skills/fetch-substack-stats/SKILL.md
--- name: fetch-substack-stats description: Pulls substacker's weekly Substack stats directly from the dashboard via Claude-in-Chrome browser automation. Navigates to substack.com/stats, parses the posts table and subscribers table, and produces the same typed WeekExport object that ingest-substack-csv produces — but without requiring a manual CSV export. The writer keeps Chrome signed in to Substack; this skill opens the dashboard in a new tab, reads the rendered stats, closes the tab. Primary
npx skillsauth add lyndonkl/claude skills/fetch-substack-statsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Related skills: Primary alternative to ingest-substack-csv. Produces the same WeekExport contract so downstream skills (compute-baseline, attribute-performance, per-section-tracking) don't care which path produced the data.
tabs_context_mcp, tabs_create_mcp, navigate, get_page_text, read_page, optionally javascript_tool.https://thethinkersnotebook.substack.com/publish/stats (the dashboard URL shape — verify on first run).Per weekly run (Mondays) or on-demand:
- [ ] Step 1: tabs_context_mcp — inspect existing tabs; if a Substack stats tab is already open, reuse; else tabs_create_mcp
- [ ] Step 2: navigate to https://substack.com/publish/stats (or publication-specific dashboard URL)
- [ ] Step 3: get_page_text on the rendered dashboard; parse:
- Total subscribers (headline number)
- Weekly delta
- Posts table with columns: title, date, opens, open rate, clicks, CTR, views, sent
- Activity-tier distribution (free / paid, active / at-risk / churned)
- [ ] Step 4: For each post in the last 7 days, also navigate to the individual post stats page for:
- Referral sources breakdown
- Post-specific engagement
- [ ] Step 5: Normalize into WeekExport object (schema matches ingest-substack-csv's output)
- [ ] Step 6: Archive the scraped stats as CSV in corpus/stats/YYYY-WW.csv (so historical baseline works identically)
- [ ] Step 7: Close the Substack tab (do NOT leave stats pages open in the user's browser)
Substack exposes referral source breakdowns only on individual post stats pages. The scraper navigates to each outlier post (|z| ≥ 1.0 candidate, determined after baseline compute — so this step may be deferred to attribute-performance) to pull referral data. For non-outliers, per-post referral is skipped.
Same schema as ingest-substack-csv:
{
"subscribers_end": int,
"delta_subscribers": int,
"posts": [
{"slug", "title", "post_date", "views", "opens", "open_rate", "clicks", "sent", ...}
],
"sends_this_week": int,
"free_subs": int,
"paid_subs": int,
"activity_tier_distribution": {...},
"source": "chrome-scrape", # vs. "csv-export" from the other skill
"scraped_at": ISO8601
}
Written to corpus/stats/YYYY-WW.csv (same archive path as CSV imports). The source field marks provenance so the writer can tell at a glance whether a week came from live scrape or manual export.
Trigger: Monday morning, Growth Analyst invokes fetch-substack-stats.
tabs_context_mcp — no existing Substack tab.tabs_create_mcp + navigate → Substack dashboard stats page.get_page_text — reads:
navigate → post-specific stats → referral breakdown shows 60% direct, 20% Notes, 20% search.WeekExport{subscribers_end: 148, delta_subscribers: 6, posts: [...], ...}.corpus/stats/2026-W17.csv.Downstream pipeline (compute-baseline, attribute-performance, etc.) runs identically whether data came from CSV or scrape — the WeekExport contract is stable.
corpus/stats/{YYYY-WW}.csv already exists for today's ISO week, compare — don't overwrite unless the scrape is strictly more recent and differs meaningfully.fetch-substack-stats FAILED: {reason}; falling back to ingest-substack-csv and halt — let the writer decide whether to retry or drop a manual CSV.login-required message; writer handles auth manually.corpus/stats/YYYY-WW.csv on success.ingest-substack-csv if browser path fails.testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.