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 sugarforever/01coder-agent-skills --skill share-readinggit clone --depth 1 https://github.com/sugarforever/01coder-agent-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/skills/sugarforever/01coder-agent-skills/share-reading)<a href="https://agentmods.dev/skills/sugarforever/01coder-agent-skills/share-reading"><img src="https://agentmods.dev/badge/skills/sugarforever/01coder-agent-skills/share-reading/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/sugarforever/01coder-agent-skills/share-reading"><img src="https://agentmods.dev/badge/skills/sugarforever/01coder-agent-skills/share-reading.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00083 | $0.01571 |
| Opus 5.5 | $0.00033 | $0.00628 |
| Sonnet 5.5 | $0.00017 | $0.00314 |
| Haiku 4.5 | $0.00008 | $0.00157 |
Grade A, and why
share-reading 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Share Reading
Help draft social media posts for sharing valuable readings, articles, tools, or resources. Generates multiple candidate posts with appropriate tone and style, ready for publishing on X, Substack, or 知识星球.
Workflow
Step 1: Process Input
Determine the input type and extract content accordingly:
If input is a URL:
- Use WebFetch to retrieve the page content
- Extract: title, author, key points, publication date
- Preserve the original URL for inclusion in the post
If input is a file path:
- Use Read to load the file
- Extract the same metadata from frontmatter or content
If input is direct text/notes:
- Use the provided content as-is
- Ask for the source URL if not included
Step 2: Understand Context
Before writing, consider:
- What makes this worth sharing? (insight, practical value, novelty, controversy)
- Who would care about this? (developers, AI enthusiasts, general tech audience)
- What's the user's likely angle? (recommendation, commentary, discussion)
If the intent is unclear, ask briefly:
这篇内容你想从什么角度分享?比如:
- 推荐给大家(觉得很有价值)
- 分享某个观点/发现
- 提出讨论/问题
- 其他想法?
Step 3: Generate Candidates
Produce 2-3 candidate posts with varying approaches. Each candidate should:
- Include the source link — always present, naturally placed
- Be self-contained — readers should understand the value without clicking
- Match the platform tone — see Platform Guidelines below
Candidate Approaches (pick 2-3 that fit)
- Summary + takeaway: Concise summary with a personal takeaway or opinion
- Key highlight: Pull out the most striking point or quote, add brief context
- Question/discussion: Frame a question around the content to spark engagement
- Practical angle: Focus on actionable value — "if you're doing X, read this because Y"
- Contrarian/fresh take: Offer a perspective the article didn't cover
Step 4: Present to User
Present candidates clearly labeled, e.g.:
## 候选 1:总结推荐型
{content}
## 候选 2:观点提炼型
{content}
## 候选 3:讨论引导型
{content}
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 · 180 lines · 83 tokens per session scan A 7df9f361c29d
share-reading is a skill published in the GitHub repository sugarforever/01coder-agent-skills (139 stars, last pushed 18d ago), licensed MIT. It adds 83 tokens to every session and 1,571 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.
share-reading compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| share-readingsugarforever/01coder-agent-skills | Skill | 139 | 18d ago | MIT | A | 83 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |