skills/ingest-substack-csv/SKILL.md
--- name: ingest-substack-csv description: Loads and validates a weekly Substack CSV stats export for the substacker Growth Analyst. Reconciles header against expected-columns schema, parses post rows + subscriber aggregates, moves file into corpus/stats/ on success, emits schema-warning stub on header drift. Never reads subscriber emails row-by-row — aggregates only. FALLBACK path when fetch-substack-stats (Chrome automation) is unavailable. Use when a CSV appears in inbox/substack-stats/, when
npx skillsauth add lyndonkl/claude skills/ingest-substack-csvInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Per weekly CSV:
- [ ] Step 1: Read header row; diff against .schema-expected.json
- [ ] Step 2: If load-bearing column missing, halt + emit schema-warning stub
- [ ] Step 3: Parse post rows (load-bearing: views, opens, open_rate, clicks, sent, post_date)
- [ ] Step 4: Parse subscribers aggregate (total, free/paid distribution, activity tiers) — NEVER row-by-row emails
- [ ] Step 5: Emit typed WeekExport object
- [ ] Step 6: On success, move CSV into corpus/stats/YYYY-WW.csv
Posts CSV columns (load-bearing in bold): id, slug, title, post_date, views, opens, open_rate, clicks, click_through_rate, sent, delivered, signups_within_1_day, subscriptions_within_1_day, unsubscribes_within_1_day, signups, subscribes, shares, estimated_value, engagement_rate, reaction_count, comment_count
Subscribers CSV (handled separately, never ingested row-by-row): email, created_at, subscription_type, activity_tier, email_opens_last_30_days, email_opens_last_7_days.
If a load-bearing column is missing:
ops/growth-analyst/YYYY-WW-schema-warning.md with the header diff..schema-expected.json if confirmed."New columns (additions) are accepted silently. Removals of non-load-bearing columns produce a soft note but don't halt.
{
"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": {active, at-risk, churned}
}
inbox/ after 7 days (privacy posture).testing
--- name: advisory-edit description: A strict advisory-only editing discipline for a writer who dictates ("speaks out") essays and wants help WITHOUT having their voice changed. The editor directs structure, flags grammar, and suggests strategic language — but never modifies the writer's text unless the writer explicitly says "apply" / "make that change" / "rewrite this." Produces a line-referenced, suggestion-only critique where every item is marked the writer's call. Four passes: structural, l
testing
Provides the house style for analyst-grade strategist writing — third-person register with sparing first-person, no em dashes, no "not X, not Y, not Z" negation cascades, numbered footnote citations rather than inline source parentheticals, specific opinion-signaling phrases, and topic-forward paragraph structure modeled on voice patterns observed in Damodaran's Musings on Markets and Thompson's Stratechery. Use when consolidating working notes into a finished long-form strategist or analyst report that must read as written by a senior human analyst rather than an AI assistant.
testing
Renders a markdown report to a PDF using pandoc with xelatex (11pt serif body, 1-inch margins, numbered footnotes, formal heading hierarchy). Requires a one-time install of pandoc and a LaTeX engine on the user's machine — basictex on macOS or texlive-xetex on Linux. Does not attempt automatic install. Fails loudly with the exact install commands if pandoc or xelatex is missing on the user's PATH. Use when producing a finished strategist or analyst report PDF from a polished markdown source.
testing
Produces step-by-step computational walkthroughs of vector and matrix operations as a sequence of numbered "frames", showing the explicit state at each step. The text-equivalent of a 3Blue1Brown animation — each frame shows what changed and why, so the learner can re-trace the operation by hand. Use when the learner needs to *see* a computation unfold (eigenvalue computation, attention with 3 tokens, gradient descent step, SVD on a 2×2, layer norm on a 3-vector, softmax of a small input), when an explanation has been given but the learner needs to ground it in a worked example, or when introducing an operation that's intimidating in symbol form but trivial in pencil-and-paper form.