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 cybernetix-lab/moss-harness --skill architecture-decision-recordsgit clone --depth 1 https://github.com/cybernetix-lab/moss-harnessWrote 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/cybernetix-lab/moss-harness/architecture-decision-records)<a href="https://agentmods.dev/skills/cybernetix-lab/moss-harness/architecture-decision-records"><img src="https://agentmods.dev/badge/skills/cybernetix-lab/moss-harness/architecture-decision-records/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/cybernetix-lab/moss-harness/architecture-decision-records"><img src="https://agentmods.dev/badge/skills/cybernetix-lab/moss-harness/architecture-decision-records.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 | $0.00055 | $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 11d 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 ships with it
1 file 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.
- 11d ago First seen · 180 lines · 55 tokens per session scan A be3ce03e4ad6
architecture-decision-records is a skill published in the GitHub repository cybernetix-lab/moss-harness (135 stars, last pushed 3mo ago), licensed MIT. 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.
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