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.
git clone --depth 1 https://github.com/Amey-Thakur/AI-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/commands/amey-thakur/ai-skills/promotion-packet)<a href="https://agentmods.dev/commands/amey-thakur/ai-skills/promotion-packet"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/promotion-packet/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/commands/amey-thakur/ai-skills/promotion-packet"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/promotion-packet.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 | $0.00021 | $0.00380 |
| Opus 5 | $0.00010 | $0.00190 |
| Sonnet 5 | $0.00004 | $0.00076 |
| Haiku 4.5 | $0.00002 | $0.00038 |
Grade A, and why
promotion-packet 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 6d 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.
What it actually says
You were invoked as a slash command. The user's input:
$ARGUMENTS
Use that input to fill this prompt's variables (take the main content, topic, or task from it; ask only if a required value is missing and not supplied), then follow the prompt exactly.
Write a promotion packet / self-review from this work:
{work}
Target level: {level}
Frame it around impact at the next level:
- Lead with impact, not activity: what changed because of your work (the business or team outcome, quantified), not what you were busy doing. "Cut incident rate 40% by redesigning the deploy pipeline" beats "Worked on deploy tooling".
- Map to the level's expectations: promotion is evidence you are ALREADY operating at the next level. For each expectation of that level (scope, technical leadership, influence, ambiguity handled), give concrete evidence.
- Show scope and multiplier effects: work that made others more effective, decisions that outlived the project, ambiguity you resolved. Higher levels are about leverage, not just output.
- Be specific and verifiable: numbers, named projects, concrete examples a reviewer could confirm.
Rules: honest and defensible, never inflated (a packet that overclaims gets challenged and hurts you). Take real credit without exaggeration: "led" only if you led, "contributed to" otherwise. Quantify impact wherever the number exists; if it does not, describe the concrete change. Where the work does not yet show next-level scope, note the gap honestly rather than papering over it. Mark any metric needing a real figure.
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.
- 6d ago First seen · 42 lines · 21 tokens per session scan A cd88457bf071
promotion-packet is a command published in the GitHub repository Amey-Thakur/AI-SKILLS (7 stars, last pushed 6d ago), licensed MIT. It adds 21 tokens to every session and 380 once invoked, about $0.0001 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 2026-09-06.
Other commands, from other repositories
close
They operate it without you.
create-ticket
Create a work-item ticket (GitHub issue / Jira) from an existing requirements.md, then promote its draft spec folder to docs/specs/ /.
finish-tasks
Finalize a work item after all tasks are done — cleanup (currently closing the ticket) and mark the work complete.
work-status
Report the current status of a work item by reading its spec files (requirements/design/tasks) and execution log. Read-only.
/opsx-apply
Implement tasks from an OpenSpec change (Experimental).
align
Align cross-functional stakeholders.