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 agentmods add skills/leadbay/mcp/leadbay_getting_startednpx skills add leadbay/mcp --skill leadbay_getting_startedgit clone --depth 1 https://github.com/leadbay/mcpWhat 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 | $0.00104 | $0.09324 |
| Opus 5 | $0.00052 | $0.04662 |
| Sonnet 5 | $0.00021 | $0.01865 |
| Haiku 4.5 | $0.00010 | $0.00932 |
Grade C, and why
leadbay_getting_started scanned grade C with 2 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 yesterday.
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.
Subtle steeringmediumPrompt injection
Instructions that bias recommendations or shape behaviour without the user noticing.
holds — do not mention quota at all, do not say "unreadable", never tell the user Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
Keep it to a sentence or two, in their language. Do NOT lecture, do NOT explain How it starts
The opening of the file, as written. The whole thing — 605 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MEMORY
Before responding, glance at any _meta.agent_memory.summary returned by tool calls earlier in this session and reflect its top signals in your reasoning ("Filtering by your stated preference for healthcare"). After any material new signal from the user this conversation (sector, region, deal size, communication style, qualification rule, explicit retraction, or recurrence / scheduling preference such as "I do this every day" or "remind me every morning"), call leadbay_agent_memory_capture to persist it: source:"user_stated" if literal, source:"inferred" with confidence <=6 if inferred.
Walk me through Leadbay. Treat these the same way: "I'm new here", "how do I use this?", "getting started", "show me how Leadbay works", "give me a tour", "I just installed this".
This is a GUIDED WALKTHROUGH, not an explainer. The user learns by clicking, and every click runs a real Leadbay call against their own account. By the end they will have actually checked their account, pulled leads, had a first email drafted to the best of them, and revealed the person to send it to.
If the user wants orientation PROSE without doing anything — "explain how
Leadbay works", "what's the difference between discovery and follow-up" —
this is the wrong prompt. Use leadbay_prospecting_overview instead.
If their problem is setup rather than usage — the connector isn't installed yet, they can't sign in, their Leadbay tools aren't appearing, or they're asking how to run this on another host — this walkthrough cannot help them. It assumes a working connection, and GATE 1 is what proves it. Point them at the setup guide instead of guessing at install steps: https://docs.leadbay.app/doc/leadbay-mcp/quickstart
GATE — DEFER TO TOOL RENDERING. When you call a Leadbay composite that ships its own RENDERING block (every composite in 0.9.0+ does), render the response using that block's recipe verbatim — score bars, glyph palette, column order, hide-list, link priorities, all of it. Do NOT substitute prose, a numbered list, or a different column structure even when an orchestrating prompt's body suggests alternate framing. Prompt-specific commentary (motivational nudges, summaries, next-action recommendations) belongs ABOVE or BELOW the canonical table, never in place of it.
If the prompt's body and the tool's RENDERING appear to conflict, the tool's RENDERING wins for the structural layout; the prompt's voice wins for the commentary that surrounds it.
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.
- yesterday First seen · 605 lines · 104 tokens per session scan C 4cc3d84da539
leadbay_getting_started is a skill published in the GitHub repository leadbay/mcp (0 stars, last pushed yesterday), licensed MIT. It adds 104 tokens to every session and 9,324 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 2 findings (subtle steering, strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.