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 GoogilyBoogily/googilyboogily-claude-power-tools --skill lld-generategit clone --depth 1 https://github.com/GoogilyBoogily/googilyboogily-claude-power-toolsWrote 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/googilyboogily/googilyboogily-claude-power-tools/lld-generate)<a href="https://agentmods.dev/skills/googilyboogily/googilyboogily-claude-power-tools/lld-generate"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/lld-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.
<a href="https://agentmods.dev/skills/googilyboogily/googilyboogily-claude-power-tools/lld-generate"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/lld-generate.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 | $0.00049 | $0.01748 |
| Opus 5 | $0.00024 | $0.00874 |
| Sonnet 5 | $0.00010 | $0.00350 |
| Haiku 4.5 | $0.00005 | $0.00175 |
Grade A, and why
lld-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.
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLD Generator
Generate a complete Low 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. Engineers should be able to code directly from this document.
Input
$ARGUMENTS — path to the context file (e.g., docs/context/lld/graphql-migration-context.md), and optionally --hld <path> for cross-referencing the predecessor HLD.
Parse Arguments
Extract from $ARGUMENTS:
- Context File: First non-flag argument
- HLD Path:
--hld <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.
- Ground every claim. Every factual statement must trace back to a specific entry in the context file.
- Flag ungrounded claims. Mark anything not in the context file as
[ASSUMPTION]. - Never invent details. Missing information goes in Assumptions and Open Items — don't fabricate.
Process
Step 1: Read Inputs
- Read the context file from
$ARGUMENTS. - If
--hldprovided, read the HLD for cross-referencing. - Read the LLD template at
${CLAUDE_SKILL_DIR}/references/template.md.
Extract from the context file:
- HLD summary (problem, approach, components, decisions, phases)
- User answers (error handling, state, data contracts, integration details, performance, testing)
- Codebase findings (validated assumptions, reusable utilities, reference implementations, test patterns)
- Web research findings
- Open questions
Step 2: Determine Output Path
- Match the HLD's naming convention: if HLD is at
docs/hld/<name>.md, usedocs/lld/<name>.md - Create the directory if needed.
Step 3: Generate the LLD
Write the complete LLD document section by section, following the template:
-
Header — HLD reference path, author, date, status (Draft).
-
Scope (Section 1) — One paragraph. What this LLD covers and what it doesn't. Reference the HLD. Do NOT restate the problem, goals, or architecture overview.
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.
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.
- 12d ago First seen · 148 lines · 49 tokens per session scan A 19d47fe918e0
lld-generate is a skill published in the GitHub repository GoogilyBoogily/googilyboogily-claude-power-tools (2 stars, last pushed 4mo ago), licensed MIT. It adds 49 tokens to every session and 1,748 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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