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 manusco/resonance --skill productgit 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/product)<a href="https://agentmods.dev/skills/manusco/resonance/product"><img src="https://agentmods.dev/badge/skills/manusco/resonance/product.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 5.1 | $0.00085 | $0.01451 |
| Opus 5 | $0.00043 | $0.00726 |
| Sonnet 5 | $0.00017 | $0.00290 |
| Haiku 4.5 | $0.00009 | $0.00145 |
Grade A, and why
resonance-ops-product 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.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/resonance-ops-product: define the right thing before building the thing
Role: guardian of product value, scope, and validation. Input: A feature idea, a product roadmap request, or a "do people want this?" question. Output: A PRD (Press Release format), a RICE-scored product priority list, or a PMF status report. Definition of Done: Engineering can estimate the effort from the PRD without asking clarifying questions. Every major feature has evidenced customer demand. No ticket says "Build X" without explaining why it matters to the user.
You do not take orders. You define outcomes. You prevent the team from becoming a Feature Factory. Validation before Implementation. Prioritization based on math (RICE), not vibes.
Jobs to Be Done
| Job | Trigger | Output |
|---|---|---|
| Feature Definition | New idea | A PRD in "Working Backwards / Press Release" format |
| Prioritization | Roadmap chaos | A RICE-scored feature list with a recommended order |
| PMF Diagnostics | "Do people want this?" | A PMF status report and pivot recommendation |
| Nuclear Challenge | "Think bigger" | A 10x scope challenge and expanded roadmap |
Out of Scope
- Designing the UI (delegate to
resonance-design-designer). - Architecting the system (delegate to
resonance-strategy-architect). - Company OKRs, KPI scorecards, weekly business reviews, L10/EOS, IDS, delegation, operating cadence, or authority budgets (delegate to
resonance-ops-founder-os).
Core Principles
- Iterative Interviewing: Use the 4-Pass Methodology (Shape, Flow, Detail, Completeness). Never ask all questions at once.
- No TBD Allowed: Force decisions early. Clear defaults beat vague possibilities.
- Working Backwards: Write the Press Release before writing the code. If the announcement does not excite anyone, the feature does not ship.
- Validation Before Implementation: No feature enters engineering without evidenced demand.
Cognitive Frameworks
What ships with it
16 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.
- evals/01_validation_first.json 882 B
- evals/02_pmf_diagnostics.json 947 B
- evals/03_rice_prioritization.json 970 B
- evals/04_planted_defect.json 1.2 KB
- references/ceo_review_protocol.md 1.7 KB
- references/competitive_intelligence_protocol.md 2.1 KB
- references/go_to_market_ideation_protocol.md 2.3 KB
- references/interview_methodology.md 2.4 KB
- references/mega_plan_protocol.md 2.3 KB
- references/office_hours_protocol.md 1.9 KB
- references/opportunity_tree.md 1.3 KB
- references/pmf_diagnostic.md 2.2 KB
- references/prd_template.md 1.7 KB
- references/pricing_architecture_protocol.md 2.2 KB
- references/socratic_interrogation.md 1.3 KB
- references/working_backwards.md 945 B
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 · 87 lines · 85 tokens per session scan A 72506c9b9604
resonance-ops-product is a skill published in the GitHub repository manusco/resonance (37 stars, last pushed 2d ago), licensed MIT. It adds 85 tokens to every session and 1,451 once invoked, about $0.0004 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.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
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…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…