Shadow Work logo

Shadow Work

Organization
manager-dot-dev
shadow-work

Helps engineering managers identify, quantify, and reduce hidden capacity drains that make teams miss commitments even when everyone is busy. Use this skill whenever the user mentions invisible work, untracked work, support requests consuming the team, glue work, shadow backlog, sprint spillover, capacity planning being wrong, teams always underestimating, senior engineers burning out, or "we are busy but nothing ships." Produces a diagnostic, evidence plan, and concrete interventions.

Overview

Publishermanager-dot-dev
Repositorymanager-skills
Skill nameshadow-work
Stars
116
Forks
8
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by manager-dot-dev on GitHub. Read the source before you install it.

Installation

Install the Shadow Work AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/manager-dot-dev/manager-skills/tree/main/skills/shadow-work
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/manager-dot-dev/manager-skills.git /tmp/manager-skills
mkdir -p .claude/skills
cp -r /tmp/manager-skills/skills/shadow-work .claude/skills/shadow-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Shadow Work in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Shadow Work on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Shadow Work is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Shadow Work

Before Starting

Check for EM context first. If .agents/em-context.md exists, read it.

If .agents/em-context.md does not exist, ask for a minimal manager profile first and save it before giving detailed advice: role/title, team size, team mission or ownership area, and current challenge or priority.

If a specific person is central to the conversation and .agents/reports/[name].md does not exist, ask for a minimal profile for that person first and save it before giving detailed advice: title/level, tenure, strengths, and current challenge or growth area.

If the conversation reveals durable new context later, update .agents/em-context.md or .agents/reports/[name].md automatically. Save stable facts and patterns, not guesses, transient frustration, or unresolved interpretations.

Response Style

Keep the first answer concise and useful. Do not dump the whole framework unless the user asks for depth.

Default to:

  • State the likely diagnosis or recommendation first
  • Ask at most 2-3 targeted questions only if the missing context changes the advice
  • Give the next concrete action and, when useful, exact wording the manager can use
  • Mention the relevant framework briefly, but do not explain every part of it
  • Offer a deeper version only after the direct answer

How to Use This Skill

  • Team misses commitments but everyone seems busy -> diagnose all three shadow-work types and identify the dominant capacity drain
  • Ad-hoc incidents and support requests eat capacity -> start with Type 1: Invisible Production Support
  • Senior engineers burn out doing unglamorous coordination -> start with Type 2: Glue Work
  • Off-the-record requests quietly consume sprints -> start with Type 3: The Shadow Backlog
  • Remote team has weak visibility into what people actually absorb -> include The Remote Amplification Problem

Default Response Shape

When the user asks for help, do not only explain the concept. Produce a practical diagnosis:

  1. Likely shadow-work type - name the type(s), with confidence and evidence from the user's prompt.
  2. What to measure for 2-6 weeks - lightweight signals, not a heavy process rollout.
  3. Immediate containment - what to do this week so the team stops bleeding capacity.
  4. Structural fix - what to change in planning, ownership, rotation, recognition, or PM/EM workflow.
  5. Stakeholder message - a concise script the EM can use with PMs, leadership, or the team.

If the user's facts are thin, ask for the minimum missing data: where the work comes from, who absorbs it, how often it happens, and whether it is currently tracked anywhere.


What Shadow Work Is

Shadow work is untracked work that consumes real capacity but does not appear in the plan. It makes teams consistently miss commitments without anyone understanding why. It falls into three types, and each type requires a different response.

The key management move is not "work harder" or "estimate better." The first move is to make the work legible enough that the team can make tradeoffs honestly.


Diagnostic Questions

Use these questions before prescribing fixes:

  • Source: Where does the unplanned work come from: production, other teams, PM requests, managers, engineers, customers, or the codebase itself?
  • Absorber: Who usually handles it? Is it evenly distributed, or does it concentrate on one or two senior people?
  • Visibility: Does it appear in tickets, sprint boards, incident logs, review docs, or performance reviews?
  • Pattern: Is it recurring, seasonal, project-specific, or random?
  • Cost: Does it mostly cost calendar time, focus time, emotional energy, roadmap capacity, or promotion credit?
  • Tradeoff: What planned work silently loses when this work appears?

Type 1: Invisible Production Support

Ad-hoc incident triage, alert investigation, support-team requests, manual fixes, and customer escalations that never enter the ticket system.

