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 jamesgray-ai/handsonai-plugins --skill editing-hbr-articlesgit clone --depth 1 https://github.com/jamesgray-ai/handsonai-pluginsWrote 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/jamesgray-ai/handsonai-plugins/editing-hbr-articles)<a href="https://agentmods.dev/skills/jamesgray-ai/handsonai-plugins/editing-hbr-articles"><img src="https://agentmods.dev/badge/skills/jamesgray-ai/handsonai-plugins/editing-hbr-articles/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/jamesgray-ai/handsonai-plugins/editing-hbr-articles"><img src="https://agentmods.dev/badge/skills/jamesgray-ai/handsonai-plugins/editing-hbr-articles.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 | $0.00075 | $0.00531 |
| Opus 5.5 | $0.00030 | $0.00212 |
| Sonnet 5.5 | $0.00015 | $0.00106 |
| Haiku 4.5 | $0.00007 | $0.00053 |
Grade A, and why
editing-hbr-articles 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.
What it actually says
Editing HBR Articles
Edit articles to Harvard Business Review publication standards. Make direct, prescriptive edits.
Workflow
- Read the article completely before making any edits
- Assess against criteria in references/editorial-criteria.md
- Make edits directly to the file, focusing on highest-impact issues first
- Provide summary of changes made and rationale
Edit Priority Order
Address issues in this order:
- Structure: Does the opening hook? Is the thesis clear by paragraph 3? Do sections flow logically?
- Evidence: Are claims supported with specific data, named companies, and credible sources?
- Redundancy: Cut repeated points, excessive examples (keep 2-3 strong ones), and summary conclusions that rehash the intro
- Voice: Remove hedging, jargon, and promotional language. Strengthen to active voice.
- Length: Target 2,500-3,500 words for features, 1,500-2,500 for thought leadership
What to Cut
- Throat-clearing openings ("In today's rapidly changing...")
- Redundant examples after a point is proven
- Conclusions that repeat the introduction
- Hedge words ("perhaps," "it seems," "might")
- Business jargon (see editorial-criteria.md for list)
What to Strengthen
- Vague claims → specific data ("improved efficiency" → "reduced costs by 30%")
- Unnamed examples → named companies and executives
- Passive voice → active voice
- Generic statements → concrete implications for the reader
Output Format
After editing, provide:
## Editorial Summary
**Major changes:**
- [List 2-4 significant structural or content changes]
**Tightening:**
- Word count: [before] → [after] ([X]% reduction)
- [Key cuts made]
**Remaining considerations:**
- [Any optional improvements or decisions for the author]
Save the edited article to the same file path.
What ships with it
1 file 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 · 67 lines · 75 tokens per session scan A 251528e73a9d
editing-hbr-articles is a skill published in the GitHub repository jamesgray-ai/handsonai-plugins (8 stars, last pushed yesterday), licensed MIT. It adds 75 tokens to every session and 531 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 2026-08-31.
editing-hbr-articles compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| editing-hbr-articlesjamesgray-ai/handsonai-plugins | Skill | 8 | yesterday | MIT | A | 75 | 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 |