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.
git clone --depth 1 https://github.com/openshift-eng/ai-helpersWrote 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/commands/openshift-eng/ai-helpers/outcome-refinement)<a href="https://agentmods.dev/commands/openshift-eng/ai-helpers/outcome-refinement"><img src="https://agentmods.dev/badge/commands/openshift-eng/ai-helpers/outcome-refinement/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/commands/openshift-eng/ai-helpers/outcome-refinement"><img src="https://agentmods.dev/badge/commands/openshift-eng/ai-helpers/outcome-refinement.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 | $0.00016 | $0.00698 |
| Opus 5.5 | $0.00006 | $0.00279 |
| Sonnet 5.5 | $0.00003 | $0.00140 |
| Haiku 4.5 | $0.00002 | $0.00070 |
Grade A, and why
outcome-refinement 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- outcome-refinement — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Name
agendas:outcome-refinement
Synopsis
/agendas:outcome-refinement
Description
The agendas:outcome-refinement command helps analyze the outcome issues and should be used to assist in preparting for outcome refinement collaboration sessions. It automatically checks for common issues that we observe that indicate follow-up actions are needed by humans. This command generates structured outcome refinement meeting agenda.
Examples
/agendas:outcome-refinement
Implementation
The agendas:outcome-refinement command runs in three main phases:
🧩 Phase 1: Data Collection
- Queries JIRA for outcome issues in the OCPSTRAT project that require work to be done, meaning the issue status is not closed or release pending.
🧠 Phase 2: Analysis & Processing
- Flags routine hygiene issues to resolve with follow up actions.
- People assignments (Assignee, Architect, QA Contact, Doc Contact) should all be filled in.
- Outcome issues should only have Feature issue types as child issues.
- Identified if child issue are actively being worked on but the outcome doesn't represent the right status.
- Shows how long an outcome issue has been open and their corresponding priority.
- Identifies incomplete or unclear issues that need clarification.
- If and outcome issue has child issues that are actively being updated but the outcome has been open for a more than a year we should discuss the scope.
- If an outcome has stayed in the new status for over a year, we should probably discuss whether it's a real outcome priority.
- Looking at all the OCPSTRAT outcomes, if any specific component is commonly assigned to the child feature issues, this indicates an team overload, so we should discuss this.
- Highlights risks, dependencies, and recommended next actions.
📋 Phase 3: Report Generation
- Automatically generates a structured outcome refinement meeting agenda in Markdown format.
- Includes discussion points, decision checklists, and action items.
- Output can be copied directly into Confluence or shared with the team.
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 · 71 lines · 16 tokens per session scan A 40d7ea386c7b
outcome-refinement is a command published in the GitHub repository openshift-eng/ai-helpers (121 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 698 once invoked, about $0.0001 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 2026-08-30.
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