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 stanislavnianko/product-discovery-claude-skills --skill competitive-scangit clone --depth 1 https://github.com/stanislavnianko/product-discovery-claude-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/stanislavnianko/product-discovery-claude-skills/competitive-scan)<a href="https://agentmods.dev/skills/stanislavnianko/product-discovery-claude-skills/competitive-scan"><img src="https://agentmods.dev/badge/skills/stanislavnianko/product-discovery-claude-skills/competitive-scan/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/stanislavnianko/product-discovery-claude-skills/competitive-scan"><img src="https://agentmods.dev/badge/skills/stanislavnianko/product-discovery-claude-skills/competitive-scan.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 | $0.00068 | $0.01079 |
| Opus 5 | $0.00034 | $0.00540 |
| Sonnet 5 | $0.00014 | $0.00216 |
| Haiku 4.5 | $0.00007 | $0.00108 |
Grade A, and why
competitive-scan 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitive Scan
Part of the discovery-phase skill pack ·
evidencegroup · readsdiscovery-context.md(runprofile-builderfirst if missing).
Answers two questions: what's already out there, and why haven't users adopted it for this problem? The second is more important.
Step 1 — Read inputs
Read discovery-context.md (sections 1. Client → Domain, 2. Product / Initiative) and problem-canvas.md (anchors the scan against the framed problem).
If discovery-context.md is missing, ask the BA inline: "client domain + product type in one line" — tag the output [ASSUMED DOMAIN]. If problem-canvas.md is missing, ask: "what problem in one line; what success signal would matter?" — tag the output [NO-PROBLEM-FRAME] so reviewers know the gap analysis is weak. Never block; recommend profile-builder / problem-framing for high-stakes work.
Step 2 — List competitive set in 3 layers
- Direct competitors — products explicitly solving the same problem in the same domain
- Indirect substitutes — adjacent products or workflows users already use (often surfaced by interviews / SME workshops)
- Adjacent (non-software) workarounds — Excel, manual ops, contractors, doing nothing
Aim for 4-8 entries across all 3 layers. More than 12 = over-research; fewer than 3 = the BA hasn't looked hard enough.
Step 3 — Delegate web research if available
If deep-research, market-research, or exa-search skills are installed:
"Use
deep-researchto produce a cited scan of tools that do<problem from canvas>for<user segment>in<domain>. Include pricing, last meaningful update, and notable user complaints."
Otherwise fall back to: G2/Capterra/Product Hunt for category, GitHub topic:<keyword> for OSS, Reddit + HN for complaints, vendor blogs for positioning (vs reality).
Step 4 — Score each entrant
Per entrant capture:
| Field | Notes |
|---|---|
| Name + URL | |
| Layer | direct / indirect / adjacent |
| Positioning (their words) | from homepage / pitch |
| Real-world use (review/complaint signal) | often differs from positioning |
| Pricing model | including free tier |
| Last meaningful release | stale tools = often opportunities |
| Key limitation for the client's user segment | the why-not-this part |
| What it's great at | be honest, not dismissive |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 86 lines · 68 tokens per session scan A c8bcbdb3bc61
competitive-scan is a skill published in the GitHub repository stanislavnianko/product-discovery-claude-skills (1 stars, last pushed 4mo ago), licensed MIT. It adds 68 tokens to every session and 1,079 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 2026-08-31.
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