remediation-planning

remediation-planning is a skill for Claude Code from openshift-eng/ai-helpers. It costs 23 tokens per session (5,267 once invoked), scanned A, original, Apache-2.0.

A planning skill for fixing known or suspected security vulnerabilities in Go codebases. It turns vulnerability details into possible dependency updates, code changes, configuration changes, patches, or temporary workarounds.

In plain words
What is it for?
Use it to create remediation steps for CVEs, assess fixed-version compatibility, plan mitigations, and include project-specific build and test checks.
Why use it?
It helps teams decide what to change when simply updating a package is not enough or no fixed version exists.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the compliance plugin — 4 skills, 1 command shipped together

Good fit Use it to create remediation steps for CVEs, assess fixed-version compatibility, plan mitigations, and include project-specific build and test checks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/openshift-eng/ai-helpers/remediation-planning
Install

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.

Any agent
npx skills add openshift-eng/ai-helpers --skill remediation-planning
Clone the repo
git clone --depth 1 https://github.com/openshift-eng/ai-helpers

Made for: Claude Code.

Or install compliance, the plugin that ships this one along with the rest of its 4 skills, 1 command.

Wrote 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.

agentmods badge for remediation-planning

README.md
[![agentmods](https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/remediation-planning/github.svg)](https://agentmods.dev/skills/openshift-eng/ai-helpers/remediation-planning)
Your own site
<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.

agentmods 80×15 button for remediation-planning

Your own site · 80×15
<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>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,267 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 7d ago against content hash 9e11d1f62595, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

plugins/compliance/skills/remediation-planning/SKILL.md · 697 lines

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

  1. Dependency Update - Update Go package to fixed version
  2. Go Runtime Update - Update Go itself (for stdlib vulnerabilities)
  3. Configuration Change - Disable features, change settings, adjust security controls
  4. Code Refactoring - Replace vulnerable patterns, add validation, refactor code
  5. Security Patch - Apply vendor-provided patch files
  6. Workarounds - Temporary mitigations (timeouts, rate limiting, input validation)
  7. Infrastructure - Network policies, firewall rules, WAF configuration
  8. 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)

Read the full file on GitHub · 697 lines

Changes

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.

  1. 7d ago First seen · 697 lines · 23 tokens per session scan A 9e11d1f62595

Subscribe to this mod's changes

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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