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 JayRHa/AgentSkills --skill adr-authorgit clone --depth 1 https://github.com/JayRHa/AgentSkillsWrote 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/jayrha/agentskills/adr-author)<a href="https://agentmods.dev/skills/jayrha/agentskills/adr-author"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/adr-author/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/jayrha/agentskills/adr-author"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/adr-author.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 | $0.00111 | $0.01728 |
| Opus 5.5 | $0.00044 | $0.00691 |
| Sonnet 5.5 | $0.00022 | $0.00346 |
| Haiku 4.5 | $0.00011 | $0.00173 |
Grade A, and why
adr-author 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 1mo 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADR Author
Overview
An Architecture Decision Record (ADR) is a short, immutable document that captures one architecturally significant decision: the forces at play (context), the realistic options, the option chosen, and the consequences that follow. ADRs form an append-only log so future maintainers can reconstruct why the system looks the way it does — not just what it does.
This skill produces rigorous ADRs that distinguish facts from opinions, force an honest options comparison, and make consequences (good, bad, and neutral) explicit.
Keywords: ADR, architecture decision record, design decision, technical decision log, MADR, Nygard, trade-off analysis, options matrix, decision rationale, superseded decision, build vs buy, framework selection, database choice, API style, architecture governance.
When a Decision Deserves an ADR
Write an ADR when the decision is architecturally significant — it is costly to reverse,
affects multiple teams/components, or constrains future choices. Use the checklist in
references/significance-checklist.md. If none of the criteria fire, capture it as a code
comment or a ticket instead, not an ADR.
Workflow
-
Confirm significance. Run the decision through
references/significance-checklist.md. If it does not qualify, tell the user and suggest a lighter-weight record. -
Assign an ID and locate the log. ADRs live in a directory such as
docs/adr/ordoc/architecture/decisions/. Number them sequentially with a zero-padded prefix:0001-use-postgresql.md,0002-adopt-rest-over-graphql.md. Usescripts/new_adr.pyto scan the directory, compute the next number, and scaffold the file. -
Gather context. Interview the user (or read the codebase) to extract the forces: business drivers, technical constraints, team skills, deadlines, existing systems, non-functional requirements (performance, security, cost, operability). Separate objective facts from assumptions — label assumptions explicitly.
What ships with it
4 files 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.
- 1mo ago First seen · 131 lines · 111 tokens per session scan A 45e13c41e3b3
adr-author is a skill published in the GitHub repository JayRHa/AgentSkills (12 stars, last pushed 2mo ago), licensed MIT. It adds 111 tokens to every session and 1,728 once invoked, about $0.0004 per session on Opus 5.5. 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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