digest▌
anthropics/knowledge-work-plugins · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Digest Command
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Scan recent activity across all connected sources and generate a structured digest highlighting what matters.
Instructions
1. Parse Flags
Determine the time window from the user's input:
--daily— Last 24 hours (default if no flag specified)--weekly— Last 7 days
The user may also specify a custom range:
--since yesterday--since Monday--since 2025-01-20
2. Check Available Sources
Identify which MCP sources are connected (same approach as the search command):
- ~~chat — channels, DMs, mentions
- ~~email — inbox, sent, threads
- ~~cloud storage — recently modified docs shared with user
- ~~project tracker — tasks assigned, completed, commented on
- ~~CRM — opportunity updates, account activity
- ~~knowledge base — recently updated wiki pages
If no sources are connected, guide the user:
To generate a digest, you'll need at least one source connected.
Check your MCP settings to add ~~chat, ~~email, ~~cloud storage, or other tools.
3. Gather Activity from Each Source
~~chat:
- Search for messages mentioning the user (
to:me) - Check channels the user is in for recent activity
- Look for threads the user participated in
- Identify new messages in key channels
~~email:
- Search recent inbox messages
- Identify threads with new replies
- Flag emails with action items or questions directed at the user
~~cloud storage:
- Find documents recently modified or shared with the user
- Note new comments on docs the user owns or collaborates on
~~project tracker:
- Tasks assigned to the user (new or updated)
- Tasks completed by others that the user follows
- Comments on tasks the user is involved with
~~CRM:
- Opportunity stage changes
- New activities logged on accounts the user owns
- Updated contacts or accounts
~~knowledge base:
- Recently updated documents in relevant collections
- New documents created in watched areas
4. Identify Key Items
From all gathered activity, extract and categorize:
Action Items:
- Direct requests made to the user ("Can you...", "Please...", "@user")
- Tasks assigned or due soon
- Questions awaiting the user's response
- Review requests
Decisions:
- Conclusions reached in threads or emails
- Approvals or rejections
- Policy or direction changes
Mentions:
- Times the user was mentioned or referenced
- Discussions about the user's projects or areas
Updates:
- Status changes on projects the user follows
- Document updates in the user's domain
- Completed items the user was waiting on
5. Group by Topic
Organize the digest by topic, project, or theme rather than by source. Merge related activity across sources:
## Project Aurora
- ~~chat: Design review thread concluded — team chose Option B (#design, Tuesday)
- ~~email: Sarah sent updated spec incorporating feedback (Wednesday)
- ~~cloud storage: "Aurora API Spec v3" updated by Sarah (Wednesday)
- ~~project tracker: 3 tasks moved to In Progress, 2 completed
## Budget Planning
- ~~email: Finance team requesting Q2 projections by Friday
- ~~chat: Todd shared template in #finance (Monday)
- ~~cloud storage: "Q2 Budget Template" shared with you (Monday)
6. Format the Digest
Structure the output clearly:
# [Daily/Weekly] Digest — [Date or Date Range]
Sources scanned: ~~chat, ~~email, ~~cloud storage, [others]
## Action Items (X items)
- [ ] [Action item 1] — from [person], [source] ([date])
- [ ] [Action item 2] — from [person], [source] ([date])
## Decisions Made
- [Decision 1] — [context] ([source], [date])
- [Decision 2] — [context] ([source], [date])
## [Topic/Project Group 1]
[Activity summary with source attribution]
## [Topic/Project Group 2]
[Activity summary with source attribution]
## Mentions
- [Mention context] — [source] ([date])
## Documents Updated
- [Doc name] — [who modified, what changed] ([date])
7. Handle Unavailable Sources
If any source fails or is unreachable:
Note: Could not reach [source name] for this digest.
The following sources were included: [list of successful sources].
Do not let one failed source prevent the digest from being generated. Produce the best digest possible from available sources.
8. Summary Stats
End with a quick summary:
---
[X] action items · [Y] decisions · [Z] mentions · [W] doc updates
Across [N] sources · Covering [time range]
Notes
- Default to
--dailyif no flag is specified - Group by topic/project, not by source — users care about what happened, not where it happened
- Action items should always be listed first — they are the most actionable part of a digest
- Deduplicate cross-source activity (same decision in ~~chat and email = one entry)
- For weekly digests, prioritize significance over completeness — highlight what matters, skip noise
- If the user has a memory system (CLAUDE.md), use it to decode people names and project references
- Include enough context in each item that the user can decide whether to dig deeper without clicking through
How to use digest on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add digest
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches digest from GitHub repository anthropics/knowledge-work-plugins and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate digest. Access the skill through slash commands (e.g., /digest) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★60 reviews- ★★★★★Michael Ndlovu· Dec 28, 2024
digest is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Harper Kapoor· Dec 24, 2024
Registry listing for digest matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Michael Haddad· Dec 20, 2024
I recommend digest for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Mei Okafor· Dec 20, 2024
Useful defaults in digest — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Kwame Park· Dec 20, 2024
We added digest from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ganesh Mohane· Dec 12, 2024
Keeps context tight: digest is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Harper Shah· Dec 4, 2024
digest fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Evelyn Jackson· Nov 19, 2024
Solid pick for teams standardizing on skills: digest is focused, and the summary matches what you get after install.
- ★★★★★Harper Sharma· Nov 15, 2024
Useful defaults in digest — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★William Verma· Nov 11, 2024
digest reduced setup friction for our internal harness; good balance of opinion and flexibility.
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