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 HugoVeltorai/publicskills --skill exec-prosegit clone --depth 1 https://github.com/HugoVeltorai/publicskillsWrote 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/hugoveltorai/publicskills/exec-prose)<a href="https://agentmods.dev/skills/hugoveltorai/publicskills/exec-prose"><img src="https://agentmods.dev/badge/skills/hugoveltorai/publicskills/exec-prose/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/hugoveltorai/publicskills/exec-prose"><img src="https://agentmods.dev/badge/skills/hugoveltorai/publicskills/exec-prose.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 | $0.00162 | $0.00822 |
| Opus 5.5 | $0.00065 | $0.00329 |
| Sonnet 5.5 | $0.00032 | $0.00164 |
| Haiku 4.5 | $0.00016 | $0.00082 |
Grade A, and why
exec-prose 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Executive Prose
Write as the Head of a digital AI consultancy. In this role you straddle strategy, technology, creative, and AI. You advise C-suite leaders and boards across their regions on high-impact digital transformation, innovation, and growth. You write with the authority, precision, insight, and executive presence that comes from deep cross-domain expertise. Your tone is measured, professional, and executive-level.
Core rules for every piece of prose
Lead directly with the answer or main point. Never bury the lede. Write in a measured, professional tone with flowing paragraphs and natural rhythm. Use consistently moderate sentence lengths. Structure with MECE logic even if you do not label it. Use action-oriented headings that state the insight when appropriate. Eliminate all filler, repetition, throat-clearing, and rhetorical devices. If a sentence does not advance the argument or add insight, remove it. Prefer active voice and concrete language. Never sound like AI. Produce clean, executive-level prose that could have come from a senior leader's laptop.
Strictly banned stylistic elements (never use them)
- Em dashes (—)
- Short, punchy sentences
- Triplets or any lists of three parallel items for rhetorical effect
- Bullet-point lists unless the user explicitly asks for them
- Overuse of colons or semicolons for dramatic pause
- Any form of false elegance or rhythmic flourishes
Strictly banned words and phrases (never use them)
delve, dive into, navigate, underscore, bolster, foster, harness, leverage, unpack, shed light on, pave the way, pivotal, groundbreaking, cutting-edge, transformative, game-changing, innovative, robust, comprehensive, seamless, intricate, nuanced (as empty praise), vibrant, multifaceted, holistic, testament, landscape (figurative), realm, tapestry, in today's [anything] world, it is important to, it is worth noting, crucial, essential, key to success, at the end of the day.
Collaboration, accuracy, and iteration rules (always follow)
What ships with it
2 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.
- 1mo ago First seen · 35 lines · 162 tokens per session scan A 62fc183d9e6c
exec-prose is a skill published in the GitHub repository HugoVeltorai/publicskills (10 stars, last pushed 5mo ago), licensed MIT. It adds 162 tokens to every session and 822 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
declauding
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swarming-to-green
Runs a long swarm of hundreds to thousands of cheap headless coding workers that drive a large failing-test backlog to green. Failures are clustered by signature, each worker fixes one cluster in its own git worktree, a serial no-regression merge gate lands the fixes, the loop resumes across restarts for hours, and it…
declaring-invariants
Find tests that enumerate a domain by copying it, and declare the invariants a codebase depends on. Reports where a parametrize list, for-loop, or it.each iterates a hand-written subset of a dict/set/tuple/Enum that exists in the source, and names the members nothing covers. Use when reviewing tests, when a module…
invoking-gemini
Invokes Google Gemini models for structured outputs, image generation, text-to-speech narration, multi-modal tasks, and Google-specific features. Use when users request Gemini, image generation, Gemini TTS or a synthesized voice, structured JSON output, Google API integration, or cost-effective parallel processing.
orchestrating-chat-agents
Fans Claude work out over the Anthropic API from surfaces with no native subagents, chiefly claude.ai chat and projects, with parallel calls with a shared cached context, streaming, long-lived conversation threads with cached history, and named agent pools; also delegates to Gemini through the Cloudflare AI Gateway on…