Knowledge Work Plugins is an open-source collection of Claude extensions organized around roles such as productivity, sales, and customer support. Each plugin combines role-specific guidance, connectors, commands, and sub-agents so knowledge workers can use Claude with their team’s tools and processes. The catalogue entries are examples of, or workflows from, this plugin collection.
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 anthropics/knowledge-work-plugins --skill scientific-problem-selectiongit clone --depth 1 https://github.com/anthropics/knowledge-work-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/anthropics/knowledge-work-plugins/scientific-problem-selection)<a href="https://agentmods.dev/skills/anthropics/knowledge-work-plugins/scientific-problem-selection"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/scientific-problem-selection/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/anthropics/knowledge-work-plugins/scientific-problem-selection"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/scientific-problem-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00118 | $0.02552 |
| Opus 5.5 | $0.00047 | $0.01021 |
| Sonnet 5.5 | $0.00024 | $0.00510 |
| Haiku 4.5 | $0.00012 | $0.00255 |
Grade A, and why
scientific-problem-selection 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 1mo 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.
Copies of this mod
5 near-identical copies found in the catalogue:
- scientific-problem-selection — 100% identical, 0 lines differ
- scientific-problem-selection — 100% identical, 0 lines differ
- scientific-problem-selection — 100% identical, 0 lines differ
- scientific-problem-selection — 100% identical, 9 lines differ
- scientific-problem-selection — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientific Problem Selection Skills
A conversational framework for systematic scientific problem selection based on Fischbach & Walsh's "Problem choice and decision trees in science and engineering" (Cell, 2024).
Getting Started
Present users with three entry points:
1) Pitch an idea for a new project — to work it up together
2) Share a problem in a current project — to troubleshoot together
3) Ask a strategic question — to navigate the decision tree together
This conversational entry meets scientists where they are and establishes a collaborative tone.
Option 1: Pitch an Idea
Initial Prompt
Ask: "Tell me the short version of your idea (1-2 sentences)."
Response Approach
After the user shares their idea, return a quick summary (no more than one paragraph) demonstrating understanding. Note the general area of research and rephrase the idea in a way that highlights its kernel—showing alignment and readiness to dive into details.
Follow-up Prompt
Then ask for more detail: "Now give me a bit more detail. You might include, however briefly or even say where you are unsure:
- What exactly you want to do
- How you currently plan to do it
- If it works, why will it be a big deal
- What you think are the major risks"
Workflow
From there, guide the user through the early stages of problem selection and evaluation:
- Skill 1: Intuition Pumps - Refine and strengthen the idea
- Skill 2: Risk Assessment - Identify and manage project risks
- Skill 3: Optimization Function - Define success metrics
- Skill 4: Parameter Strategy - Determine what to fix vs. keep flexible
See references/01-intuition-pumps.md, references/02-risk-assessment.md, references/03-optimization-function.md, and references/04-parameter-strategy.md for detailed guidance.
Option 2: Troubleshoot a Problem
Initial Prompt
Ask: "Tell me a short version of your problem (1-2 sentences or whatever is easy)."
Response Approach
After the user shares their problem, return a quick summary (no more than one paragraph) demonstrating understanding. Note the context of the project where the problem occurred and rephrase the problem—highlighting its core essence—so the user knows the situation is understood. Also raise additional questions that seem important to discuss.
What ships with it
10 files 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.
- LICENSE.txt 10 KB
- references/01-intuition-pumps.md 12 KB
- references/02-risk-assessment.md 13 KB
- references/03-optimization-function.md 18 KB
- references/04-parameter-strategy.md 14 KB
- references/05-decision-tree.md 3.2 KB
- references/06-adversity-planning.md 4.6 KB
- references/07-problem-inversion.md 5.5 KB
- references/08-integration-synthesis.md 5.3 KB
- references/09-meta-framework.md 16 KB
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.
- 1mo ago First seen · 270 lines · 118 tokens per session scan A 9ec99e3e5b2f
scientific-problem-selection is a skill published in the GitHub repository anthropics/knowledge-work-plugins (29,887 stars, last pushed yesterday), licensed Apache-2.0. It adds 118 tokens to every session and 2,552 once invoked, about $0.0005 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.
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