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1 pack

Pre-planning knowledge-work skills for the Han suite: understanding a problem before anyone commits to a plan. Home of research, gap-analysis, and issue-triage, plus the research-analyst agent. Depends on han-communication and han-core; bundled by the han meta-plugin.

3 skills · pack

Results for “gap-analysis”

16 skills

Researches competitors, compares features, pricing, and tech stacks, then produces a prioritized gap analysis with actionable roadmap.

Turns raw client requirements into an IEEE 830 software requirements specification through structured clarification and gap analysis.

competitive-analysis

Conduct a structured competitive analysis comparing UX patterns, features, strengths, and gaps across rival products.

Performs a gap analysis between two artifacts (a current state and a desired state) and produces a plain-language, stakeholder-readable report indexed by stable gap IDs. Use when the user wants to compare, evaluate, audit, or reconcile one artifact against another. Does not investigate runtime bugs — use investigate. Does not assess module-level architecture — use architectural-analysis. Does not research open-ended options with no second artifact to compare against — use research.

competitive-intel

Systematically track competitors, analyze market moves, and produce battlecards, win/loss analysis, and positioning maps for sales, marketing, and product teams.

20.4k · bundle

Runs a full research-to-ideation pipeline: competitive gap analysis, technology trend research, user feedback analysis, and feature ideation, producing structured reports.

More results

Generates an IEEE 830 Software Requirements Specification from raw client input through structured brainstorming, gap analysis, and clarification rounds.

gen-specs-as-issues

Identify missing features in a project, prioritize them, and create detailed GitHub issues with specifications.

Reviews development plans for gaps, hidden assumptions, critical misses, and XP violations, then presents prioritized findings for confirmation.

ab-test-analysis

Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations.

keyword-research-pipeline

Master orchestrator for the keyword research pipeline. Chains topic-discovery → seed/modifier ideation → competitor + AI-search gap analysis → question mining → BID method → AIO cannibalization check → adversarial redteam → final ranked queue. Same anti-context-bloat pattern as /blog-pipeline (every layer is an Agent dispatch, never a Skill fork).

Discover, profile, and evaluate MCP servers for a given domain or purpose. Use when searching for MCP servers to add to a project, comparing server capabilities, enriching the local registry cache, or evaluating whether a server suite covers a stated need. Covers cache-first discovery, remote registry scanning, deep server profiling, and gap analysis.

Turns a raw idea, brain dump, or conversation into a complete PRD with mandatory acceptance criteria and measurable success metrics. Extracts the problem, target users, and jobs-to-be-done, runs a quick competitive scan (web if available, logged as a gap if not), drafts the full PRD (problem, personas, user stories.

seo-keyword-research

Use this skill when a user asks for SEO keyword research, keyword discovery, search volume analysis, keyword difficulty, search intent mapping, topic clusters, content opportunities, competitor keyword gaps, or a keyword strategy for a domain, URL, product, market, or seed topic. When a website is provided, crawl and interpret the site first, then use AIsa API access to DataForSEO keyword, SERP, trend, Labs, and OnPage endpoints plus AIsa LLM reasoning to find non-brand keyword opportunities. Use when: the user needs web search, research, source discovery, or content extraction.

Expert India Digital Personal Data Protection Act, 2023 (DPDPA) compliance advisor. Use this skill whenever a user asks about the DPDPA, DPDP Act, DPDP Rules 2025, India data privacy law, Data Fiduciary obligations, Data Principal rights, Significant Data Fiduciary, Data Protection Board of India, consent under DPDPA, notice requirements, breach notification India, children's data India, cross-border data transfer India, India privacy compliance, DPDPA gap analysis, DPDPA vs GDPR, or any obligation under India's personal data protection framework. Also trigger for: "Section 6 consent", "Section 7 legitimate uses", "Section 9 children's data", "Section 10 SDF", "Section 16 cross-border", "Rule 6 breach notification", "Rule 13 SDF obligations", "Data Protection Board complaint", "verifiable parental consent India", "DPDPA compliance roadmap", or "India privacy law global company".

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