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 hashgraph-online/awesome-codex-plugins --skill architecture-decision-recordsgit clone --depth 1 https://github.com/hashgraph-online/awesome-codex-pluginsWrote 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/hashgraph-online/awesome-codex-plugins/architecture-decision-records)<a href="https://agentmods.dev/skills/hashgraph-online/awesome-codex-plugins/architecture-decision-records"><img src="https://agentmods.dev/badge/skills/hashgraph-online/awesome-codex-plugins/architecture-decision-records.svg" alt="Measured on agentmods" 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 | $0.00055 | $0.01633 |
| Opus 5 | $0.00028 | $0.00816 |
| Sonnet 5 | $0.00011 | $0.00327 |
| Haiku 4.5 | $0.00006 | $0.00163 |
Grade A, and why
architecture-decision-records 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 3d 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.
This is a copy
92% identical to architecture-decision-records — 28 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Decision Records
Capture architectural decisions as they happen during coding sessions. Instead of decisions living only in Slack threads, PR comments, or someone's memory, this skill produces structured ADR documents that live alongside the code.
When to Activate
- User explicitly says "let's record this decision" or "ADR this"
- User chooses between significant alternatives (framework, library, pattern, database, API design)
- User says "we decided to..." or "the reason we're doing X instead of Y is..."
- User asks "why did we choose X?" (read existing ADRs)
- During planning phases when architectural trade-offs are discussed
ADR Format
Use the lightweight ADR format proposed by Michael Nygard, adapted for AI-assisted development:
# ADR-NNNN: [Decision Title]
**Date**: YYYY-MM-DD
**Status**: proposed | accepted | deprecated | superseded by ADR-NNNN
**Deciders**: [who was involved]
## Context
What is the issue that we're seeing that is motivating this decision or change?
[2-5 sentences describing the situation, constraints, and forces at play]
## Decision
What is the change that we're proposing and/or doing?
[1-3 sentences stating the decision clearly]
## Alternatives Considered
### Alternative 1: [Name]
- **Pros**: [benefits]
- **Cons**: [drawbacks]
- **Why not**: [specific reason this was rejected]
### Alternative 2: [Name]
- **Pros**: [benefits]
- **Cons**: [drawbacks]
- **Why not**: [specific reason this was rejected]
## Consequences
What becomes easier or more difficult to do because of this change?
### Positive
- [benefit 1]
- [benefit 2]
### Negative
- [trade-off 1]
- [trade-off 2]
### Risks
- [risk and mitigation]
Workflow
Capturing a New ADR
When a decision moment is detected:
- Initialize (first time only) — if
docs/adr/does not exist, ask the user for confirmation before creating the directory, aREADME.mdseeded with the index table header (see ADR Index Format below), and a blanktemplate.mdfor manual use. Do not create files without explicit consent. - Identify the decision — extract the core architectural choice being made
- Gather context — what problem prompted this? What constraints exist?
- Document alternatives — what other options were considered? Why were they rejected?
- State consequences — what are the trade-offs? What becomes easier/harder?
- Assign a number — scan existing ADRs in
docs/adr/and increment - Confirm and write — present the draft ADR to the user for review. Only write to
docs/adr/NNNN-decision-title.mdafter explicit approval. If the user declines, discard the draft without writing any files. - Update the index — append to
docs/adr/README.md
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.
- 3d ago First seen · 180 lines · 55 tokens per session scan A be3ce03e4ad6
architecture-decision-records is a skill published in the GitHub repository hashgraph-online/awesome-codex-plugins (947 stars, last pushed yesterday), licensed Apache-2.0. It adds 55 tokens to every session and 1,633 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to architecture-decision-records, differing in 28 lines, and is treated as a copy.
Other skills, from other repositories
search
Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.
sprr
Single PR reviewer for awesome-quant. Use when the user asks to review, validate, comment on, label, close, or merge one specific pull request that adds README.md entries. Triggers include "sprr", "review PR", "check PR", and "validate contribution".
bprr
Bulk PR reviewer for awesome-quant. Use when the user asks to review all open PRs, review unreviewed PRs, bulk review, or mentions "bprr". Reviews open PRs lacking the reviewed label and presents a summary before any merge/comment/label action.
drawio-reconstruction
Reconstructs reference images into high-fidelity, editable Draw.io files with rendered previews: native Draw.io elements carry text and structure, SVG covers simple icons that match the reference, and cropped or transparent PNGs preserve complex visuals. Use when the user wants a diagram image, research figure…
benchmark-paper-template
Structures Benchmark and Evaluation papers using the five-pillar framework (Research Gap, Construction Pipeline, Evaluation Framework, Empirical Findings, optional Companion Method). Returns a completeness audit, a six-part Introduction logic chain, a Section 2-7 skeleton, and a pre-submission checklist. Use when…
reverse-engineering-android-malware-with-jadx
Reverse engineers malicious Android APK files using JADX decompiler to analyze Java/Kotlin source code, identify malicious functionality including data theft, C2 communication, privilege escalation, and overlay attacks. Examines manifest permissions, receivers, services, and native libraries. Activates for requests…