reimagine-work

reimagine-work is a skill for Claude Code, Codex from ericosiu/ai-marketing-skills. It costs 194 tokens per session (6,970 once invoked), scanned A, original, MIT.

A process-design guide for finding work that unnecessarily waits for a person and redesigning it for a team of agents. It uses a repeated cycle of questioning, removing, simplifying, speeding up, and automating steps.

In plain words
What is it for?
Use it to review an agent-based workflow, rank automation opportunities, and define when work should proceed or be escalated.
Why use it?
It helps identify human bottlenecks and avoid repeatedly asking for approval that has already been given. It proposes changes rather than sending messages, spending money, or approving actions.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to review an agent-based workflow, rank automation opportunities, and define when work should proceed or be escalated.

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Install with agentmods
npx agentmods add skills/ericosiu/ai-marketing-skills/reimagine-work
About the project

AI Marketing Skills is a collection of open-source workflows that help AI coding agents handle marketing and sales work, including growth experiments, pipeline management, content operations, outbound outreach, SEO, and finance analysis. It is intended for marketing and sales teams that want reusable agent-driven processes. The catalogue entries package these workflows as skills for compatible coding agents.

ericosiu/ai-marketing-skills · 3,784 stars · on GitHub · singlegrain.com

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.

Any agent
npx skills add ericosiu/ai-marketing-skills --skill reimagine-work
Clone the repo
git clone --depth 1 https://github.com/ericosiu/ai-marketing-skills

Made for: Claude Code, Codex.

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.

agentmods badge for reimagine-work

README.md
[![agentmods](https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/reimagine-work/github.svg)](https://agentmods.dev/skills/ericosiu/ai-marketing-skills/reimagine-work)
Your own site
<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/reimagine-work"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/reimagine-work/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.

agentmods 80×15 button for reimagine-work

Your own site · 80×15
<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/reimagine-work"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/reimagine-work.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 194 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,970 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00194 $0.06970
Opus 5.5 $0.00078 $0.02788
Sonnet 5.5 $0.00039 $0.01394
Haiku 4.5 $0.00019 $0.00697

Measured 5d ago against content hash f0266497d5b2, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

reimagine-work 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.

reimagine-work/skills/reimagine-work/SKILL.md · 476 lines

How it starts

The opening of the file, as written. The whole thing — 476 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reimagine work (v3.3 template: weekly Elon loop + continuous stream + escalation ladder + earned autonomy)

Template. Replace every <PLACEHOLDER> with your own values. Nothing in this skill sends, posts, spends or approves anything; it only proposes. The judge it refers to ships in shadow mode (log only) with hard gates on.

Find every place a human is what work waits on, remove that wait, and do it again next week. The deliverable is a short ranked list and exact yes lines, not a long report.

Asks that are already answered (never re-ask)

Before any yes line reaches the human, check these in order. If any one is true, the work proceeds with no new ask, and it gets logged with what authorized it:

  1. The human already said it. If the owner's own words in this conversation asked for this exact action ("send these follow-ups, by email and chat"), that is the yes. A copy tweak they asked for ("keep it simple like the outbound") is still inside that yes. Do not send a confirm line for the same batch.
  2. A lane owner approved it. If the person who owns the lane (for example, the content lead for publishing) approved, proceed under their conditions. Don't ask the top owner for a second yes.
  3. A standing approval with a cap covers it. Check the approval ledger. A matching row with owner_bots, a cap and a scope is the yes.
  4. It's reversible or read-only. No yes at all.

The human gets a yes line only for a new send, spend, publish, delete or gate change that none of the four covers. Each yes line that does reach them gets counted. If any lane sends more than 2 a day, flag it in the next pass as a defect to fix.

Where the idea comes from

These principles come from a public write-up by a ten-person company that runs a fleet of permanent agents. Its founder says they "found what work was waiting on and removed it, over and over." Use these principles to judge each redesign:

  1. Own an area. Each agent is permanent and owns one part of the company. Two agents doing the same job is a SIMPLIFY finding.
  2. Coordinate through the world, not through messages. Agents stop "telling each other what they were about to do." They read and change a shared record (queue, ledger, state file). A human relaying a message between agents is a finding.
  3. Fix forward. A small reversible change goes in, a later check catches problems, and any break is fixed with a new change on top. Asking before a reversible internal fix is a finding.
  4. Test on a clock, not per change. Run the full check on a schedule against the latest state. "It broke since the last run" is enough to find the cause.
  5. Review by someone who didn't write it. Helper agents try to break a change before it lands.
  6. Agents read production themselves. Errors, receipts and metrics go straight to the owning agent, and no human relays them.
  7. A stall is an incident. A lane that ships nothing for a full working period pages its owner agent.
  8. AI judges from recorded decisions. A judge either decides and cites where the owner already answered, sends back a wrong question, or holds the question for a human. "When a judge gets one wrong we say so, and the judges read that next time."
  9. Nothing waits on the queue. Open asks that sit for days are the loop's main measure of failure.
  10. Hard gates stay small and dumb. Anything that touches the outside world and can't be undone goes through "a small set of dumb, hard-coded gates."
  11. Humans keep taste. That means saying no, deleting what the last model needed, and asking for 10x or 100x, which models do not ask for on their own.

Read the full file on GitHub · 476 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.

  1. 5d ago Changed · +84 lines · +31 tokens per session f0266497d5b2
  2. 6d ago First seen · 392 lines · 163 tokens per session scan A 3632dfcdaa01

Subscribe to this mod's changes

reimagine-work is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,784 stars, last pushed 5d ago), licensed MIT. It adds 194 tokens to every session and 6,970 once invoked, about $0.0008 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 2026-10-31.

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