Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add prajapatimehul/claude-aws-cost-saver/plugin install aws-cost-saverWrote 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/prajapatimehul/claude-aws-cost-saver/reviewing-findings)<a href="https://agentmods.dev/skills/prajapatimehul/claude-aws-cost-saver/reviewing-findings"><img src="https://agentmods.dev/badge/skills/prajapatimehul/claude-aws-cost-saver/reviewing-findings/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/prajapatimehul/claude-aws-cost-saver/reviewing-findings"><img src="https://agentmods.dev/badge/skills/prajapatimehul/claude-aws-cost-saver/reviewing-findings.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 | $0.00075 | $0.01947 |
| Opus 5 | $0.00037 | $0.00974 |
| Sonnet 5 | $0.00015 | $0.00389 |
| Haiku 4.5 | $0.00007 | $0.00195 |
Grade A, and why
reviewing-findings scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reviewing AWS Cost Findings
Multi-perspective review of cost optimization findings with confidence-based filtering.
Quick Start
# Review findings and update findings.json in place
python3 "${CLAUDE_PLUGIN_ROOT}/skills/reviewing-findings/scripts/review_findings.py" findings.json --profile your-profile
# Re-review findings that already carry a review_status
python3 "${CLAUDE_PLUGIN_ROOT}/skills/reviewing-findings/scripts/review_findings.py" findings.json --force
For the detailed per-check confidence matrices and false-positive patterns, read REVIEW_CRITERIA.md in this skill's directory.
Review Process
1. Pre-flight Checks
Skip review if:
- No findings.json exists
- Findings already reviewed (has
review_status) - Empty findings array
2. Multi-Agent Review (4 Parallel Agents)
Launch 4 independent review agents:
Agent #1: Resource Verification
├── Verify resource still exists
├── Check current utilization metrics
└── Confirm finding is still valid
Agent #2: Recommendation Quality
├── Validate recommendation is actionable
├── Check for edge cases (ASG, DR, scheduled)
└── Verify savings calculation logic
Agent #3: Business Context
├── Identify environment (prod/dev/staging)
├── Check for dependencies
└── Flag potential risks
Agent #4: Historical Pattern
├── Check for burst patterns
├── Identify seasonal usage
└── Review recent changes
3. Confidence Scoring
Each agent assigns confidence (0-100):
| Score | Meaning |
|---|---|
| 90-100 | Definite savings - act immediately |
| 70-89 | High confidence - safe to implement |
| 50-69 | Medium confidence - needs validation |
| 25-49 | Low confidence - likely false positive |
| 0-24 | Skip - insufficient evidence |
Filter threshold: 50 (adjustable)
4. Update Findings
Add review metadata to each finding:
{
"check_id": "EC2-001",
"monthly_savings": 150.00,
"review_status": {
"reviewed_at": "2026-01-19T15:00:00Z",
"final_confidence": 85,
"agents": {
"resource_verification": 90,
"recommendation_quality": 80,
"business_context": 85,
"historical_pattern": 85
},
"action": "approved",
"notes": "Resource verified idle for 21 days"
}
}
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 296 lines · 75 tokens per session scan A 37287de2352d
reviewing-findings is a skill published in the GitHub repository prajapatimehul/claude-aws-cost-saver (22 stars, last pushed 2mo ago), licensed MIT. It adds 75 tokens to every session and 1,947 once invoked, about $0.0004 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 2026-08-30.
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