Compete – A Claude Code plugin for interactive competitor intelligence

Forthright Tech released Compete, a Claude Code plugin that automatically generates competitive intelligence reports from any software repository. The tool analyzes code, manifests, and README files to identify a product, then builds a multi-dimensional database covering competitors' product, technology, business, marketing, SEO, social media, customers, sales, hiring, funding, and brand positioning. It outputs a self-contained interactive HTML report with dashboards, comparison matrices, and prioritized recommendations, aiming to streamline competitive analysis for developers.

A Claude Code Skill that turns your repository into a complete competitive intelligence report. compete analyzes the current repository to identify your product, then discovers competitors and builds a complete competitive intelligence database spanning product, technology, business, marketing, SEO, social media, customers, sales, hiring, funding, and brand positioning. It is an AI product research assistant — not just a comparison generator. Everything is collected into normalized, confidence-annotated JSON datasets and rendered into a self-contained interactive HTML report the InsightKit report with dashboards, comparison matrices, capability radars, pricing ladders, a positioning scatter, SWOT accordions, and prioritized recommendations. Repo-aware — reads your code, manifests, and README to identify the product before searching the web. No manual product brief required. Multi-dimensional intelligence — company profile, pricing, tech stack, social presence, marketing, and SEO for every competitor. Confidence everywhere — every field is wrapped with a confidence score, source, provenance, and an explicit unknown fallback. Nothing is asserted without showing its work. Verify before acting. Normalized data contract — all stages write JSON validated against the schemas in, joined by skills/compete/schemas/ entity ref . Visualizations consume the data, never scrape directly. Self-contained report — one report.html file ~570 KB opens standalone in any browser. The only external dependencies are the Chart.js and D3 CDN bundles. A walkthrough of the seven tabbed views in a generated InsightKit report this repository analyzed against its own competitive landscape : compete is packaged as a Claude Code plugin . The plugin bundles three things that work together: - the compete skill skills/compete/ — the workflow, scripts, schemas, and report template; - the /compete slash command commands/compete.md — a one-shot entry point that drives the whole pipeline; - the plugin manifest .claude-plugin/plugin.json . /plugin marketplace add forthrighttech/compete /plugin install compete The first command registers this repository as a plugin marketplace it ships a .claude-plugin/marketplace.json /lbj96347/compete/blob/main/.claude-plugin/marketplace.json ; the second installs the plugin. Restart or start a new session and confirm with /plugin — compete should be listed as enabled, the /compete command available, and the compete skill discoverable via /skills . git clone https://github.com/forthrighttech/compete.git \ ~/.claude/plugins/compete The plugin must live at a directory whose root contains .claude-plugin/plugin.json with skills/ and commands/ alongside it . Claude Code discovers it on the next session. If you only want the skill without the slash command, clone just the skill subtree into your skills folder: Personal all projects git clone https://github.com/forthrighttech/compete.git /tmp/compete && \ cp -r /tmp/compete/skills/compete ~/.claude/skills/compete Project-scoped one repository cp -r /tmp/compete/skills/compete \ /path/to/your-repo/.claude/skills/compete The skill must live at .../.claude/skills/compete/ with SKILL.md at its root. Confirm with /skills it should appear as compete . You trigger it in natural language rather than with /compete . Claude Code with web access WebSearch / WebFetch for competitor discovery and intelligence collection. Python 3.9+ for the helper scripts in. The collection and rendering scripts use only the standard library — no skills/compete/scripts/ pip install required. Open Claude Code in the repository you want analyzed and run: /compete With no argument, Stage 1 auto-detects the product from the current repo and the command runs the full pipeline end to end, writing the report to ./insightkit-output/ . You can also pass an optional seed — a competitor URL or name: /compete https://www.crayon.co /compete Klue A seed is folded into Discovery as a known competitor candidate and is used to anchor the product's market/category, instead of relying solely on auto-detection. The seed is slugified into an entity ref e.g. crayon , classified, and appears in the roster alongside the auto-discovered competitors. If you installed the skill without the command — or just prefer plain language — ask Claude directly. The skill triggers on phrases such as: "Find my competitors" "Run a competitive analysis on this repo" "Who are my competitors and how do I compare?" "Build me a competitive landscape / positioning matrix / SWOT" Claude runs the pipeline end to end. You can