Generates an evidence-calibrated product or marketing persona using the canonical v2.5 output contract. Use when shaping artifact perspective, stress-testing decisions, or framing product and GTM strategy.
npx skills add https://github.com/product-on-purpose/pm-skills --skill foundation-persona
<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->
This skill produces decision-usable personas from one canonical template pack.
productmarketingbuyer as input alias for marketing (output remains labeled Marketing)Generated agent mode is out of scope for v2.5.0.
If the user asks for agent, ask them to choose product or marketing.
define-jtbd-canvas; the canvas captures what customers hire products to do, the persona captures who they arediscover-stakeholder-summarydiscover-interview-synthesis; a persona built on unsynthesized notes inherits their noiseWhen asked to generate a persona, follow these steps:
Determine whether the request is product or marketing (buyer alias allowed).
If mode is omitted, ask for mode selection.
If execution must continue without reply, default to product and state that fallback explicitly.
Use user-provided context first (goals, audience, domain, constraints, sources).
If evidence is thin, continue generation but mark gaps and calibrate confidence.
Use references/TEMPLATE.md and choose exactly one of:
Product Persona TemplateMarketing Persona TemplateFill the selected template end-to-end:
Persona Card1 through 11Evidence & ConfidenceHigh|Medium|Low confidence with rationale.Remove template guidance blockquotes (> notes) from the final output.
Ensure narrative entries are concrete and decision-changing, not placeholder bullets.
Product or Marketing) per output.Before finalizing, verify:
buyer inputs are normalized to MarketingPersona Card are present1 through 11 sections from the selected template are present and completeHigh, Medium, or Low with rationaleValidated, Assumed, Open questions, and Governance blocks are present> guidance lines) are removed from the completed outputSee references/EXAMPLE.md for a completed sample output.
Plan, write, and diagnose Instagram Reels that earn cold-audience reach. Use whenever someone wants a reels script or reels hook for a specific Reel, is debugging why a Reel flopped, wants to know if a draft is worth testing with Trial Reels before going public, or needs a reels caption tuned for the post-hashtag instagram algorithm. Built around what Mosseri has publicly named as the signal hierarchy (watch time, sends per reach, likes per reach), the Trial Reels test-then-publish loop, the Original Content Guidelines and 30-day recovery window, the Edits app, and Reels Insights metrics (skip rate, share rate, followers from this post). Covers a Reels-specific reels strategy: send-driving CTAs, originality without watermarks, audio licensing by account type, captions as the primary SEO signal, and the anti-patterns that quietly cap distribution. Pattern-based guidance, not a virality promise.
Perform relative value analysis on bonds by combining pricing, yield curve context, credit spreads, and scenario stress testing. Use when analyzing bond richness/cheapness, computing spread decomposition, comparing bonds, assessing bond value vs curves, or running rate shock scenarios.
Build quick IRR/MOIC sensitivity tables for PE deal evaluation. Models returns across entry multiple, leverage, exit multiple, growth, and hold period scenarios. Use when sizing up a deal, stress-testing assumptions, or preparing IC returns exhibits. Triggers on "returns analysis", "IRR sensitivity", "MOIC table", "what's the return at", "model the returns", or "back of the envelope".
Design lean startup experiments (pretotypes) for a new product. Creates XYZ hypotheses and suggests low-effort validation methods like landing pages, explainer videos, and pre-orders. Use when validating a new product idea, creating pretotypes, or testing market demand.
Amazon Alexa for Shopping Q&A automation: submits questions to Amazon's Alexa/Rufus AI shopping assistant and collects response text; supports optional keyword search context (navigate to search results page before asking for category-specific answers). Use when user mentions Amazon Alexa, Rufus, Amazon shopping assistant, Amazon AI chat, ask Amazon, Amazon Q&A, automate Alexa questions, Rufus chatbot, Amazon assistant automation, collect Alexa responses, bulk question submission to Amazon, keyword search context, category research. Also applies to extracting Amazon product recommendations from conversational AI, automating repeated queries to Amazon's AI shopping feature, collecting Alexa shopping responses at scale, or market research within a specific product category.
When the user wants to create UGC ad campaigns, recruit UGC creators, generate AI UGC content, or scale with user-generated content. Also use when the user mentions 'UGC,' 'user-generated content,' 'creator ads,' 'Spark Ads,' 'whitelisting,' 'AI UGC,' 'Arcads,' 'Creatify,' 'creator brief,' or 'UGC testing.' This skill covers the UGC growth framework from creator recruitment through AI-powered scaling. Do NOT use for technical implementation, code review, or software architecture.
Parse, modify, validate, and patch simulator input files. Use when working with reservoir simulation input files, testing scenarios, or validating simulation configurations. This implementation supports reference format (.DATA); other simulators use different extensions (e.g., .afi, .DAT). Supports natural language modifications, keyword patching, and syntax validation.
Triage ASM/recon output for ownership before testing — separate the target's real assets from namespace-collision noise. Automated recon keyword-matches on the brand name, so for any target whose name is a common/dictionary word, the output is dominated by assets belonging to UNRELATED same-named companies (repos, cloud buckets, mobile apps, breach corpora, typosquats). Built from an authorized engagement where an ASM report's "Criticals" were overwhelmingly false positives and the combo/repos/mobile/bucket lists were polluted with unrelated same-named orgs. Use at the START of any engagement, immediately on receiving any ASM/recon/OSINT dataset, BEFORE testing anything.
Take product-on-purpose/foundation-persona from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.