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 openshift-eng/ai-helpers --skill remediation-planninggit 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/skills/openshift-eng/ai-helpers/remediation-planning)<a href="https://agentmods.dev/skills/openshift-eng/ai-helpers/remediation-planning"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/remediation-planning/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-eng/ai-helpers/remediation-planning"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/remediation-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00023 | $0.05267 |
| Opus 5 | $0.00012 | $0.02634 |
| Sonnet 5 | $0.00005 | $0.01053 |
| Haiku 4.5 | $0.00002 | $0.00527 |
Grade A, and why
remediation-planning 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 7d 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:
- Remediation Planning — 88% identical, 24 lines differ
How it starts
The opening of the file, as written. The whole thing — 697 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remediation Planning
Creates actionable remediation plans for vulnerable Go codebases based on analysis results from previous phases.
Supported Remediation Types
- Dependency Update - Update Go package to fixed version
- Go Runtime Update - Update Go itself (for stdlib vulnerabilities)
- Configuration Change - Disable features, change settings, adjust security controls
- Code Refactoring - Replace vulnerable patterns, add validation, refactor code
- Security Patch - Apply vendor-provided patch files
- Workarounds - Temporary mitigations (timeouts, rate limiting, input validation)
- Infrastructure - Network policies, firewall rules, WAF configuration
- Combination - Multiple remediation types together
The skill automatically determines the appropriate remediation strategy based on CVE details and impact analysis.
When to Use This Skill
Use this skill when:
- Codebase is confirmed or likely affected by a CVE
- Need specific remediation steps beyond "update the package"
- Fixed version exists but need to assess compatibility
- No fixed version available and need workarounds
- Project uses custom build/test commands via Makefile
Implementation Steps
Step 1: Analyze Inputs and Determine Strategy
Required Inputs from Previous Phases:
From Phase 1 (cve-intelligence-gathering):
- CVE ID, severity, CVSS score
- Affected package/module and vulnerable version range
- Fixed version (if available)
- Vulnerability type and remediation guidance
From Phase 2 (codebase-impact-analysis):
- Risk level (HIGH / MEDIUM / LOW / NEEDS REVIEW)
- Current package version and dependency type
- Usage locations and functions being called
From Call Graph Analysis (optional):
- Reachability risk level and call chain
- Entry points
From govulncheck (optional):
- Detection result and vulnerable symbols
Decision Tree:
IF risk_level = "LOW":
→ Document findings, recommend monitoring and manual review
IF risk_level = "HIGH" or "MEDIUM":
IF fixed_version EXISTS:
IF affected_package is in go.mod → Dependency Update (Step 2)
ELSE IF affected_package is Go stdlib → Go Runtime Update (Step 2A)
IF fixed_version = null:
IF remediation_guidance has "configuration" → Configuration Change (Step 2B)
IF remediation_guidance has "code change" OR pattern vulnerability → Code Refactoring (Step 2C)
IF remediation_guidance has "patch" → Apply Patch (Step 2D)
ELSE → Workarounds (Step 3)
IF risk_level = "NEEDS_REVIEW":
→ Manual review + defensive workarounds (Step 3)
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.
- 7d ago First seen · 697 lines · 23 tokens per session scan A 9e11d1f62595
remediation-planning is a skill published in the GitHub repository openshift-eng/ai-helpers (116 stars, last pushed today), licensed Apache-2.0. It adds 23 tokens to every session and 5,267 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 2026-09-03.
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