hld-generate

hld-generate is a skill for Claude Code from GoogilyBoogily/googilyboogily-claude-power-tools. It costs 37 tokens per session (1,597 once invoked), scanned A, original, MIT.

A generator for a High Level Design (HLD), a document that describes a system's main structure and important technical choices. It builds the document from a previously prepared context file and can refer to an ADR, which records an earlier architecture decision.

In plain words
What is it for?
Use it to turn gathered project context into a complete HLD, such as for planning a GraphQL migration. It can also connect the new design to an earlier architecture decision.
Why use it?
It keeps design claims tied to recorded research and code findings instead of filling gaps with guesses. Missing information is collected as open questions or clearly marked as an assumption.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter.

Part of the architecture-docs plugin — 19 skills shipped together

Good fit Use it to turn gathered project context into a complete HLD, such as for planning a GraphQL migration. It can also connect the new design to an earlier architecture decision.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/googilyboogily/googilyboogily-claude-power-tools/hld-generate
Install

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.

Any agent
npx skills add GoogilyBoogily/googilyboogily-claude-power-tools --skill hld-generate
Clone the repo
git clone --depth 1 https://github.com/GoogilyBoogily/googilyboogily-claude-power-tools

Made for: Claude Code.

Or install architecture-docs, the plugin that ships this one along with the rest of its 19 skills.

Wrote 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.

agentmods badge for hld-generate

README.md
[![agentmods](https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/hld-generate/github.svg)](https://agentmods.dev/skills/googilyboogily/googilyboogily-claude-power-tools/hld-generate)
Your own site
<a href="https://agentmods.dev/skills/googilyboogily/googilyboogily-claude-power-tools/hld-generate"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/hld-generate/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.

agentmods 80×15 button for hld-generate

Your own site · 80×15
<a href="https://agentmods.dev/skills/googilyboogily/googilyboogily-claude-power-tools/hld-generate"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/hld-generate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,597 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00037 $0.01597
Opus 5 $0.00018 $0.00798
Sonnet 5 $0.00007 $0.00319
Haiku 4.5 $0.00004 $0.00160

Measured 12d ago against content hash fc422f7c7b61, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

hld-generate 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 12d 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.

plugins/architecture-docs/skills/hld-generate/SKILL.md · 138 lines

How it starts

The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.

HLD Generator

Generate a complete High Level Design document from a previously gathered context file. This skill runs with clean context and is non-interactive — all questions were answered during the gather phase.

Input

$ARGUMENTS — path to the context file (e.g., docs/context/hld/graphql-migration-context.md), and optionally --adr <path> for cross-referencing the predecessor ADR.

Parse Arguments

Extract from $ARGUMENTS:

  • Context File: First non-flag argument
  • ADR Path: --adr <path> (optional, for cross-referencing and back-reference updates)

Source Integrity Rules

Every factual claim in this document must be traceable to the context file.

  1. Ground every claim. Every factual statement must trace back to a specific entry in the context file (user answers, codebase findings with file:line, or web research with URLs).
  2. Flag ungrounded claims. If you need to state something not in the context file, mark it explicitly as [ASSUMPTION].
  3. Never invent details. If the context file doesn't cover something, put it in Open Questions — don't fabricate.

Process

Step 1: Read Inputs

  1. Read the context file from $ARGUMENTS.
  2. If --adr provided, read the ADR for cross-referencing.
  3. Read the HLD template at ${CLAUDE_SKILL_DIR}/references/template.md.

Extract from the context file:

  • Problem statement and goals/non-goals
  • User answers (scope, consumers, data entities, external systems, NFRs, deployment)
  • ADR context (if applicable)
  • Codebase findings with file:line citations
  • Web research findings with URLs
  • Change map (files to modify/create/delete, config changes, schema changes)
  • Implementation phases
  • Open questions

Step 2: Determine Output Path

  1. If docs/ exists, propose docs/hld/<kebab-case-name>.md
  2. If rfcs/ or designs/ exists, use that directory
  3. Otherwise, propose docs/hld/<kebab-case-name>.md
  4. Create the directory if needed.

Step 3: Generate the HLD

Write the complete HLD document section by section, following the template structure:

Read the full file on GitHub · 138 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 12d ago First seen · 138 lines · 37 tokens per session scan A fc422f7c7b61

Subscribe to this mod's changes

hld-generate is a skill published in the GitHub repository GoogilyBoogily/googilyboogily-claude-power-tools (2 stars, last pushed 4mo ago), licensed MIT. It adds 37 tokens to every session and 1,597 once invoked, about $0.0002 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.

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