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 JasonColapietro/suede-creator-skills --skill suede-competitor-profilinggit clone --depth 1 https://github.com/JasonColapietro/suede-creator-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/jasoncolapietro/suede-creator-skills/suede-competitor-profiling)<a href="https://agentmods.dev/skills/jasoncolapietro/suede-creator-skills/suede-competitor-profiling"><img src="https://agentmods.dev/badge/skills/jasoncolapietro/suede-creator-skills/suede-competitor-profiling/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/jasoncolapietro/suede-creator-skills/suede-competitor-profiling"><img src="https://agentmods.dev/badge/skills/jasoncolapietro/suede-creator-skills/suede-competitor-profiling.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 Excessive Agency · line 16 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00085 | $0.03130 |
| Opus 5 | $0.00043 | $0.01565 |
| Sonnet 5 | $0.00017 | $0.00626 |
| Haiku 4.5 | $0.00009 | $0.00313 |
Grade A, and why
suede-competitor-profiling 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 11d 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 — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Suede Competitor Profiling
Use this Suede competitive-intelligence playbook to turn current public evidence into structured profiles with fact, inference, and unknowns kept separate.
Initial Assessment
Check for .agents/product-marketing.md (or .claude/product-marketing.md, or the legacy product-marketing-context.md) and read it if present — your own positioning and ICP decide which competitors are actually comparable and which dimensions are worth profiling, and they are usually already written down there.
Then work the intake list under Task-Specific Questions below. If the user gave URLs and the context file covers the rest, proceed without asking.
Saving Raw Data
Before synthesizing the profile, persist all raw page captures, SEO inputs, and review evidence to disk so they can be re-read, audited, or reused without repeating provider requests or manual collection.
Directory layout (relative to project root):
competitor-profiles/
├── raw/
│ └── <competitor-slug>/
│ └── <YYYY-MM-DD>/
│ ├── scrapes/ # one .md file per captured page (homepage.md, pricing.md, ...)
│ ├── seo/ # one .json or .csv file per authorized metric source
│ └── reviews/ # one .md or .json file per review source (g2.md, capterra.md, ...)
├── <competitor-slug>.md # final synthesized profile
└── _summary.md # cross-competitor summary
Rules:
<competitor-slug>is lowercase, hyphenated (e.g.responsehub,safe-base)<YYYY-MM-DD>is the date the data was pulled — supports re-running and diffing snapshots over time- Save each browser, manual, or authorized-fetch page capture as raw markdown
to
scrapes/<page-name>.md - Save each authorized SEO response or user-supplied export to
seo/<source-name>.<json|csv> - Save each review source to
reviews/<source>.md(cleaned text) or.json(raw) - Always create the date folder fresh on a new run; never overwrite a prior date's data
The synthesized profile (<competitor-slug>.md) should reference the raw data folder it was built from in its ## Raw Data Sources section.
What ships with it
5 files 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.
- 11d ago First seen · 334 lines · 85 tokens per session scan A 9702d0b7993b
suede-competitor-profiling is a skill published in the GitHub repository JasonColapietro/suede-creator-skills (135 stars, last pushed today), licensed MIT. It adds 85 tokens to every session and 3,130 once invoked, about $0.0004 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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