Plugins

1 plugin

Logo Design and OG Image

Install this pack to design a logo and create matching Open Graph images for social sharing.

4 skills · plugin

Results for “matching”

9 skills

Find compatible cofounders, collaborators, and friends through validated psychological profiling and mathematical compatibility matching.

Creates and manages an AI agent's dating profile on inbed.ai, including registration, discovery, matching, and messaging via the platform's API.

Routes product-discovery work into the right stage — opportunity brief, research, PRD, tickets, or outcome review — and hands off to the matching sub-skill.

More results

Template Discovery

Finds, inspects, and compares .NET project templates by resolving natural-language descriptions to ranked template matches with pre-filled parameters.

Company Research

Discovers and researches companies matching an ideal customer profile, using Browserbase Search API and a Plan→Research→Synthesize pattern to produce scored reports and CSV exports.

Continuous Discovery

Build a weekly cadence of customer touchpoints using Opportunity Solution Trees, assumption mapping, and interview snapshots to keep product decisions grounded in evidence.

Sales Sales Discovery Coach

Coaches sales teams on elite discovery methodology — question design, current-state mapping, gap quantification, and call structure that surfaces real buying motivation.

Paw Pa Research

Proposal research workflow that matches local case studies and gathers web evidence into an HTML research dossier. Use when the user needs proposal research, client intel, tech stack discovery, pricing benchmarks, competitive context, or case-study matching for a brief. Triggers: 'research this proposal', 'build a research dossier', 'match case studies', 'find pricing benchmarks', 'client intel for', 'what tech does X use'.

brycewang-stanford

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting mu