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 AtlasOmnia/donna-starter --skill product-competitor-analysisgit clone --depth 1 https://github.com/AtlasOmnia/donna-starterWrote 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/atlasomnia/donna-starter/product-competitor-analysis)<a href="https://agentmods.dev/skills/atlasomnia/donna-starter/product-competitor-analysis"><img src="https://agentmods.dev/badge/skills/atlasomnia/donna-starter/product-competitor-analysis/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/atlasomnia/donna-starter/product-competitor-analysis"><img src="https://agentmods.dev/badge/skills/atlasomnia/donna-starter/product-competitor-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 97 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00053 | $0.01408 |
| Opus 5 | $0.00026 | $0.00704 |
| Sonnet 5 | $0.00011 | $0.00282 |
| Haiku 4.5 | $0.00005 | $0.00141 |
Grade A, and why
product-competitor-analysis 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 10d 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:
- product-competitor-analysis — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Competitor Analysis
What this skill covers
Use this skill when asked to compare a local app/product to mainstream alternatives from local evidence only (or with explicit permission to use external research). It is intentionally structured for code-first competitive assessments where output should cite files, line ranges, and concrete implementation details.
Core principle
Every claim in the final analysis must be traceable to one of:
- concrete local code artifacts (files + line ranges)
- local config/docs
- command output that is itself grounded in local files
Category separation: native integration versus standalone replacement
When comparing an in-host integration to a standalone AI application, classify the products before scoring features. Do not treat “AI edits Office files” as the category; distinguish:
- Standalone replacement: owns its document, spreadsheet, or presentation engine; typically wins on convenience, breadth, cross-platform independence, and zero host-app requirements.
- Native integration: operates against the live host application's object model; can win on active-document context, fidelity to existing business files, in-place workflows, and verified host mutations, but carries more installation and compatibility complexity.
State the customer segment explicitly. A native integration is not universally superior: it is materially stronger for customers already dependent on the host application and its existing templates, while a standalone replacement may be better for users seeking an Office alternative. Avoid declaring a moat from architecture alone; translate the difference into an observable before/after workflow and identify the host-acceptance, onboarding, and reliability gates required to make the advantage real.
For this comparison pattern, apply the comparison framework in this skill.
Preflight
Before analysis:
- Confirm target slice and language scope (e.g., iOS, macOS, CLI).
- Confirm whether external validation is allowed.
- If user says “no web research” (or equivalent), perform conceptual only comparisons against mainstream categories.
- Capture project shape first (top-level files, app layers, active targets).
- Identify authoritative README(s), architecture notes, and build metadata.
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.
- 10d ago First seen · 170 lines · 53 tokens per session scan A 79cc21815b0c
product-competitor-analysis is a skill published in the GitHub repository AtlasOmnia/donna-starter (107 stars, last pushed 10d ago), licensed MIT. It adds 53 tokens to every session and 1,408 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.
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