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 agentmods add skills/bmbouter/redhat-agents/decision-recordnpx skills add bmbouter/redhat-agents --skill decision-recordgit clone --depth 1 https://github.com/bmbouter/redhat-agentsWhat 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 | $0.00025 | $0.00474 |
| Opus 5 | $0.00013 | $0.00237 |
| Sonnet 5 | $0.00005 | $0.00095 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
decision-record 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 2d 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.
What it actually says
When to Use
After any significant product decision — feature scoping, priority changes, architectural choices, trade-offs, scope cuts, or direction changes.
Instructions
1. Capture the decision
Ask the PM:
- What was decided? (one clear statement)
- What triggered this decision? (customer feedback, technical constraint, strategic shift)
- What alternatives were considered? (at least 2, ideally 3+)
- Why was this option chosen? (criteria, trade-offs)
- Who was involved? (decision maker, consulted, informed)
- What does this affect? (epics, teams, timelines, customers)
2. Draft the record
## Decision Record: [Decision Title]
**Date**: <date>
**Status**: Decided / Proposed / Superseded
**Decision maker**: [Name/role]
### Context
[What prompted this decision — the problem or question]
### Decision
[Clear statement of what was decided]
### Alternatives Considered
| Option | Pros | Cons |
|--------|------|------|
| **[Chosen option]** | ... | ... |
| [Alternative A] | ... | ... |
| [Alternative B] | ... | ... |
### Rationale
[Why the chosen option won — which criteria mattered most]
### Consequences
- **Epics affected**: [List with links]
- **Timeline impact**: [Acceleration, delay, or neutral]
- **Teams affected**: [Who needs to know]
- **Customer impact**: [Visible change, migration needed, or transparent]
### Stakeholders
| Role | Name | Status |
|------|------|--------|
| Decision maker | ... | Decided |
| Consulted | ... | Agreed / Disagreed |
| Informed | ... | Notified / Pending |
3. Store the record
Ask the PM where to put it:
- As a Jira comment on the relevant epic
- As a Confluence page
- As a local file in the repo
Present for review before posting.
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.
- 2d ago First seen · 70 lines · 25 tokens per session scan A 0afb3490d899
decision-record is a skill published in the GitHub repository bmbouter/redhat-agents (5 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 474 once invoked, about $0.0001 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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