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 implementgit 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/implement)<a href="https://agentmods.dev/skills/googilyboogily/googilyboogily-claude-power-tools/implement"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/implement/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/implement"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/implement.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 | $0.00051 | $0.03104 |
| Opus 5 | $0.00026 | $0.01552 |
| Sonnet 5 | $0.00010 | $0.00621 |
| Haiku 4.5 | $0.00005 | $0.00310 |
Grade A, and why
implement scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| T-01 | Truth | GraphQL responds with schema | `curl /graphql` + check introspection | How it starts
The opening of the file, as written. The whole thing — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Document Implementation
Translate an HLD or LLD into working code through phased, verified implementation with goal-backward verification. Performs gap analysis, establishes must-haves before execution, and verifies the actual goals were achieved — not just that tasks completed.
When to Use
Use after an HLD or LLD is finalized and ready for implementation. Works best with LLDs (which contain file-level implementation plans), but can also work from HLDs at a higher level.
Use --resume to continue from a paused implementation session.
Source Integrity Rules
Every factual claim about the codebase must be verified through tool calls in this session.
- Cite your work. When referencing code, patterns, or architecture, cite the specific tool call that discovered it (file path + line number from Read, Grep result, Explore agent finding).
- Never reference prior Claude sessions or Claude memory. Do not source implementation decisions from auto-memory, MCP memory tools, or cross-session context.
- Assumptions are labeled, not hidden. If you lack evidence for a claim and cannot research it, explicitly label it as an assumption.
Process
Human-in-the-loop: Never proceed past a decision point without user approval. Each implementation phase requires explicit sign-off before moving to the next.
Resume Check
If --resume is set or .continue-here.md exists in the project root:
- Read
.continue-here.md - Present what was completed, what remains, and any anti-patterns encountered
- Ask: "Resume from where we left off?"
- If yes: skip to the recorded next action
- If no: start fresh (rename
.continue-here.mdto.continue-here-<timestamp>.md)
Phase 1: Absorb the Design Document
- Read the document at the path provided via
$ARGUMENTS. If no path is provided, ask which HLD or LLD to implement. - Detect document type (HLD vs LLD) from content and structure:
- LLD indicators: method signatures, sequence diagrams, file-level implementation plan, error catalogs
- HLD indicators: component diagrams, trade-off analysis, high-level architecture
- If HLD: check if a corresponding LLD exists (look for
lld-variant of the filename, or references in the HLD). If found, suggest reading both. If not, note that implementation will proceed at a higher level of abstraction. - Extract key elements:
- Goals — what the implementation achieves
- Components — modules, services, files involved
- Implementation phases — if the doc defines them
- File-level plan — specific files to create/modify (LLD)
- Dependencies — libraries, services, infrastructure
- Assumptions — stated assumptions that need validation
- Open questions — unresolved items from the design process
- D-XX decisions — if the context file has numbered decisions, extract them for coverage tracking
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 · 281 lines · 51 tokens per session scan A 253af511f8e6
implement is a skill published in the GitHub repository GoogilyBoogily/googilyboogily-claude-power-tools (2 stars, last pushed 4mo ago), licensed MIT. It adds 51 tokens to every session and 3,104 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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