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 Citlyze/citlyze-skills --skill citlyze-action-plangit clone --depth 1 https://github.com/Citlyze/citlyze-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/citlyze/citlyze-skills/citlyze-action-plan)<a href="https://agentmods.dev/skills/citlyze/citlyze-skills/citlyze-action-plan"><img src="https://agentmods.dev/badge/skills/citlyze/citlyze-skills/citlyze-action-plan/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/citlyze/citlyze-skills/citlyze-action-plan"><img src="https://agentmods.dev/badge/skills/citlyze/citlyze-skills/citlyze-action-plan.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 | $0.00066 | $0.00603 |
| Opus 5 | $0.00033 | $0.00302 |
| Sonnet 5 | $0.00013 | $0.00121 |
| Haiku 4.5 | $0.00007 | $0.00060 |
Grade A, and why
citlyze-action-plan 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Citlyze action plan
Convert the recommendation backlog into a two-week plan a marketing or content team can execute.
Prerequisites
The Citlyze MCP server must be connected (setup guide). Tools are read-only; marking recommendations planned or done happens in the app.
Workflow
- Pull the backlog. Call
list_recommendations. Rows arrive highestpriority_scorefirst, each withtitle,summary,category,status,impact,confidence,effort, andtarget_url. - Filter. Drop rows whose
statusshows they are already done or dismissed; keep new and in-progress work. Treatcategoryas a free-form label: group by whatever values appear. - Build an impact/effort matrix. Place each recommendation in one of four quadrants (high impact + low effort first). Where
confidenceis low, say what evidence would raise it before committing serious effort. - Cluster by page. Group recommendations sharing a
target_url; one page edit often clears several items at once. Clusters usually beat one-off fixes. - Sequence into a plan:
- Week 1 - quick wins (high impact, low effort) plus anything unblocking other items.
- Week 2 - the highest-impact medium-effort cluster.
- Backlog - everything else in priority order, with a one-line reason each.
- For each planned item state: what to change, on which URL, who plausibly owns it (content, dev, PR), and how success will show up in Citlyze afterwards (better mention rate, new citation, improved visibility on a named engine).
Interpretation rules
priority_scorealready blends Citlyze's view of value; use it as the default sort but override with the user's business context when they give any.- Never promise a score outcome; frame expected results as the metric to watch in the next measurement window.
- If two recommendations conflict on one page, surface the conflict instead of silently picking one.
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 · 36 lines · 66 tokens per session scan A bc9b9ecbe0ad
citlyze-action-plan is a skill published in the GitHub repository Citlyze/citlyze-skills (0 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 603 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.
Other skills, from other repositories
ganhuo-geo-engineer
Use this Skill when the user provides an existing article, product page, tutorial, FAQ, or knowledge note and wants to rebuild it into a GEO or AI-search-friendly content asset. Use it for old-content refresh, citation-readiness improvement, answer-first restructuring, GEO upgrades, and Ganhuo AI content workflows. Do…
xerj-code
Reference-coding with XERJ. Clone the libraries that already solved your problem, index them locally, and retrieve the exact implementation before writing code — so the agent reads passages instead of re-deriving algorithms across retry loops. Use when starting a task in an unfamiliar API, porting an algorithm, or…
fire-your-seo-agency
A procedure for improving how a website appears in search engines and how AI answer systems find and cite it. It covers search, answer-engine, generative-AI, and Naver visibility.
geo-loop
Run one bounded eGEOagents loop iteration over a workspace domain - read the charter and fresh collector data, do ONE unit of work, write substrate artifacts, append one Timeline entry and one LOG line. Use for loop mode, /geo:loop, scheduled GEO runs, or continuous monitoring.
competitive-analysis
Analyze AI-search competitors for a query and recommend ranking strategy. Use when users ask competitor analysis, who ranks, or competitive landscape.
validation-doctor
Check Brave Search and Chrome DevTools MCP availability and provide exact setup snippets. Use when validation dependencies are missing or uncertain.