Borrowing it
Nothing to install: this file belongs to openshift/ols. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/openshift/ols/main/.claude/skills/estimate-risk/SKILL.mdgit clone --depth 1 https://github.com/openshift/olsWrote 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/openshift/ols/estimate-risk)<a href="https://agentmods.dev/skills/openshift/ols/estimate-risk"><img src="https://agentmods.dev/badge/skills/openshift/ols/estimate-risk/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/openshift/ols/estimate-risk"><img src="https://agentmods.dev/badge/skills/openshift/ols/estimate-risk.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 | $0.00060 | $0.00949 |
| Opus 5.5 | $0.00024 | $0.00380 |
| Sonnet 5.5 | $0.00012 | $0.00190 |
| Haiku 4.5 | $0.00006 | $0.00095 |
Grade A, and why
estimate-risk 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 2mo 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assess Risk Level for OLS Stories
Overview
Assess the risk level for one or more OLS Jira stories using the team's risk rubric. After assessing, set the Risk Score field and add a risk assessment comment.
Usage
/estimate-risk OLS-1234
/estimate-risk OLS-1234 OLS-1235 OLS-1236
Also invoked automatically after creating a new OLS story. Works for Stories, Bugs, Tasks, Weaknesses, and Vulnerabilities.
Rubric Location
Read the full rubric from the OLS repo root: risk-level-rubric.md
You MUST read this file before assessing. It contains:
- Risk level definitions (1, 2, 3) with customer impact and review requirements
- Classification examples by change type
- Decision tree for determining risk level
- Edge cases (cross-repo, CVEs, spikes, feature flags)
Workflow
Step 1: Read the rubric
Read risk-level-rubric.md
Step 2: Parse story keys from arguments
Extract all OLS-XXXX keys from the skill arguments. If no arguments
provided, ask the user for story key(s).
Step 3: For each story
3a. Fetch the story from Jira
Use mcp__atlassian__getJiraIssue with:
cloudId:redhat.atlassian.netissueIdOrKey: the story keyfields:["summary", "description", "components", "labels", "issuetype", "customfield_10976"]responseContentFormat:markdown
Extract: summary, description, components, labels, current Risk Score value.
If Risk Score is already set, tell the user the current value and ask whether to re-assess or skip.
3b. Apply the rubric
Using the rubric you read in Step 1:
- Read the summary and description — identify what kind of change this is
- Walk the decision tree:
- External contract change? → Risk 3
- User-visible behavior change? → Risk 3
- Internal logic change? → Risk 2
- Mechanical/cosmetic change? → Risk 1
- Check classification examples — match the change type to the table
- Check edge cases — cross-repo, CVE, spike, feature flag
- When in doubt, bias UP — Risk 2 → Risk 3 is safer than the reverse
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.
- 2mo ago Changed · +1 lines 09fa1ad5a126
- 2mo ago First seen · 112 lines · 60 tokens per session scan A 50953a35061f
estimate-risk is a skill published in the GitHub repository openshift/ols (3 stars, last pushed 3d ago), licensed Apache-2.0. It adds 60 tokens to every session and 949 once invoked, about $0.0002 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.
estimate-risk compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| estimate-riskopenshift/ols | Skill | 3 | 3d ago | Apache-2.0 | A | 60 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |