — they'll invent plausible-sounding specifics.
- Context is not telepathy: The executor has only what's written, not what you meant.
- Clarification beats inference: When you can't resolve ambiguity from context, ask the author.
- Specificity enables verification: Vague success criteria can't be tested.
Inputs / Outputs
| Input | Required | Description |
|---|---|---|
prompt_text |
Yes | The prompt/instructions to review (inline or file path) |
mode |
No | audit (report findings) or improve (rewrite prompt). Default: audit |
context_files |
No | Additional files for resolving ambiguities |
author_available |
No | If true, can ask clarifying questions. Default: false |
| Output | Type | Description |
|---|---|---|
findings_report |
Inline | Categorized findings with severity and remediation |
improved_prompt |
Inline/File | Rewritten prompt (improve mode only) |
clarification_requests |
Inline | Questions for author if ambiguities unresolvable |
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.
- 1mo ago First seen · 189 lines · 90 tokens per session scan A e75d293702c9
sharpening-prompts is a skill published in the GitHub repository axiomantic/spellbook (10 stars, last pushed 2d ago), licensed MIT. It adds 90 tokens to every session and 1,824 once invoked, about $0.0004 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 2026-09-03.
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