Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/MoizIbnYousaf/marketing-clinpx agentmods add skills/moizibnyousaf/marketing-cli/landscape-scanWrote 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/moizibnyousaf/marketing-cli/landscape-scan)<a href="https://agentmods.dev/skills/moizibnyousaf/marketing-cli/landscape-scan"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/landscape-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/moizibnyousaf/marketing-cli/landscape-scan"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/landscape-scan.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 411 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 | $0.00093 | $0.04236 |
| Opus 5 | $0.00046 | $0.02118 |
| Sonnet 5 | $0.00019 | $0.00847 |
| Haiku 4.5 | $0.00009 | $0.00424 |
Grade A, and why
landscape-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 9d 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 — 501 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/landscape-scan -- Ground-Truth Ecosystem Snapshot
Every claim your marketing makes sits on top of an assumption about the market. "We're the first to..." "Nobody else does..." "The market is moving toward..." When these assumptions are wrong, your positioning collapses on contact with reality. Customers who Google your claims and find them false don't come back.
This skill builds a verified ecosystem snapshot that becomes the single source of truth for all downstream content. The Claims Blacklist it produces hard-gates every content skill -- if a claim is blacklisted, no skill writes it. Period.
No SaaS tools needed. Live web research + user validation.
On Activation
- Check if
brand/directory exists in the project root. - If it does, read available files in priority order:
competitors.md-- existing competitive intel (primary input)positioning.md-- current positioning angles and claimsaudience.md-- target market and buyer personasvoice-profile.md-- brand personality (for tone of output)learnings.md-- past marketing learnings and corrections
- Apply loaded brand context to focus the landscape scan -- existing competitor data narrows the research query, positioning data identifies claims to verify.
- If
brand/does not exist or is empty, proceed without it -- this skill works standalone by asking the user foundational questions.
Iteration Detection
Before starting, check whether ./brand/landscape.md already exists.
If landscape.md EXISTS --> Refresh Mode
Do not start from scratch. Instead:
- Read the existing landscape file.
- Present a summary of the current state:
EXISTING LANDSCAPE SNAPSHOT Last updated {date} by /landscape-scan Ecosystem segments: {N} Claims blacklisted: {N} Market shifts tracked: {N} Freshness: {days} days old (threshold: 14 days) ------------------------------------------ What would you like to do? 1. Full refresh -- re-run /last30days, revalidate everything 2. Verify claims -- check if blacklisted claims are still invalid 3. Add new segment -- expand to cover a new market area 4. Rebuild from scratch -- discard and start over
What ships with it
2 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.
- 9d ago First seen · 501 lines · 93 tokens per session scan A 2d7603b336cc
landscape-scan is a skill published in the GitHub repository MoizIbnYousaf/marketing-cli (31 stars, last pushed 22d ago), licensed MIT. It adds 93 tokens to every session and 4,236 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.
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