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-adsgit 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-ads)<a href="https://agentmods.dev/skills/jasoncolapietro/suede-creator-skills/suede-ads"><img src="https://agentmods.dev/badge/skills/jasoncolapietro/suede-creator-skills/suede-ads/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-ads"><img src="https://agentmods.dev/badge/skills/jasoncolapietro/suede-creator-skills/suede-ads.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 450 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 | $0.00097 | $0.05605 |
| Opus 5 | $0.00048 | $0.02802 |
| Sonnet 5 | $0.00019 | $0.01121 |
| Haiku 4.5 | $0.00010 | $0.00560 |
Grade A, and why
suede-ads 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 — 459 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Suede Paid Ads
Use this Suede paid-acquisition playbook to create, optimize, and scale campaigns against explicit acquisition economics. Never assume account access.
Before Starting
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Gather this context (ask if not provided):
1. Campaign Goals
- Which platform(s) are running now, or which do you want to start with?
- What's the primary objective? (Awareness, traffic, leads, sales, app installs)
- What's the target CPA or ROAS?
- What's the monthly/weekly budget?
- Any constraints? (Brand guidelines, compliance, geographic)
2. Product & Offer
- What are you promoting? (Product, free trial, lead magnet, demo)
- What's the landing page URL?
- What makes this offer compelling?
3. Audience
- Who is the ideal customer?
- What problem does your product solve for them?
- What are they searching for or interested in?
- Do you have existing customer data for lookalikes?
4. Current State
- Have you run ads before? What worked/didn't?
- Do you have existing pixel/conversion data?
- What's your current funnel conversion rate?
- Do you have creative assets already, or do they need to be produced?
Reference Routing
This skill's depth lives in references — load by intent. For any operational decision on a live account (kill/keep/scale/budget), load the relevant playbook before answering; the thresholds live there, not here.
| User intent | Load | Covers |
|---|---|---|
| B2B strategy, funnel stages, budget splits, kill rules, lead quality, breakeven math | b2b-paid-playbook.md | Demand lifecycle, leading/lagging signals, kill rules, offline conversion loop, U/B/F lead scoring, scaling quadrant |
| Meta operations: when to kill/graduate/scale an ad, fatigue, testing structure | meta-decision-system.md | TCPL-anchored decision tree, ad-count ceiling, 80/20 CBO structure, fatigue bands, lead forms, Advantage+ transition |
| LinkedIn operations: bidding, audience sizing, scaling, benchmarks, TLAs, formats | linkedin-b2b-playbook.md | Bidding progression, penetration scaling, sizing rules, funnel benchmarks, document/conversation ads, audit shortlist |
| Google Search: what to spend on first, structure, match types, negatives, PMax | google-search-playbook.md | Intent ladder, account structure, match-type gates, negatives, bidding by volume, offline conversions, PMax guardrails |
| Named-account targeting, pipeline acceleration, cross-channel retargeting | abm-playbook.md | LinkedIn/Meta ABM, list mechanics, acceleration campaigns, UTM cross-channel remarketing, ABM measurement |
| Generating Google RSAs | rsa-output-spec.md | Mandatory output spec — limits, sidecars, template, self-check |
| Audience setup, tracking setup, launch checklists, copy formulas | audience-targeting.md · conversion-tracking.md · platform-setup-checklists.md · ad-copy-templates.md | Existing foundations |
What ships with it
13 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.
- agents/openai.yaml 576 B
- CARD.md 4.4 KB
- evals/evals.json 6.3 KB
- references/abm-playbook.md 7.0 KB
- references/ad-copy-templates.md 4.7 KB
- references/audience-targeting.md 5.9 KB
- references/b2b-paid-playbook.md 7.8 KB
- references/conversion-tracking.md 11 KB
- references/google-search-playbook.md 9.6 KB
- references/linkedin-b2b-playbook.md 8.7 KB
- references/meta-decision-system.md 10 KB
- references/platform-setup-checklists.md 7.4 KB
- references/rsa-output-spec.md 3.9 KB
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 · 459 lines · 97 tokens per session scan A c4ca4e0dd4ba
suede-ads is a skill published in the GitHub repository JasonColapietro/suede-creator-skills (135 stars, last pushed today), licensed MIT. It adds 97 tokens to every session and 5,605 once invoked, about $0.0005 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
skill-router
Use when a [skill-router] route card appears in the turn, when no card appeared on a non-trivial task, or when a route looks wrong. Routes every prompt to the right installed skill, pairs it with a process skill, tiers enforcement, and briefs sub-agents. 79 local skills + plugin skills indexed.
generate-report
Generate a comprehensive summary report of the latest experiment including metrics, plots, and comparison with baseline. Use this after training and evaluation to create a shareable experiment summary.
evaluate-model
Load the latest model checkpoint, run evaluation on the test set, and generate a metrics report with confusion matrix. Use this after training to assess model performance or to re-evaluate a specific checkpoint.
run-pipeline
Run the full data science pipeline: validate raw data, preprocess, engineer features, train model, and evaluate. Use this when you want to execute the end-to-end ML pipeline or re-run it after data or code changes.
api-test
Run API integration tests against the running backend, verify endpoints return expected responses and status codes. Use after deploying a preview or starting the dev server.
run-simulator
Build and launch the app in the iOS Simulator. Automatically selects an appropriate simulator device, boots it if needed, and installs and launches the app.