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/manusco/resonance/outbound-sequencenpx skills add manusco/resonance --skill outbound-sequencegit clone --depth 1 https://github.com/manusco/resonanceWrote 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/manusco/resonance/outbound-sequence)<a href="https://agentmods.dev/skills/manusco/resonance/outbound-sequence"><img src="https://agentmods.dev/badge/skills/manusco/resonance/outbound-sequence.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.01471 |
| Opus 5 | $0.00026 | $0.00736 |
| Sonnet 5 | $0.00011 | $0.00294 |
| Haiku 4.5 | $0.00005 | $0.00147 |
Grade A, and why
resonance-sales-outbound-sequence 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 4d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/resonance-sales-outbound-sequence: draft sequences that sell, not spam
Role: resonance-sales Input: Target description (persona, trigger event, campaign context) + CRM/account data + sender's value proposition. Output: A structured, source-backed outbound sequence with operator notes. Definition of Done: The sequence contains 3-5 steps with subject lines, body copy, CTAs, personalization markers, operator notes, and assumption flags. Every claim traces to a source. No fabricated proof. No fake reply framing. No auto-send. Free of AI slop and em dashes. Passed the validator.
Prerequisites (fail fast)
- A target persona is identified (role, seniority, industry, company size).
- At least one trigger signal or campaign context is provided (event, behavior, timing, pain hypothesis).
- The sender's value proposition is clear enough to connect to a specific outcome.
Algorithm
Copy this checklist and tick items as you go.
-
Context Gathering: Pull the best available context from CRM records, account notes, campaign docs, meeting notes, and any approved data sources. Separate facts from assumptions. → verify: sources are listed, assumptions are flagged.
-
Decompose the Motion: Identify the five elements that make a sequence specific:
- Audience: Who exactly receives this? Role, seniority, segment.
- Signal: What triggered this outreach? Event, behavior, timing, pain.
- Offer: What is the specific value proposition for this audience?
- Sender: Who sends it? Rep, founder, SDR? Tone follows sender.
- Channel mix: Email, LinkedIn, call, video? Sequence the channels. → verify: all five elements are documented before drafting.
-
Separate Reusable from Specific: Identify which elements can be templated across the campaign and which must change per account or per persona. Mark personalization points with
[PERSONALIZE: field]markers. Never treat first name, company name, or a title token as real personalization. → verify: each personalized sentence uses a real source, signal, or pain hypothesis.
What ships with it
5 files 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.
- 4d ago First seen · 82 lines · 53 tokens per session scan A f00a96e750ee
resonance-sales-outbound-sequence is a skill published in the GitHub repository manusco/resonance (37 stars, last pushed 2d ago), licensed MIT. It adds 53 tokens to every session and 1,471 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 2026-08-30.
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.
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.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…