Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/rjmurillo/ai-agentsnpx agentmods add skills/rjmurillo/ai-agents/adr-generatorWrote 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/rjmurillo/ai-agents/adr-generator)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/adr-generator"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/adr-generator/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/rjmurillo/ai-agents/adr-generator"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/adr-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 16 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00170 | $0.03854 |
| Opus 5.5 | $0.00068 | $0.01542 |
| Sonnet 5.5 | $0.00034 | $0.00771 |
| Haiku 4.5 | $0.00017 | $0.00385 |
Grade A, and why
adr-generator 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 4d 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 — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADR Generator
Create well-structured Architectural Decision Records that document technical decisions with clear context, rationale, consequences, and alternatives.
Triggers
| Trigger Phrase | Operation |
|---|---|
write an ADR |
Full ADR generation workflow |
write an architecture decision record |
Full ADR generation workflow |
create an ADR |
Full ADR generation workflow |
generate ADR for |
Full ADR generation workflow |
document these design choices |
Capture rationale for durable architecture or design decisions |
Quick Start
# These all work:
write an ADR for the event model
write an architecture decision record for database selection
create an ADR for database selection
new ADR for authentication strategy
document this architecture decision about event sourcing
generate ADR for switching from REST to gRPC
document these design choices for future us to revisit as models change
record why we chose this instruction-file structure for future maintainers
When to Use
| Situation | Use This Skill? |
|---|---|
| New architectural decision needs documenting | Yes |
| Durable design choices or rationale need capturing for future readers | Yes |
| Changing an existing system or pattern | Yes (includes Prior Art Investigation) |
| Reviewing or validating an existing ADR | No, use adr-review skill |
| Minor implementation detail, not architectural | No |
Process
Phase G1: Gather
Collect required information from the user:
- Decision Title: Clear, concise name
- Context: Problem statement, technical constraints, business requirements
- Decision: Chosen solution with rationale
- Alternatives: Options considered (at least 2) and rejection reasons
- Stakeholders: People or teams involved
If any required information is missing, ask the user before proceeding.
Significance check: Before proceeding, quickly assess whether the decision warrants an ADR using the ASR Test. If the decision is trivially reversible, purely local, and has no stakeholder concern, suggest skipping the ADR.
What ships with it
5 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.
- 4d ago Changed · +21 lines 39c32e01c23e
- 15d ago Changed · +1 lines c83155110f8b
- 22d ago Changed · +5 lines a2efa6a2bde9
- 24d ago Changed · +2 tokens per session ac6ffb8311d6
- 25d ago Changed · +46 lines f171bdfcfa13
- 1mo ago First seen · 252 lines · 168 tokens per session scan A 24f61b48fb96
adr-generator is a skill published in the GitHub repository rjmurillo/ai-agents (48 stars, last pushed today), licensed MIT. It adds 170 tokens to every session and 3,854 once invoked, about $0.0007 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 2026-09-03.
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