Borrowing it
Nothing to install: this file belongs to ozgurkarahan/ai-agent-memory. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ozgurkarahan/ai-agent-memory/master/.claude/skills/new-engagement/SKILL.mdgit clone --depth 1 https://github.com/ozgurkarahan/ai-agent-memoryWrote 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/ozgurkarahan/ai-agent-memory/new-engagement)<a href="https://agentmods.dev/skills/ozgurkarahan/ai-agent-memory/new-engagement"><img src="https://agentmods.dev/badge/skills/ozgurkarahan/ai-agent-memory/new-engagement/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/ozgurkarahan/ai-agent-memory/new-engagement"><img src="https://agentmods.dev/badge/skills/ozgurkarahan/ai-agent-memory/new-engagement.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 | $0.00058 | $0.02152 |
| Opus 5.5 | $0.00023 | $0.00861 |
| Sonnet 5.5 | $0.00012 | $0.00430 |
| Haiku 4.5 | $0.00006 | $0.00215 |
Grade C, and why
new-engagement scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf "$PROJECT_DIR/.git" How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
New Engagement
When the user says "new engagement", "scaffold engagement", or invokes a slash command like /new-engagement, scaffold one or more client engagement projects from the resolved project template.
Resolve required roots
Before scaffolding:
- Resolve
WIKI_ROOTusingmemory/schema.md, thenschema.md, then the memory-wiki path declared inAGENT.md. - Resolve
TEMPLATE_ROOTby checking forproject-template/besideWIKI_ROOT, then the path declared inAGENT.md. - If the template is absent but network access is available, the agent may clone
https://github.com/ozgurkarahan/ai-agent-memory.gitinto a temporary folder and use itsproject-template/directory. - If either root remains unresolved, report the missing root and stop. Do not guess a personal path.
- Remove any temporary clone after scaffolding.
All wiki paths below are relative to WIKI_ROOT.
Step 1: Parse the request
Extract from the user input:
- Client name (e.g., "Acme", "Contoso", "Contoso")
- Topics — one or more engagement topics, each with a format
- Projects root — use an explicit destination from the request or
AGENT.md; otherwise ask where to create the client workspace
Expected input format: <ClientName> — <topic1> (format), <topic2> (format)
Formats: presentation, workshop, demo, presentation + demo, or combinations.
If the input is ambiguous or missing details, ask clarifying questions:
- What is the target audience? (e.g., technical leadership, developers, executives)
- What format? (presentation, workshop, demo, or combination)
- What are the key objectives?
- Any specific technologies or topics to cover?
Step 2: Scaffold each project
For each topic, do the following.
2a. Create project directory
PROJECT_DIR={PROJECTS_ROOT}/{ClientName}/10-projects/{project-slug}
mkdir -p "$PROJECT_DIR"
Convert the topic to a kebab-case slug (e.g., "Agent Framework Engagement" → agent-framework-engagement).
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.
- 2mo ago First seen · 249 lines · 58 tokens per session scan C 845881d72e77
new-engagement is a skill published in the GitHub repository ozgurkarahan/ai-agent-memory (8 stars, last pushed 3mo ago), licensed MIT. It adds 58 tokens to every session and 2,152 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
new-engagement compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| new-engagementozgurkarahan/ai-agent-memory | Skill | 8 | 3mo ago | MIT | C | 58 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |