skills/strategist-voice/SKILL.md
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.
npx skillsauth add lyndonkl/claude strategist-voiceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Apply this skill in the final consolidation step of a strategist or analyst report, when freeform working notes are being shaped into a publishable long-form document. The output should read like a research note from a buy-side desk or a strategy consultancy, not like a default assistant response.
The house style synthesizes two analyst-blogger voices:
Both write at a sentence level that no LLM default produces. The rules below capture what they share. Where their habits do not serve a strategist report (notably their heavy use of em dashes), this skill deliberately diverges.
The consolidating agent must enforce these. They are non-negotiable. Run the checklist at the end of this file before treating the report as done.
Strategist inference: prefixes. Signal synthesis through phrasing (see "Opinion signaling" below).claim text.[^7] in the body with [^7]: Source — URL in a bibliography section. Never inline [Source: Org — URL] or (Source: Org, URL).Replace Strategist inference: with specific phrasing. The strength scale runs from tentative to high-conviction:
Use first-person ("we," "this analyst's reading," "in our reading") sparingly. Once or twice per long section is correct; once per paragraph is wrong. Reserve it for the genuine judgment calls.
Use one of these three patterns. None of them define the company.
Banned openings: "Company X is a Y that does Z," definitions of the product category, restatements of the directive.
Numbered footnotes. In the body:
Figma's Q4 2025 revenue grew 40% year on year.[^7]
In the bibliography section at the end of the report, grouped by source type:
**Primary company material**
[^1]: Figma — How Figma's multiplayer technology works — https://www.figma.com/blog/...
[^7]: Figma Investor Relations — Q4 2025 results — https://investor.figma.com/...
**Founder and executive voices**
[^12]: Lenny's Podcast with Dylan Field, October 2025 — https://...
**Engineering and architecture material**
[^18]: ...
**Secondary analysis**
[^24]: ...
Footnote markers go after the sentence punctuation. Never two markers on the same fact. Never a parenthetical citation inside a sentence.
See style-examples.md for annotated good-and-bad rewrites of opening paragraphs, opinion-signaled sentences, negation-cascade fixes, citation forms, and synthesis statements.
Copy this into the working response when consolidating the final report. Tick each item before declaring the report finished.
Strategist-voice final pass:
- [ ] Zero em dashes in the final document
- [ ] Zero "not X, not Y, not Z" negation cascades
- [ ] Zero `Strategist inference:` prefixes (or any variation thereof)
- [ ] Zero inline `[Source: ...]` or `(Source: ...)` citations
- [ ] Zero "It's worth noting" hollow openings
- [ ] Zero "In conclusion / Ultimately / To summarize" closers
- [ ] The opening paragraph uses one of the three permitted patterns
- [ ] At least three opinion-signaling phrases (from the list above) appear in the synthesis section
- [ ] Sentence length varies visibly within paragraphs
- [ ] First-person ("we," "this analyst's reading") used at most twice per major section
- [ ] Bibliography is grouped by source type and uses numbered footnote anchors that match in-text markers
- [ ] Every strategy-jargon term ("wedge", "flywheel", "TAM", "NDR", "moat", "attach rate", "X-shaped" labels, etc.) is either defined on first use or rewritten away
- [ ] No abstract X-shaped / Y-shaped label is doing the analytical work that a concrete description should be doing
If any box cannot be ticked, return to the draft and fix before producing the final markdown.
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.