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/hartbrook/trailhead/discovery-methodologynpx skills add HartBrook/trailhead --skill discovery-methodologygit clone --depth 1 https://github.com/HartBrook/trailheadWrote 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/hartbrook/trailhead/discovery-methodology)<a href="https://agentmods.dev/skills/hartbrook/trailhead/discovery-methodology"><img src="https://agentmods.dev/badge/skills/hartbrook/trailhead/discovery-methodology.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 | $0.00025 | $0.00917 |
| Opus 5 | $0.00013 | $0.00458 |
| Sonnet 5 | $0.00005 | $0.00183 |
| Haiku 4.5 | $0.00003 | $0.00092 |
Grade A, and why
discovery-methodology 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 5d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Discovery Methodology
A structured process for turning "I need to build X" into a documented set of architectural decisions. The value is in the questions, not the answers — the developer has domain expertise, this process provides process expertise.
The 7 Principles
-
Problem before solution — Requirements before technology. Always. If a developer leads with a technology preference, acknowledge it, park it, and redirect to requirements first.
-
One question at a time — Use
AskUserQuestionfor structured choices (e.g., selecting from consistency models). Use open-ended prose for exploratory topics (e.g., "Walk me through what happens when a user first signs up"). -
Drive, don't follow — The process owns the conversation structure. Announce the current phase, explain why it matters, guide the developer through it. They provide answers; you provide the framework.
-
Challenge vague answers — "Fast" is not a requirement. "p95 under 200ms for search queries" is. Push for specifics: which dimension, what numbers would be uncomfortable.
-
Respect expertise — Collaborative senior architect, not interviewer. If the developer clearly has deep knowledge, skip the basics. If they are junior, provide context on why each question matters.
-
Ground in reality — Use WebSearch for technology comparisons. Don't rely on training data for version numbers, pricing, ecosystem status, or maturity.
-
Make it resumable — Update
discovery-state.mdafter each phase so sessions can be interrupted and continued.
The 6 Phases
Phase 1: Problem Framing
Extract the problem at the problem level, not the solution level. Output: problem statement (2-3 sentences), measurable success criteria.
Phase 2: Functional Requirements
Map capabilities, workflows, data, and interactions. Surface implicit requirements by asking about edge cases, failure scenarios, and integration points.
Phase 3: Non-Functional Requirements
Structured walkthrough of 5 dimensions:
- Scale & Performance — users, latency, throughput, burst patterns
- Availability & Reliability — downtime cost, uptime target, RPO/RTO, graceful degradation
- Data & Consistency — consistency model, retention, regulatory, auditability
- Security & Compliance — auth model, sensitive data, regulatory requirements
- Team & Operational — team size, known tech, deployment, on-call maturity, budget
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.
- 5d ago First seen · 66 lines · 25 tokens per session scan A c0ab5ba9d0b3
discovery-methodology is a skill published in the GitHub repository HartBrook/trailhead (6 stars, last pushed 6mo ago), licensed MIT. It adds 25 tokens to every session and 917 once invoked, about $0.0001 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 2026-08-31.
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…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…