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 adr-discussgit 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/adr-discuss)<a href="https://agentmods.dev/skills/googilyboogily/googilyboogily-claude-power-tools/adr-discuss"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/adr-discuss/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/adr-discuss"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/adr-discuss.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 | $0.00060 | $0.01605 |
| Opus 5 | $0.00030 | $0.00803 |
| Sonnet 5 | $0.00012 | $0.00321 |
| Haiku 4.5 | $0.00006 | $0.00161 |
Grade A, and why
adr-discuss 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADR Discussion — Gray Area Identification
Identify the decisions that actually matter before gathering detailed context. This skill scouts the codebase, finds existing constraints, surfaces concrete gray areas, and guides the user through each one. The output is a structured decisions file with D-XX numbered entries that downstream skills (adr-gather, adr-generate) treat as a contract.
Philosophy: Don't ask everything — ask what matters. Generic Q&A ("what's the problem?") wastes the user's time. Instead, analyze the decision space, identify where the outcome hinges on a choice, and focus the conversation there.
Input
$ARGUMENTS — the decision topic (e.g., "migrate from REST to GraphQL").
If no topic is provided, ask the user what architectural decision needs to be recorded.
Process
Human-in-the-loop: Every decision is captured from user input, never assumed.
Phase 1: Scout the Landscape
- Scan existing ADRs — Glob for
docs/decisions/*.md, read titles and status fields. Extract:- Active constraints (accepted ADRs that narrow this decision space)
- Related decisions (same domain, overlapping scope)
- Superseded decisions (deprecated approaches)
- Scout the codebase — lightweight Glob/Grep for patterns related to the decision topic:
- Existing implementations in the affected area
- Current conventions and patterns
- Integration points that constrain options
- Keep this fast — inline tools only, ~2 minutes max. This is NOT a full code research phase.
- Check for prior decisions file — look for
docs/context/decisions/<topic>-decisions.md. If found, offer to update vs start fresh.
Budget: Use at most 10 tool calls in this phase. The goal is orientation, not exhaustive research.
Phase 2: Identify Gray Areas
Analyze the decision topic in context of what you found in Phase 1. Identify 3-5 gray areas — concrete decision points where:
- Multiple valid approaches exist
- The choice materially changes the architecture
- The user's preference can't be inferred from existing constraints
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 · 155 lines · 60 tokens per session scan A de8f342a2cbe
adr-discuss is a skill published in the GitHub repository GoogilyBoogily/googilyboogily-claude-power-tools (2 stars, last pushed 4mo ago), licensed MIT. It adds 60 tokens to every session and 1,605 once invoked, about $0.0003 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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