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-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/adr-generate)<a href="https://agentmods.dev/skills/googilyboogily/googilyboogily-claude-power-tools/adr-generate"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/adr-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/adr-generate"><img src="https://agentmods.dev/badge/skills/googilyboogily/googilyboogily-claude-power-tools/adr-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 | $0.00042 | $0.01479 |
| Opus 5 | $0.00021 | $0.00740 |
| Sonnet 5 | $0.00008 | $0.00296 |
| Haiku 4.5 | $0.00004 | $0.00148 |
Grade A, and why
adr-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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADR Generator
Generate a complete Architecture Decision Record 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/decisions/migrate-to-graphql-context.md).
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 (user answers, codebase findings with file:line, or web research with URLs).
- Flag ungrounded claims. If you need to state something not in the context file, mark it explicitly as
[ASSUMPTION]in the document. - Never invent details. If the context file doesn't cover something, put it in Open Questions or More Information — don't fabricate.
MADR 4.0.0 Template
The output file MUST match this structure precisely, including HTML comments and frontmatter. Optional sections should be INCLUDED by default unless the context file explicitly says to skip them.
---
status: "{proposed | rejected | accepted | deprecated | ... | superseded by ADR-NNNN}"
date: {YYYY-MM-DD when the decision was last updated}
decision-makers: {list everyone involved in the decision}
consulted: {list everyone whose opinions are sought; two-way communication}
informed: {list everyone kept up-to-date; one-way communication}
---
# {short title, representative of solved problem and found solution}
## Context and Problem Statement
{Describe the context and problem statement in 2-3 sentences.}
<!-- This is an optional element. Feel free to remove. -->
## Decision Drivers
* {decision driver 1}
* {decision driver 2}
## Considered Options
* {title of option 1}
* {title of option 2}
* {title of option 3}
## Decision Outcome
Chosen option: "{title of option}", because {justification}.
<!-- This is an optional element. Feel free to remove. -->
### Consequences
#### Good
* Good, because {positive consequence}
#### Bad
* Bad, because {negative consequence}
<!-- This is an optional element. Feel free to remove. -->
### Confirmation
{How implementation correctness will be verified.}
<!-- This is an optional element. Feel free to remove. -->
## Pros and Cons of the Options
### {title of option 1}
{description}
#### Good
* {argument}
#### Neutral
* {argument}
#### Bad
* {argument}
### {title of option 2}
...
<!-- This is an optional element. Feel free to remove. -->
## More Information
{Additional context, links, team agreements, revisit timeline.}
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 · 178 lines · 42 tokens per session scan A 8a68dc7cbdc3
adr-generate is a skill published in the GitHub repository GoogilyBoogily/googilyboogily-claude-power-tools (2 stars, last pushed 4mo ago), licensed MIT. It adds 42 tokens to every session and 1,479 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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