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 ankitjha67/product-architect --skill product-architectgit clone --depth 1 https://github.com/ankitjha67/product-architectWrote 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/ankitjha67/product-architect/product-architect)<a href="https://agentmods.dev/skills/ankitjha67/product-architect/product-architect"><img src="https://agentmods.dev/badge/skills/ankitjha67/product-architect/product-architect/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/ankitjha67/product-architect/product-architect"><img src="https://agentmods.dev/badge/skills/ankitjha67/product-architect/product-architect.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 | $0.00253 | $0.05028 |
| Opus 5.5 | $0.00101 | $0.02011 |
| Sonnet 5.5 | $0.00051 | $0.01006 |
| Haiku 4.5 | $0.00025 | $0.00503 |
Grade A, and why
product-architect 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 1mo 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 — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Architect
80 specialized agents covering every department from solo founder Day 0 to IPO. 36 frameworks with tactical playbooks, compliance guides, process maps, and a modern AI-engineering stack (LangGraph, RAG, agents) applied across every department.
Depth Promise: Research-First, Never Surface-Level
This system goes deep "until the Mariana Trench." Before recommending building
ANY feature, product, or bet, agents run the Deep Research Protocol
(frameworks/deep-research-protocol.md, owned by Agent 47): they investigate the
market end to end and return a grounded verdict - "this already exists, here are
the competitors + citations, refine it" or "this is white-space, no competition
or citations found in this niche" (with the honest caveat that absence of evidence
is not proof of novelty). Every agent inherits this via references/agent-standards.md
and must grade its output L3+ on the Depth Rubric. Agents never fabricate a company,
statistic, study, patent, or URL; when live-search tools are unavailable they say so
and label market claims as hypotheses.
Every agent also reasons through the Enterprise Reasoning Protocol
(references/agent-standards.md): frame → options (≥2, incl. do-nothing) → evidence →
quantified trade-offs → recommendation with sensitivity → risks + reversal condition →
verify against KDRs and governance. In enterprise/regulated contexts, agents add the six
enterprise lenses (compliance & audit trail, scale/SLA, integration with the existing
stack, procurement/security review, change management, 3-year TCO). Each agent file
carries its own Decision Framework specializing this protocol for its domain's hardest calls.
Critical: Read SMART-LOADER.md First
Before loading any agent files, consult SMART-LOADER.md. It contains:
- Request classification and agent routing (which agents to load)
- Context budget rules (never load more than 5 agents per turn)
- Multi-intent decomposition (handling complex requests)
- KDR memory system (Key Decision Records that survive chat compaction)
- Conflict detection protocol (what to do when agents disagree)
What ships with it
9 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.
- references/agent-standards.md 20 KB
- references/compliance/eu.md 14 KB
- references/compliance/india.md 17 KB
- references/compliance/sea.md 14 KB
- references/compliance/uk.md 12 KB
- references/compliance/us.md 15 KB
- references/DISCLAIMER.md 7.2 KB
- references/github-readme.md 14 KB
- references/industry-references.md 7.1 KB
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.
- 1mo ago Changed · +86 lines · +42 tokens per session 4860c6b5d094
- 1mo ago First seen · 263 lines · 211 tokens per session scan A 178198c87a01
product-architect is a skill published in the GitHub repository ankitjha67/product-architect (118 stars, last pushed 1mo ago), licensed MIT. It adds 253 tokens to every session and 5,028 once invoked, about $0.0010 per session on Opus 5.5. 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
gitlab-platform
GitLab merge requests, review threads, failed pipelines and stacked merge requests, through GitLab's MCP server and the glab CLI. Use when opening, reviewing or updating a merge request, fixing a failed pipeline, or splitting a change into a stack.
self-improvement
Keeps the team's memory: where what you learned goes (your memory, shared with the team when it is about the project, a lesson in .opencastle/lessons/, or a team skill for a procedure), and how lessons are cited, re-verified and graduated. Use when you confirm something true for anyone working in the repository (a fix…
typescript-best-practices
Type design in TypeScript: discriminated unions, branded types, narrowing, exhaustiveness and boundary validation. Use when modelling domain types, tightening strictness or generics, removing as casts, or migrating code to stricter types.
github-platform
GitHub pull requests, review threads, failing Actions checks and stacked pull requests, through GitHub's MCP server and the gh CLI. Use when opening, reviewing or updating a pull request, fixing a failed check, or splitting a change into a stack.
nextjs-framework
Next.js App Router conventions, server and client components, data fetching, caching and route protection, by Next version. Use when building or debugging a Next.js app, its caching, middleware/proxy or hydration errors.
vercel-deployment
Vercel deployments, environments, domains and build troubleshooting through the Vercel MCP. Use when deploying to Vercel, checking a deployment, reading its build or runtime logs, or managing its environments.