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Official agent skill

Remediating With AWS Security Agent

by aws in aws/agent-toolkit-for-aws

Pull AWS Security Agent findings (penetration tests and code reviews) and drive remediation.

OfficialApache-2.0Auto-check passedSecurity

Install Remediating With AWS Security Agent

skills CLI
$ npx skills add aws/agent-toolkit-for-aws --skill remediating-with-aws-security-agent -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aws/agent-toolkit-for-aws remediating-with-aws-security-agent --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/aws-agents-for-devsecops/skills/remediating-with-aws-security-agent .claude/skills/remediating-with-aws-security-agent && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
remediating-with-aws-security-agent
GitHub stars
2.8k
Token cost
~2.9k tokens
SKILL.md length
1,302 words
Files
1
Skills in repo
138
Repo updated
First seen
Licence
Apache-2.0

At a glance

Pull AWS Security Agent findings (penetration tests and code reviews) and drive remediation.

  • Works in 4 steps: Discover scans (live, read-only) → Export findings to .security-agent/… → Triage into a prioritized plan → …
  • Mentions Security Agent
  • SKILL.md covers Why the ordering and the…, Stage 1: Discover scans (live,…, Stage 2: Export findings to… and Stage 3: Triage into a…, plus 2 more sections
  • Calls aws and git

What it does

Remediating With AWS Security Agent is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Pull AWS Security Agent findings (penetration tests and code reviews) and drive remediation. Use this whenever the user mentions Security Agent, security findings, pentest or penetration test results, code review findings, vulnerabilities found in their AWS account, "what did the security scan find", remediating or triaging security risks, or wants to start fixing reported vulnerabilities — even if they don't name the service explicitly. Trigger it for phrases like "get my security findings", "what…

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Security, covering Cloud security, Code review and Penetration testing. It works with Amazon Web Services and Git. The repository describes itself as: Official, AWS-supported MCP servers, skills, and plugins to help AI agents build on AWS. The licence is Apache-2.0.

When your agent uses it

  • Mentions Security Agent
  • Security findings
  • Penetration test results
  • Code review findings

Example prompts

  • “what did the security scan find”
  • “t name the service explicitly. Trigger it for phrases like”
  • “what vulnerabilities do we have”
  • “/remediating-with-aws-security-agent”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Discover scans (live, read-only)
  2. Export findings to .security-agent/ (gitignored)
  3. Triage into a prioritized plan
  4. Offer to remediate

What it can do on your machine

Read from SKILL.md and the folder at commit df2ab44. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • aws
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use aws and git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Remediating With AWS Security Agent loads about 2.9k tokens when it runs. Until then it costs about 213 tokens; SKILL.md has 1,302 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~213
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aws/agent-toolkit-for-aws at commit df2ab44, republished under its Apache-2.0 licence (© aws). 1,302 words, ~2,897 tokens.

Download SKILL.mdSave it as .claude/skills/remediating-with-aws-security-agent/SKILL.md (or your agent's skills folder).
name
remediating-with-aws-security-agent
description
Pull AWS Security Agent findings (penetration tests and code reviews) and drive remediation. Use this whenever the user mentions Security Agent, security findings, pentest or penetration test results, code review findings, vulnerabilities found in their AWS account, "what did the security scan find", remediating or triaging security risks, or wants to start fixing reported vulnerabilities — even if they don't name the service explicitly. Trigger it for phrases like "get my security findings", "what vulnerabilities do we have", "let's fix the pentest results", or "triage the security report". The skill discovers scans, exports findings to a gitignored local directory (so sensitive exploit detail is never committed), produces a prioritized triage summary, and offers to start fixing the highest-risk issues.

Security Agent Remediation

AWS Security Agent is a frontier agent that runs on-demand penetration tests and code reviews against a customer's applications and reports verified security risks. This skill takes you from "I have findings somewhere in AWS" to "I'm actively fixing the most important ones," while keeping the sensitive exploit detail out of source control.

The flow has four stages, and they matter in order:

  1. Discover which scans exist and how the account is configured (live, read-only).
  2. Export the findings to a local gitignored directory.
  3. Triage the findings into a prioritized, human-readable plan.
  4. Remediate by offering to fix the highest-risk issues.

