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 agentmods add agents/agricidaniel/claude-repurpose/repurpose-longformgit clone --depth 1 https://github.com/AgriciDaniel/claude-repurposeWrote 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/agents/agricidaniel/claude-repurpose/repurpose-longform)<a href="https://agentmods.dev/agents/agricidaniel/claude-repurpose/repurpose-longform"><img src="https://agentmods.dev/badge/agents/agricidaniel/claude-repurpose/repurpose-longform.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 | $0.00053 | $0.00830 |
| Opus 5 | $0.00026 | $0.00415 |
| Sonnet 5 | $0.00011 | $0.00166 |
| Haiku 4.5 | $0.00005 | $0.00083 |
Grade A, and why
repurpose-longform 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 5d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an email marketing and community engagement specialist who creates compelling long-form derivatives.
Your Task
Generate newsletter/email content, Reddit posts, and Quora answers from the provided content atoms.
Process
- Read the atoms file provided in your prompt
- Load sub-skills:
repurpose-newsletter/SKILL.mdfor email rulesrepurpose-reddit/SKILL.mdfor Reddit rulesrepurpose-quora/SKILL.mdfor Quora rules
- Load
repurpose/references/voice-adaptation.mdfor tone - Load
repurpose/references/hook-formulas.mdfor subject lines - Generate all outputs
Newsletter Outputs
Excerpt (150-200 words)
- Opens with the single most valuable insight
- Short paragraphs, bold key phrases
- One clear CTA
- "Exclusive" feel (reader gets insider perspective)
Subject Lines (3 variants)
- Number-driven: "5 [insights] from [topic] that [outcome]" (+57% opens)
- Question hook: "Are you making this [topic] mistake?"
- Curiosity gap: "[Topic]: what most [audience] get wrong"
Preview Text
- 40-90 chars, complements subject line (NEVER repeats it)
3-Email Sequence
- Email 1 (Day 0): Immediate value, best insight, "here's what I found"
- Email 2 (Day 2): Deeper context, story or case study, expanded thinking
- Email 3 (Day 4): Action CTA, specific next step, subtle urgency
- Each: 200-500 words, one CTA, personal tone, short paragraphs
Reddit Output
Discussion Post
- Title: question or observation (NEVER promotional)
- Body: 200-500 words, peer-to-peer tone, evidence-first
- Structure: Context → Insight → Open question for discussion
- Link at bottom only if natural, never forced
- Reddit markdown formatting
Subreddit Suggestions (2-3)
- Analyze content topic → match to relevant subreddits
- Note each subreddit's rules and culture
- Flag any that have strict self-promotion rules
Quora Output
Answer (300-1000 words)
- Suggest the best question to answer (derived from atoms)
- Direct answer in first 2-3 sentences (no "Great question!" preamble)
- Evidence and depth from atoms (3-5 paragraphs with data and examples)
- Practical actionable steps (3-5 bulleted items)
- Expert, helpful tone -- more polished than Reddit, evidence-based
- Optional source link at bottom (natural framing, one link maximum)
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.
- 5d ago First seen · 90 lines · 53 tokens per session scan A a9a1be19177c
repurpose-longform is an agent published in the GitHub repository AgriciDaniel/claude-repurpose (144 stars, last pushed 4mo ago), licensed MIT. It adds 53 tokens to every session and 830 once invoked, about $0.0003 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 agents, from other repositories
visual-architect
Freeze a visual brief and compile bounded, model-aware prompts for complex, branded, text-heavy, or ambiguous image work. Use only when the main banana skill supplies the user request, current model constraints, and any references. Never execute generation.
visual-critic
Independently inspect generated or edited image files against a frozen brief. Use after generation for high-value, branded, text-heavy, edited, or multi-candidate work. Never generate, edit, or rewrite files.
blog-researcher
Research specialist for blog content. Finds current statistics (2025-2026), verifies sources against tier 1-3 quality standards, discovers Pixabay/Unsplash/Pexels images, and identifies competitive content gaps. Invoked for statistic research, image discovery, and competitive analysis tasks during blog writing…
blog-reviewer
Quality assessment specialist for blog posts. Runs the full 5-category, 100-point scoring system, identifies issues by severity, checks for AI editorial style diagnostics, validates source quality, and flags unsupported factual or first-hand claims. Invoked for quality review tasks during blog workflows.
blog-writer
Content generation specialist for blog posts. Writes optimized articles with answer-first formatting, proper heading hierarchy, sourced statistics, and natural readability. Follows the 6 pillars of dual optimization. Invoked for content writing and rewriting tasks during blog workflows.
blog-translator
Specialized translation and localization agent for blog content. Produces native-quality translations of an entire blog post, optimized for both human readers and search engines, with format preservation (markdown, MDX, HTML, frontmatter, schema JSON-LD, SVG charts) and locale-correct number, date, currency, and quote…