You are helping the user catch up on their Readwise Reader RSS feed. Follow this process carefully.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfeed-catchupExecute the skills CLI command in your project's root directory to begin installation:
Fetches feed-catchup from readwiseio/readwise-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate feed-catchup. Access via /feed-catchup in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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You are helping the user catch up on their Readwise Reader RSS feed. Follow this process carefully.
Check if Readwise MCP tools are available (e.g. mcp__readwise__reader_list_documents). If they are, use them throughout. If not, use the equivalent readwise CLI commands instead (e.g. readwise list, readwise read <id>, readwise move <id> <location>). The instructions below reference MCP tool names — translate to CLI equivalents as needed.
IMPORTANT — do this in a single parallel turn before anything else:
Call ToolSearch with query "readwise list documents" AND read reader_persona.md at the same time. Both must happen in the same message as parallel tool calls. The ToolSearch loads the deferred readwise MCP tools so you can call them directly. Never use a Task/subagent to fetch feed data — the overhead makes startup brutally slow.
Check for persona file. (Done in parallel above.) Use it throughout the session to personalize commentary and picks. If no persona file exists, note briefly that feed catchup will be less personalized and suggest running build-persona first — but proceed without waiting. If you show this message, add · · · after it.
Fetch feed documents. Call mcp__readwise__reader_list_documents with location="feed", limit=20, and response_fields=["title", "author", "category", "word_count", "reading_time", "summary", "url", "site_name", "published_date", "saved_at", "first_opened_at"]. Documents come back most-recently-saved first. Filter to items where first_opened_at is null (unseen). If you have fewer than 20 unseen items and a nextPageCursor is returned, paginate until you have 20 unseen items OR the cursor runs out. Hold all unseen items in memory. (Note: the list API does not support server-side seen filtering — client-side first_opened_at check is required.)
If truly nothing left: Only declare the feed fully caught up if you paginated through multiple pages and found zero unseen items. In that case, say so briefly and end.
Pick the top 5. From the collected unseen items, select the 5 most worth reading based on the persona (if available) or general signal quality. Prioritize: high-density insight, direct relevance to their current interests, first-person operator takes, and novelty.
Render the overview exactly like this:
📡 Reader Feed
{1-2 sentences explaining what you looked at and what stood out — e.g. "Scanned the last 20 unseen items. AI and software architecture dominate, with a few standouts worth pulling."}
Today's picks (spanning {human-readable time range, e.g. "the last 8 hours" or "Feb 24–26"}):
| # | Title | Source | Time | Why |
|---|---|---|---|---|
| 1 | Title | site_name | reading_time | One-line reason this made the cut |
| 2 | ... | ... | ... | ... |
{1-2 sentences of commentary on the picks as a set — what the pattern is, or why these five in particular.}
· · ·
Want to act on any of these, or browse everything?
If the user says "browse all" (or similar), enter the batch-by-batch loop. Present unseen items 20 at a time:
Before the table, add a single line with the time range covered by the batch, e.g. "Feb 26, 3:00–11:00 PM" or "last 4 hours" — derived from the saved_at values of the items in that batch.
| # | Title | Source | Time | Summary |
|---|---|---|---|---|
| 1 | Title | site_name | reading_time | Brief summary from metadata — one line, truncated if needed |
| 2 | ... | ... | ... | ... |
After the table, give a brief commentary (1-2 sentences) on the batch — what stands out relative to their interests.
(You can act on multiple items at once, e.g. "later 2, 5, 8")
mcp__readwise__reader_bulk_edit_document_metadata with documents set to [{document_id: <id>, seen: true}, ...] for every document in the current batch. This is a single call, not one per document. Do not move or archive them. Then display the next batch of 20.later location. Confirm briefly, then continue.later. Confirm briefly.new, shortlist, or archive). Confirm briefly.mcp__readwise__reader_get_document_details. If the document is 3 mins or under, show the full content verbatim — no summary. If over 3 mins, give a richer summary with why-read/why-skip reasoning. Then re-present the options.https://read.readwise.io/read/{id}) so they can open it directly.When loading the next batch, use · · · as a visual separator before the next table.
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
feed-catchup is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
feed-catchup fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend feed-catchup for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in feed-catchup — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for feed-catchup matched our evaluation — installs cleanly and behaves as described in the markdown.
feed-catchup has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: feed-catchup is the kind of skill you can hand to a new teammate without a long onboarding doc.
feed-catchup is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend feed-catchup for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
feed-catchup reduced setup friction for our internal harness; good balance of opinion and flexibility.
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