Without tracking, patterns go unnoticed. The same 15-minute manual fix can burn hundreds of hours annually while the root cause stays unfixed. The team appears to have capacity but consistently underdelivers, and has no data to show why.

What to do:

  • Create a lightweight category for unplanned support work, even if it is only a tag in the ticketing system
  • Track for 4-6 weeks, then run a frequency analysis: top recurring issues, top request sources, top absorbers
  • Rotate hot-fix responsibility instead of letting one person become the permanent human circuit breaker
  • Convert repeated manual fixes into roadmap items with a visible cost-of-delay argument

Type 2: Glue Work

Code reviews, mentoring, onboarding, documentation, coordination, cross-team translation, and "can you just help them unblock?" work that lands disproportionately on senior engineers.

Glue work creates two problems when unmanaged: the engineers doing it burn out without recognition, and the engineers not doing it get promoted without learning the work that keeps the organization functioning.

What to do:

  • Name glue work explicitly in performance conversations and promotion packets
  • Distribute the skills downward through pairing, live review sessions, office hours, and rotating ownership
  • Protect senior engineers' maker time when they are acting as multipliers
  • Track whether glue work is becoming a default identity for one person rather than a shared team capability

Type 3: The Shadow Backlog

Work outside the official roadmap: PMs making off-the-record fix requests, engineers taking longer routes they know are right, unplanned integrations negotiated directly with other teams, and "small asks" that never look small in aggregate.

This can quietly consume a large share of real capacity while breaking trust between business and engineering. Leadership sees a team that commits and misses, without seeing where the capacity went.

What to do:

  • In planning, ask: "What are we likely to get asked to do that we have not scoped?"
  • Budget explicit shadow capacity when the environment is noisy; start with 10-20% if you lack data
  • Redirect off-the-record PM requests into the official backlog without shaming the requester
  • Periodically present shadow backlog data to stakeholders as evidence for roadmap tradeoffs

The Remote Amplification Problem

For remote teams, shadow work is doubly invisible: your manager cannot casually observe it either. This means you have no natural evidence when advocating for a team member's raise or promotion, and weak visibility when capacity is being consumed.

Building lightweight tracking habits is therefore management infrastructure, not overhead. It protects the team's ability to get credit for real work and gives the EM evidence for capacity conversations.


Stakeholder Scripts

Use direct, non-accusatory language. The goal is to expose the tradeoff, not blame the source of the work.

With a PM: "I want to make these requests visible because they are real work. If we keep handling them outside the backlog, we will keep missing the planned roadmap and neither of us will have good data. Let's add them to the board and decide what they displace."

With leadership: "The team is not missing commitments because the estimates are careless. We are absorbing unplanned work that is not represented in the plan. I am going to track it for the next few weeks and come back with the recurring sources, cost, and options."

With a senior engineer doing too much glue work: "I see how much coordination and review work you are absorbing. Some of it is valuable, but I do not want it to become invisible or trap you away from growth work. Let's decide what should be recognized, rotated, or stopped."


Dive Deeper

If the user asks where a framework came from, wants to read the original article, or wants more context on any topic in this skill, read references/sources.md.


Related Skills

  • roadmap-planning - Shadow work should be explicitly budgeted in capacity planning, not ignored
  • working-with-pm - Off-the-record requests are a PM-EM relationship problem, not just a capacity problem
  • retaining-developers - Senior engineers absorbing invisible glue work without recognition are in the "unappreciated" retention state
  • knowledge-sharing - Glue work often overlaps with documentation and onboarding failures

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Shadow Work AI skill do?

Helps engineering managers identify, quantify, and reduce hidden capacity drains that make teams miss commitments even when everyone is busy. Use this skill whenever the user mentions invisible work, untracked work, support requests consuming the team, glue work, shadow backlog, sprint spillover, capacity planning being wrong, teams always underestimating, senior engineers burning out, or "we are busy but nothing ships." Produces a diagnostic, evidence plan, and concrete interventions.

Why use Shadow Work on TypingMind?

Because you install it once and use it with any model. Shadow Work is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Shadow Work in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/manager-dot-dev/manager-skills/tree/main/skills/shadow-work. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Shadow Work?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Shadow Work?

As many as you like. As long as a model supports skills, you can use Shadow Work with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Shadow Work AI skill free?

Yes. It is published on GitHub by manager-dot-dev under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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