Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add shawnpang/startup-founder-skills --skill competitor-monitoringgit clone --depth 1 https://github.com/shawnpang/startup-founder-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shawnpang/startup-founder-skills/competitor-monitoring)<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/competitor-monitoring"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/competitor-monitoring/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/competitor-monitoring"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/competitor-monitoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00059 | $0.01308 |
| Opus 5 | $0.00030 | $0.00654 |
| Sonnet 5 | $0.00012 | $0.00262 |
| Haiku 4.5 | $0.00006 | $0.00131 |
Grade A, and why
competitor-monitoring scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- competitor-monitoring — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Monitoring
When to Use
- Founder wants to know when competitors change pricing, ship features, or raise funding
- Founder wants a recurring "what changed this week" scan of competitor activity
- Founder wants to detect strategic shifts from competitor job postings, blog posts, or product updates
- Founder wants to stay informed without manually checking 10 websites daily
This is the recurring sibling of competitive-analysis (one-time deep dive). Use this skill for ongoing monitoring, not initial research.
Context Required
- List of 3-7 competitors to track (names, websites, product URLs)
- What the founder cares about most (pricing, features, positioning, hiring, funding, content)
- Monitoring frequency (weekly recommended for early-stage, biweekly for established markets)
- The founder's own positioning (to flag threats and opportunities)
Workflow
- Define the monitoring surface — for each competitor, identify what to watch:
- Pricing page — plan changes, new tiers, free plan adjustments
- Changelog / release notes — new features, deprecations, platform shifts
- Job postings — engineering roles signal product direction, sales roles signal GTM shifts, exec hires signal strategy changes
- Blog / content — new positioning, case studies (reveal target customers), thought leadership pivots
- Social media — founder posts, company announcements, community reactions
- Review sites — new reviews on G2, Capterra, Trustpilot (sentiment shifts)
- Funding / press — Crunchbase alerts, press releases, media coverage
- Set up the monitoring stack — recommend tools and manual checks:
- Automated: Google Alerts (brand mentions), Visualping or ChangeTower (page change detection), Crunchbase alerts (funding), LinkedIn job alerts
- Manual weekly scan: pricing pages, changelogs, recent blog posts, latest job postings
- Quarterly deep dive: full
competitive-analysisrefresh
- Run the scan — check all sources for the monitoring period and flag changes.
- Analyze signals — for each change detected:
- What changed (factual description)
- What it signals (interpretation — are they moving upmarket? entering your segment? struggling with churn?)
- Threat level (none / watch / respond / urgent)
- Recommended action (if any)
- Generate the report — produce a concise weekly/biweekly competitor intel brief.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 119 lines · 59 tokens per session scan A 842186eff2d8
competitor-monitoring is a skill published in the GitHub repository shawnpang/startup-founder-skills (321 stars, last pushed 6mo ago), licensed MIT. It adds 59 tokens to every session and 1,308 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…