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/impact-assessmentnpx skills add bmbouter/redhat-agents --skill impact-assessmentgit clone --depth 1 https://github.com/bmbouter/redhat-agentsWrote 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/bmbouter/redhat-agents/impact-assessment)<a href="https://agentmods.dev/skills/bmbouter/redhat-agents/impact-assessment"><img src="https://agentmods.dev/badge/skills/bmbouter/redhat-agents/impact-assessment.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.00464 |
| Opus 5 | $0.00013 | $0.00232 |
| Sonnet 5 | $0.00005 | $0.00093 |
| Haiku 4.5 | $0.00003 | $0.00046 |
Grade A, and why
impact-assessment 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 5d 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
When the PO needs to understand the impact of an issue before prioritizing it, especially for bugs and escalations.
Instructions
Read local/jira-workflow.md to understand priority levels and blocking conventions.
1. Gather context
Read the full ticket. Also query:
- Related issues: Issues linked to this one, or under the same epic
- Recent similar issues: Search for recently closed issues in the same area
- Blocking impact: Is anything currently blocked by this issue?
2. Assess dimensions
Evaluate across three dimensions:
Severity — How bad is the impact?
- Critical: Data loss, security issue, or complete feature failure
- High: Major feature degraded, workaround exists but is painful
- Medium: Feature partially broken, reasonable workaround exists
- Low: Cosmetic, minor inconvenience, edge case
Urgency — How soon must it be fixed?
- Immediate: Actively causing problems in production
- Soon: Will cause problems if not addressed this iteration
- Normal: Should be planned but no immediate pressure
- Low: Can wait for a natural opportunity
Effort — How much work is it? (if estimable from the description)
- Large: Multiple days, cross-cutting changes
- Medium: A day or two, contained scope
- Small: Hours, single-area change
- Unknown: Not enough information to estimate
3. Present assessment
## Impact Assessment: ISSUE-123
**Severity**: High — user-facing feature is degraded
**Urgency**: Soon — reported by 3 users this week
**Effort**: Medium — likely a single-service fix based on error description
**Context**:
- Related to EPIC-456 (authentication improvements)
- Similar to ISSUE-100 (closed 2 weeks ago, different root cause)
- Currently blocking: ISSUE-789
**Recommended priority**: Major
**Recommended action**: Add to current iteration, assign to auth team
The PO decides the final priority and action.
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.
- 5d ago First seen · 63 lines · 26 tokens per session scan A 9af282aca34e
impact-assessment is a skill published in the GitHub repository bmbouter/redhat-agents (5 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 464 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…