An automatic-delegation workflow for producing a Harvard Business Review–style business article with help from specialist agents.

Use it to create a publication-quality article for senior business leaders, delivered as Markdown and Word files.

It coordinates research, writing, editing, and other work while choosing which specialists to use and when. The finished article is expected to be evidence-based and cited.

hbr-article — Command for Claude Code by jamesgray-ai

Install

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.

Clone the repo

git clone --depth 1 https://github.com/jamesgray-ai/handsonai-plugins

Made for: Claude Code.

Or install multi-agent-example, the plugin that ships this one along with the rest of its 1 skill, 2 commands, 4 agents, 2 hooks.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/jamesgray-ai/handsonai-plugins/hbr-article/github.svg)](https://agentmods.dev/commands/jamesgray-ai/handsonai-plugins/hbr-article)

Your own site

<a href="https://agentmods.dev/commands/jamesgray-ai/handsonai-plugins/hbr-article"><img src="https://agentmods.dev/badge/commands/jamesgray-ai/handsonai-plugins/hbr-article/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.

Your own site · 80×15

<a href="https://agentmods.dev/commands/jamesgray-ai/handsonai-plugins/hbr-article"><img src="https://agentmods.dev/badge/commands/jamesgray-ai/handsonai-plugins/hbr-article.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 17 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,184 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00017	$0.01184
Opus 5.5	$0.00007	$0.00474
Sonnet 5.5	$0.00003	$0.00237
Haiku 4.5	$0.00002	$0.00118

Measured 2mo ago against content hash 65f1196c4aa3, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

hbr-article 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.

plugins/multi-agent-example/commands/hbr-article.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Goal

Produce a Harvard Business Review–style article, for a senior business leadership audience, on: $1

If $1 is empty, the topic is: companies that have successfully deployed AI agents in their business, and what separated them from the ones still running pilots.

Done means two files exist on disk: the article as markdown, and the article as a Word document. Publication quality, and defensible — every factual claim traceable to a credible, cited source.

How to approach this

You have specialist subagents available, and you decide which to use and when. This is deliberate: read the available agent descriptions, work out which specialists this goal calls for, and delegate to them in whatever order the work requires. Nobody is handing you a sequence.

Three rules constrain how you delegate, not what you choose:

Do not do a specialist's work yourself. If a specialist exists for a part of this goal, dispatch it rather than doing that part in your own context. This is the whole point — you are coordinating experts, not doing the job with help. Notably: do not research the case studies yourself, and do not write or edit the prose yourself.
Every handoff goes through a file, and every subagent gets the absolute workspace path. Subagents cannot see your context or each other's. If you don't tell an agent where to read from and write to, the chain breaks.
Keep your own context clean. Subagents return short summaries plus paths by design. Don't pull whole dossiers or drafts into your context; read a specific file only when you need a specific passage.

Workspace

Choose a short kebab-case slug for the topic. Create outputs/articles/<slug>/.
Write this brief, with the resolved topic, to <workspace>/00-goal.md.
Arm the quality gates:
```
echo "outputs/articles/<slug>" > outputs/articles/.active-run
```
The hooks are inert until this flag exists. Remove it when the run ends, including if you abandon the run.

Read the full file on GitHub · 109 lines

Changes

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 · 109 lines · 17 tokens per session scan A 65f1196c4aa3

Subscribe to this mod's changes

hbr-article is a command published in the GitHub repository jamesgray-ai/handsonai-plugins (8 stars, last pushed yesterday), licensed MIT. It adds 17 tokens to every session and 1,184 once invoked, about $0.0001 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.

Building agents Writing and docs

hbr-article next to 6 commands from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
hbr-articlejamesgray-ai/handsonai-plugins	Command	8	yesterday	MIT	A	17	Claude Code
checklistgithub/spec-kit	Command	148k	yesterday	MIT	A	13	Claude Code
clarifygithub/spec-kit	Command	148k	yesterday	MIT	A	27	Claude Code
specifygithub/spec-kit	Command	148k	yesterday	MIT	A	13	Claude Code
analyzegithub/spec-kit	Command	148k	yesterday	MIT	A	26	Claude Code
convergegithub/spec-kit	Command	148k	yesterday	MIT	A	35	Claude Code
implementgithub/spec-kit	Command	148k	yesterday	MIT	A	14	Claude Code