Why the ordering and the guardrails matter

Findings contain working attack scripts, reproduction steps, file paths, and sometimes leaked secrets or environment details. If that lands in a Git repo, a customer can accidentally commit and publish a step-by-step exploit for their own production system. So the non-negotiable rule is: findings are written only to .security-agent/, and that path is gitignored before anything is written.

Stage 1: Discover scans (live, read-only)

Find out what the account has. All commands are read-only list-* operations.

AWS Security Agent organizes data as a hierarchy — work down it:

Application (account + Region)
└── Agent Space        (workspace for design review, code review, and pentests)
    ├── Penetration test → Pentest job → Findings
    └── Code review      → Code review job → Findings

Run these to orient yourself and show the user what exists:

bash
aws securityagent list-agent-spaces
aws securityagent list-pentests          --agent-space-id <as-...>
aws securityagent list-code-reviews      --agent-space-id <as-...>
aws securityagent list-pentest-jobs-for-pentest         --agent-space-id <as-...> --pentest-id <pt-...>
aws securityagent list-code-review-jobs-for-code-review --agent-space-id <as-...> --code-review-id <cr-...>

Job status is one of IN_PROGRESS, STOPPING, STOPPED, FAILED, COMPLETED. Only COMPLETED jobs have a stable, full set of findings.

Match the codebase to a scan, then confirm

Agent spaces, pentests, and code reviews are named after the application they target. Before asking the user to pick from a raw list, make an informed guess about which scan corresponds to this repository — the user is working in a codebase for a reason, and the relevant findings are almost always for the app in front of them.

Infer the app identity from the workspace using cheap, high-signal sources:

  • The repository / root directory name and the Git remote URL (git remote -v).
  • Project manifests and their name/description (package.json, pyproject.toml, *.csproj, go.mod, Cargo.toml).
  • README titles, product/steering docs, and any obvious product or company name.
  • Distinctive frameworks or domains that match a scan title.

Compare those signals against the agent space / scan names (case-insensitive, allow partial and fuzzy matches). Then always confirm before exporting — present your best guess and your reasoning, and let the user correct it:

"This repo looks like <product> (from <signal>), which matches the <name> agent space. Use that, or pick another? [Other Agent Space names, ...]"

If nothing matches with reasonable confidence, say so plainly and show the full list rather than forcing a wrong guess. Never export from a guessed scan without the user's confirmation.

Stage 2: Export findings to .security-agent/ (gitignored)

Pull findings using AWS CLI commands. Write everything into .security-agent/ in the repo — never to chat or stdout — because findings include working attack scripts, reproduction steps, and sometimes leaked secrets.

1. Lock down the output directory before pulling anything
bash
mkdir -p .security-agent
echo '*' > .security-agent/.gitignore
2. Resolve the latest COMPLETED job

You should already have the agentSpaceId and the pentest/code-review id from Stage 1. List jobs for the chosen scan:

bash
# Pentest jobs:
aws securityagent list-pentest-jobs-for-pentest \
  --agent-space-id <as-...> --pentest-id <pt-...>

# Code review jobs:
aws securityagent list-code-review-jobs-for-code-review \
  --agent-space-id <as-...> --code-review-id <cr-...>

Paginate by passing --next-token from the previous response until absent. Filter the job summaries to status == "COMPLETED". If none are COMPLETED, stop and tell the user "No completed jobs found. Please wait for a job to complete or check job statuses." Otherwise, pick the COMPLETED job with the greatest createdAt timestamp.

3. List finding summaries and filter by confidence
bash
# Pentest findings:
aws securityagent list-findings \
  --agent-space-id <as-...> --pentest-job-id <pj-...>

# Code review findings:
aws securityagent list-findings \
  --agent-space-id <as-...> --code-review-job-id <cj-...>

Paginate on --next-token until exhausted. Confidence values from weakest to strongest: FALSE_POSITIVE, UNCONFIRMED, LOW, MEDIUM, HIGH. Keep only HIGH and MEDIUM by default. Widen only when the user explicitly asks.