also drive any stage yourself: 1. Product Intelligence — analyze the repo, write product.json python skills/compete/scripts/analyze repo.py --repo . --validate 2. Competitor Discovery — plan searches, then normalize results python skills/compete/scripts/discover competitors.py plan --product product.json Claude runs the plan with WebSearch/WebFetch → candidates.json python skills/compete/scripts/discover competitors.py build --product product.json \ --candidates candidates.json --validate 3. Intelligence Collection — per-competitor company/pricing/tech/social/marketing/SEO python skills/compete/scripts/collect intelligence.py plan --competitors competitors.json Claude runs the plan with WebSearch/WebFetch → findings.json python skills/compete/scripts/collect intelligence.py build --competitors competitors.json \ --findings findings.json --validate 4 + 5. Knowledge Graph + Visualization — synthesize report.json and render report.html python skills/compete/scripts/build report.py --input-dir . --output-dir ./insightkit-output add --open to also launch the report in a browser Open the result: open ./insightkit-output/report.html A fully rendered example ships in insightkit-output/ /lbj96347/compete/blob/main/insightkit-output so you can see the report without running the pipeline: | File | What it is | |---|---| report.html | report.json screenshots/overview.png The sample analyzes this very repository compete against 17 discovered competitors , classifying each by type and competitive threat. The report has seven tabbed views: Overview — stat cards, competitor-classification and threat-distribution doughnuts, and the executive summary. Comparison — sortable matrix with confidence-bar cells per dimension. Radar — six transparent 0–100 capability axes, self vs. competitors, toggleable series. Pricing — entry-price bar chart plus a plan-ladder table. Positioning — D3 scatter of price × scale, bubble size = similarity, color = threat. SWOT — expandable strengths/weaknesses/opportunities/threats per competitor. Opportunities — market gaps and prioritized recommendations. Every judgment in the report carries a method note explaining the heuristic behind it. repo ──▶ analyze repo.py ──▶ product.json │ ▼ discover competitors.py ──▶ competitors.json │ ▼ collect intelligence.py ──▶ companies/pricing/techstack/ │ social/marketing/seo.json ▼ build report.py ──▶ report.json ──▶ report.html The normalized JSON datasets are the contract between stages. Each is validated against a schema in skills/compete/schemas/ /lbj96347/compete/blob/main/skills/compete/schemas ; the rules confidence-wrapped fields, unknown fallback, entity ref joins are documented in . /lbj96347/compete/blob/main/skills/compete/references/data-schema.md skills/compete/references/data-schema.md | Path | Purpose | |---|---| .claude-plugin/plugin.json | .claude-plugin/marketplace.json /plugin install . commands/compete.md /compete slash command optional seed argument . skills/compete/SKILL.md PRD.md skills/compete/references/ skills/compete/scripts/ skills/compete/templates/ report.html template. skills/compete/schemas/ insightkit-output/ compete v1 ships the full one-shot pipeline: product intelligence → discovery → multi-dimensional collection → knowledge graph → interactive report. The following are deliberately deferred to v2 . v1 collects a solid breadth-first profile across all dimensions. v2 goes deep: Deep SEO — keyword universe and rankings, backlink graph and authority, content gap analysis, SERP-feature ownership, and traffic-trend estimates beyond v1's meta/structure snapshot . Deep social — engagement-rate and follower-growth time series, share-of- voice, sentiment, and channel-mix breakdowns per competitor. Hiring intelligence — open-roles tracking, team-growth and org-shape signals, and the strategic bets implied by what each competitor is hiring for. Sales intelligence — go-to-market motion PLG vs. sales-led , funnel and packaging signals, win/loss themes, and target-segment inference. v1 produces a point-in-time report. v2 adds a monitoring mode that re-runs the pipeline on a schedule and surfaces diffs — turning InsightKit from a one-time report generator into a continuous competitive-intelligence platform. Daily or weekly it would track: - pricing changes - new features - new blog posts - hiring trends - GitHub releases - social-media activity - funding news - SEO changes See PRD.md /lbj96347/compete/blob/main/PRD.md for the complete v1 research scope and the v2 vision. Contributions are welcome — see CONTRIBUTING.md /lbj96347/compete/blob/main/CONTRIBUTING.md for the data-contract rules, schema-validation workflow, and how to regenerate the sample report. compete is an open-source project built by forthrighttech https://x.com/lbjhkg , developed with the help of a few products I build: — private AI that runs local LLMs on your machine. tokkong http://tokkong.forthrighttech.com — an AI keyboard with on-device, local transcription. whiskey https://whiskey.asktobuild.app — a macOS menu bar manager, with support for macOS 27. lounge https://lounge.asktobuild.app Find me on X: @lbjhkg https://x.com/lbjhkg . MIT /lbj96347/compete/blob/main/LICENSE © 2026 forthrighttech