build-persona

Readwise Access

mcp__readwise__reader_list_documents

readwise

readwise list

readwise read <id>

readwise search <query>

readwise highlights <query>

Welcome

Build Persona · Readwise Reader

I'll analyze your reading history — saves, highlights, and tags — and build a

reader_persona.md

profile in the current directory. Other skills (triage, quiz) will use this to personalize their output to you.

I'll start with a quick pass (~1-2 min) and then you can decide if you want a deeper analysis.

Process

Phase 1: Quick Pass

1. Gather data. Run ALL of these in parallel (one batch of tool calls):
   • 4 highlight searches:

     mcp__readwise__readwise_search_highlights

     with 4 broad queries (e.g. "ideas strategy product", "learning technology culture", "writing craft creativity", "business leadership growth") with

     limit=50

     each. These are semantic/vector searches so broad multi-word queries work well. Highlights are cheap and high-signal — cast a wide net.
   • 4 document lists:

     mcp__readwise__reader_list_documents

     from each non-feed location:

     location="new"

     ,

     location="later"

     ,

     location="shortlist"

     , and

     location="archive"

     with

     limit=100

     each. If the combined results are very sparse (< 20 docs total), also try without a location filter or with

     location="feed"

     as a fallback. Only fetch metadata:

     response_fields=["title", "author", "category", "tags", "site_name", "summary", "saved_at", "published_date"]

     . Do NOT fetch full content.
   • Tags:

     mcp__readwise__reader_list_tags

     to understand their organizational system.
2. Parse results efficiently. The JSON responses from document lists can be large (25k+ tokens). Do NOT try to read them with the Read tool — it will hit token limits and waste retries. Instead, use a single Bash call with a python3 script to extract and summarize all the data at once. The script should parse all result files together and output:
   • Document counts by category
   • Top 20 sites, authors, and tags
   • Save velocity by month
   • All docs saved in the last 3 weeks (title, category, author, date)
   • A representative sample of highlight texts with their source titles/authors
3. Write the persona. Write

   reader_persona.md

   to the current working directory with these sections:
   • Identity & Role — Who they appear to be (profession, role, industry)
   • Core Interests — Top themes and topics, ranked by frequency and recency
   • Reading Personality — How they read (saves a lot but reads selectively? highlights heavily? prefers short or long-form?)
   • Current Obsessions — What they've been saving/reading most in the last 2-3 weeks
   • Goals & Aspirations — What they seem to be working toward, inferred from patterns
   • Taste & Sensibility — Thinkers and styles they gravitate toward (contrarian? practical? philosophical? technical?)
   • Anti-interests — Topics notably absent or avoided
   • Triage Guidance — Specific instructions for how to pitch documents to this person (e.g. "lead with practical applicability", "connect to their interest in X", "bar is high for AI content — flag when it's genuinely novel")
4. Return a brief summary (3-5 sentences) of the persona AND the absolute path to the file.

• Do NOT call

  readwise_list_highlights

  — it often errors and is redundant with search.
• Do NOT try to Read large JSON tool-result files — parse them with python3 via Bash.
• Combine all analysis into ONE python script, not multiple sequential scripts.
• Maximize parallel tool calls. Every API fetch in step 1 should be a single parallel batch.

Phase 2: Deep Pass (optional)

• Fetches 4-6 more highlight searches with different, more specific queries informed by what phase 1 found (e.g. if the persona shows interest in AI tooling, search "AI agents workflows automation"; if they read fiction, search "fiction narrative storytelling") with

  limit=50

  each
• Paginates beyond the first 100 docs per location using

  next_page_cursor

  from phase 1 results — fetch the next 100-200 per location to build a much larger sample
• Reads the existing

  reader_persona.md

  and enriches/rewrites it with the additional data — more nuanced sections, stronger evidence, sharper triage guidance
• Returns a summary of what changed

After Each Subagent Returns

1. Show the file link. Always tell the user:

   reader_persona.md

   was written to

   {absolute_path}

   . Display the full path so they can open it.
2. Show a summary of the persona (use the subagent's returned summary).
3. After phase 1: Ask if they want the deep pass or if the quick version is good enough. Also ask if they want to adjust anything.
4. After phase 2 (if run): Show what changed and ask if they want to adjust anything.
5. If adjustments needed, edit the file directly based on their feedback.
6. Confirm saved. Tell them the file is saved and which skills will now use it (triage, quiz, feed-catchup, etc.).