4. Fetch full detail in batches of 25

batch-get-findings accepts at most 25 ids per call. Chunk the filtered finding ids into groups of 25:

bash
aws securityagent batch-get-findings \
  --agent-space-id <as-...> \
  --finding-ids <fid-1> <fid-2> ... <fid-25>

Tag each returned finding with its source (pentest or code-review) before writing, so triage in Stage 3 can tell them apart.

5. Write findings into .security-agent/

Group findings by job id. For each job, write a full markdown report to .security-agent/findings_<jobId>.md with ALL fields returned by the API (findingId, name, description, riskLevel, riskType, confidence, status, codeLocations, remediationCode, and any other fields). Do not leave off any fields.

Edge cases
  • No agent space, scan, or COMPLETED job — stop and surface that to the user rather than retrying.
  • Credentials or service unavailable — confirm with aws sts get-caller-identity and check the Region (default us-east-1; Security Agent is regional).
  • Don't paste finding contents into chat beyond short titles and counts. The detail belongs in the gitignored files.

Stage 3: Triage into a prioritized plan

Rank by risk, because remediation time is finite and a CRITICAL unauthenticated RCE outranks a LOW informational finding every time. Read the exported findings_*.md files from .security-agent/ and sort them deterministically.

Show full SKILL.md (520 more words)Show less
Ranking rules

Sort ascending by this composite key (lower wins, i.e. more urgent first):

  1. Risk level, in this order: CRITICAL (0) → HIGH (1) → MEDIUM (2) → LOW (3) → INFORMATIONAL (4) → UNKNOWN / missing (5).
  2. Risk score, highest first. riskScore is a numeric string on pentest findings (e.g. "10.0"), often absent on code-review findings — treat missing as the lowest possible score so it sorts after scored findings of the same level.
  3. Confidence, in this order: HIGH (0) → MEDIUM (1) → LOW (2) → UNCONFIRMED (3) → FALSE_POSITIVE (4).

Also compute a severity-count summary across all findings (e.g. 2 CRITICAL · 5 HIGH · 3 MEDIUM) for the header of the report.

Pulling the code location

For each finding, derive a single short location string:

  • If filePath is set, use it as-is.
  • Otherwise, take codeLocations[0]. Strip the scanner's sandbox prefix from filePath (everything up to and including that marker) so the path is repo-relative; if that marker isn't present, fall back to the basename. Append :<lineStart> when present.
  • If neither is available (typical for some pentest findings), leave it blank and describe the affected endpoint or attack chain in the impact line instead.
Summary format

Write a compact summary for the user:

## Security Agent triage — <agent space name>

<N> findings exported (<P pentest, C code review>) · confidence: <levels> · severity: <counts>

### Priority order
1. [CRITICAL · score 10.0 · HIGH confidence] <finding name>
   - Type: <riskType> · Source: <pentest|code-review>
   - Where: <file:line or endpoint, if present>
   - Impact: <one-line plain-language summary>
2. [HIGH · ...] ...

### Recommended remediation order
<short rationale: which to fix first and why — e.g. "1 and 3 are both
unauthenticated RCE on internet-facing endpoints; fix those before the
stored-XSS issues.">

If more than ~10 findings, show the top N in detail and summarize the rest as a count by severity at the bottom.

What to keep out of chat

The full description, reasoning, and attackScript stay in the gitignored files — they contain working exploit detail. In the chat summary keep impact lines to one line each, in plain language. Code-review findings usually carry a filePath/location and a suggestedFix; call those out since they map directly to repo changes. Pentest findings describe endpoints and attack chains; map them to the responsible code where you can. Look for findings that corroborate each other (a pentest and a code review flagging the same root cause) — those are strong signals for what to fix first.

Stage 4: Offer to remediate

After presenting the triage, offer to start fixing — don't silently begin editing code.

Ask the user something like: "Want me to start fixing the top finding(s)? I'd recommend starting with #1 (<name>)." If they agree, work top-down by priority:

  1. Read the finding detail from the gitignored export file (location, description, suggested fix).
  2. Open the affected file and apply the fix via the editor.
  3. Report one line per fix: "Fixed {name} in {filePath}:{lineStart}."

