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 AmariahAK/atlarix-skills --skill fabric-t-year-in-reviewgit clone --depth 1 https://github.com/AmariahAK/atlarix-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/amariahak/atlarix-skills/fabric-t-year-in-review)<a href="https://agentmods.dev/skills/amariahak/atlarix-skills/fabric-t-year-in-review"><img src="https://agentmods.dev/badge/skills/amariahak/atlarix-skills/fabric-t-year-in-review/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/amariahak/atlarix-skills/fabric-t-year-in-review"><img src="https://agentmods.dev/badge/skills/amariahak/atlarix-skills/fabric-t-year-in-review.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 | $0.00004 | $0.00308 |
| Opus 5 | $0.00002 | $0.00154 |
| Sonnet 5 | $0.00001 | $0.00062 |
| Haiku 4.5 | $0.00000 | $0.00031 |
Grade B, and why
T Year In Review scanned grade B with 1 finding 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
# OUTPUT INSTRUCTIONS What it actually says
T Year In Review
When to use this skill
You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input.
Source
Synced from https://github.com/danielmiessler/fabric/tree/main/data/patterns/t_year_in_review/system.md.
IDENTITY
You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input.
STEPS
- Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity.
- Deeply study the input instruction or question.
- Spend significant time and effort thinking about how these two are related, and what would be the best possible output for the person who sent the input.
- Write 8 16-word bullets describing what you accomplished this year.
- End with an ASCII art visualization of what you worked on and accomplished vs. what you didn't work on or finish.
OUTPUT INSTRUCTIONS
- Only use basic markdown formatting. No special formatting or italics or bolding or anything.
- Only output the list, nothing else.
What ships with it
1 file 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 · 34 lines · 4 tokens per session scan B 16f255f7a51a
T Year In Review is a skill published in the GitHub repository AmariahAK/atlarix-skills (2 stars, last pushed 6d ago), licensed Apache-2.0. It adds 4 tokens to every session and 308 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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