If the user wants to handle several findings, fix one at a time (or one cluster of related findings) so each change stays reviewable, and proceed in the priority order from Stage 3.

Notes and edge cases

  • No completed jobs: a scan may still be IN_PROGRESS. Tell the user; offer to re-check later rather than exporting a partial job.
  • Re-running: each run overwrites the files for that job id. The directory is safe to delete; it only holds exported copies, not source-of-truth data.
  • Multiple accounts/Regions: findings are Region-scoped. If the user expected results and got none, confirm the region matches where Security Agent is configured.
  • Data handling: treat exported findings as sensitive. They are copies of verified exploits against the user's own systems.

© aws, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/aws-agents-for-devsecops/skills/remediating-with-aws-security-agent of aws/agent-toolkit-for-aws.

Open the folder on GitHubat commit df2ab44

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Trailmark Review Gatetrailofbits/skills7.4k—~1.1kAutomated safety check: NotesCC-BY-SA-4.0
Differential Security Reviewtrailofbits/skills7.4k—~1.8kAutomated safety check: NotesCC-BY-SA-4.0
Security ReviewerAratKruglik/claude-laravel1551 repos~1.1kAutomated safety check: NotesNone
Code Review with Beads Tasksmaslennikov-ig/claude-code-orchestrator-kit260—~2kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Remediating With AWS Security Agent

What does Remediating With AWS Security Agent do?

Pull AWS Security Agent findings (penetration tests and code reviews) and drive remediation. Remediating With AWS Security Agent is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Pull AWS Security Agent findings (penetration tests and code reviews) and drive remediation.

When should I use Remediating With AWS Security Agent?

Remediating With AWS Security Agent fits situations like: mentions Security Agent; security findings; penetration test results; code review findings.

How do I install Remediating With AWS Security Agent in Claude Code?

Run `npx skills add aws/agent-toolkit-for-aws --skill remediating-with-aws-security-agent -a claude-code`. Or copy the skill folder (plugins/aws-agents-for-devsecops/skills/remediating-with-aws-security-agent in aws/agent-toolkit-for-aws) into .claude/skills/remediating-with-aws-security-agent in your project. Claude Code loads it when a task matches its description.

How do I install Remediating With AWS Security Agent in Codex?

Run `npx skills add aws/agent-toolkit-for-aws --skill remediating-with-aws-security-agent -a codex`. Or copy the skill folder (plugins/aws-agents-for-devsecops/skills/remediating-with-aws-security-agent in aws/agent-toolkit-for-aws) into .agents/skills/remediating-with-aws-security-agent in your project. Codex loads it when a task matches its description.

Can I use Remediating With AWS Security Agent in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aws/agent-toolkit-for-aws --skill remediating-with-aws-security-agent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/remediating-with-aws-security-agent, .gemini/skills/remediating-with-aws-security-agent, .github/skills/remediating-with-aws-security-agent and .opencode/skills/remediating-with-aws-security-agent in your project.

What does Remediating With AWS Security Agent need to run?

Going by SKILL.md and its folder, Remediating With AWS Security Agent needs the command-line tools its instructions call (aws and git).

Does Remediating With AWS Security Agent access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Remediating With AWS Security Agent safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Remediating With AWS Security Agent use?

Remediating With AWS Security Agent is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Remediating With AWS Security Agent use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Remediating With AWS Security Agent?

Skills that share tags, products or a category with Remediating With AWS Security Agent: Trailmark Graph Evolution (trailofbits/skills, 7.4k stars), Trailmark Review Gate (trailofbits/skills, 7.4k stars), Differential Security Review (trailofbits/skills, 7.4k stars) and Security Reviewer (AratKruglik/claude-laravel, 155 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Remediating With AWS Security Agent?

aws (a GitHub organization, an official publisher) maintains it in aws/agent-toolkit-for-aws, which has 2,830 GitHub stars. The repository holds 138 skills in this directory. The repository was last updated on October 9, 2026.

Source: aws/agent-toolkit-for-aws on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.