Creator · ricmmartins
Last updated · Aug 25, 2026
finops-intelligence
Comprehensive FinOps analysis combining cost optimization, waste identification, and chargeback reporting. Use when asked about reducing Azure spend, finding unused resources, cost per team, chargeback, showback, cost anomalies, rightsizing, or monthly cost review.
Do not auto-install
Install targets
Codex install prompt
Install the "finops-intelligence" agent skill from https://github.com/ricmmartins/azure-sre-agent-skills/tree/main/skills/04-finops-intelligence. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Comprehensive FinOps analysis combining cost optimization, waste identification, and chargeback reporting. Use when asked about reducing Azure spend, finding unused resources, cost per team, chargeback, showback, cost anomalies, rightsizing, or monthly cost review. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"ricmmartins-finops-intelligence","task":"Install finops-intelligence","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence
Maintenance
fresh
Pushed today
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
70
65/100 Quality · 66/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
Trust, audit, and install readiness at a glance
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Human review before install
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
70 GitHub stars
Repo activity
70 stars, 14 forks
Maintenance
Pushed today
License
MIT
Install
npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence
Install safety
standard package or runtime install path
Permission surface
shell or command execution, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Review before production
- The skill does not explicitly state that all commands are read-only, though they are.
- Quality score needs review
- Permission surface needs review: shell or command execution, network or browser access
- GitHub adoption: 70 GitHub stars
Install readiness
Install path available
- Install path is available
- Repository evidence is available
- License is declared
- No Agent Proven outcome evidence yet
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
View technical data+
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Search sources
Suited agents
Install decision
- Command
- npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence
- Policy
- review
- Human review
- yes
Trust and risk
- Trust
- 58/100
- Audit
- 75/100
- Risk level
- Needs review
Outcome loop
- Endpoint
- /api/agent/outcome
- Event ID
- resolve
- Outcomes
- 5
Install command
npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligenceDo not use when
- teams that need a vendor-supported SLA
- production agents without a repository review
- The skill does not explicitly state that all commands are read-only, though they are.
- No OpenAgentSkill engagement data yet
- High-risk permission hints: Shell or command execution
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Agent safety v2
47/100 · Avoid automatic install
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Shell or command execution
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Database access
Skill may inspect schemas, query databases, or work with persistent stores.
- High-risk permission hints: Shell or command execution
- Dependency or permission surface needs review
Agent resolve plan
Let an agent verify fit before installing.
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20finops-intelligence%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20finops-intelligence%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/ricmmartins-finops-intelligence/install
Agent should check
- Task fit and alternatives from Resolve API.
- Audit score, trust score, and safety policy warnings.
- Install target compatibility for Codex, Claude Code, Cursor, or CLI.
Copy prompt
Task: Use finops-intelligence in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20finops-intelligence%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/ricmmartins-finops-intelligence/install
Install command: npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Give an agent the install path, not another directory page.
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/ricmmartins-finops-intelligence/install
LLM text format
/api/skills/ricmmartins-finops-intelligence/install?format=text
Find alternatives
/api/skills/search?q=finops-intelligence&limit=3
Agent prompt
Use finops-intelligence for this task. Review https://www.openagentskill.com/api/skills/ricmmartins-finops-intelligence/install, then install with: npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligenceRegistry metadata
Agent-readable profile for automatic skill selection.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/ricmmartins-finops-intelligence
LLM text
/api/registry/manifest/ricmmartins-finops-intelligence?format=text
Install alias
/api/registry/install/ricmmartins-finops-intelligence
Recommend
/api/registry/recommend?task=Use%20finops-intelligence%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
Needs review · 75/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
Install path
Command ready
Use when
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
Evidence
- recent repository activity
- install command or GitHub repo available
- 65/100 quality profile
review first
- The skill does not explicitly state that all commands are read-only, though they are.
- No OpenAgentSkill engagement data yet
Implementation path
- 1Install it in a sandbox agent and run one Research agents task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Trust profile
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub adoption
CHECK70 GitHub stars
Stars/forks activity
CHECK70 stars, 14 forks; issue activity unavailable in current metadata
Recent maintenance
PASSPushed today
License clarity
PASSMIT
Good signals
- AI review approved
- Install path is available
- Repository evidence is available
- Recently maintained repository
- Install command has no obvious high-risk pattern
- Outcome loop is ready but needs first real agent run
Review before install
- The skill does not explicitly state that all commands are read-only, though they are.
- Quality score needs review
- Permission surface needs review: shell or command execution, network or browser access
- GitHub adoption: 70 GitHub stars
- Stars/forks activity: 70 stars, 14 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, network or browser surface
- Permission surface: shell or command execution, network or browser access
- No real agent outcome reports yet
- Human review required before unattended installation
Recommended action
Choose a stronger alternative or inspect the source manually before any install attempt.
Quality profile
Promising candidate for agent workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Use this skill in these scenarios
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Work with data stores
Database and SQL
I need my agent to inspect database schemas, write SQL, and explain query results.
Workflow fit
Add it to a complete workflow
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
RAG knowledge base
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Compare before you install
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Overview
--- name: finops-intelligence description: Comprehensive FinOps analysis combining cost optimization, waste identification, and chargeback reporting. Use when asked about reducing Azure spend, finding unused resources, cost per team, chargeback, showback, cost anomalies, rightsizing, or monthly cost review. tools: - RunAzCliReadCommands - execute_kusto_query ---
# FinOps Intelligence
## Purpose Unified cost intelligence skill that identifies savings opportunities, tracks cost trends, and generates chargeback/showback reports by team or project. Answers both "where can we save?" and "who spent what?"
## When to use this skill - User asks "why did our bill go up?" - User asks for cost optimization or savings opportunities - User asks "how much did team X spend this month?" - User asks for chargeback, showback, or cost allocation report - Monthly proactive cost review or FinOps cadence
## Pre-check Confirm with the user: - **Scope**: Which subscriptions to scan (all or specific ones) - **Time range**: For trend analysis (default: last 3 months) - **Allocation model** (for chargeback): Which tag to use for cost splitting? - `cost-center` tag (most common) - `owner` or `team` tag - `application` or `project` tag - Resource group naming convention (e.g., `rg-teamname-*`) - **Exclusions**: Dev/test subscriptions, sandbox resource groups
## Analysis procedure
### Step 1: Cost trend overview Get the big picture using Azure Cost Management.
```bash # Current month cost by service (last 30 days) az costmanagement query --type ActualCost --timeframe MonthToDate \ --scope "subscriptions/<sub-id>" \ --dataset-aggregation '{"totalCost":{"name":"Cost","function":"Sum"}}' \ --dataset-grouping name="ServiceName" type="Dimension" \ -o table ```
```bash # Previous month for comparison az costmanagement query --type ActualCost --timeframe TheLastMonth \ --scope "subscriptions/<sub-id>" \ --dataset-aggregation '{"totalCost":{"name":"Cost","function":"Sum"}}' \ --dataset-grouping name="ServiceName" type="Dimension" \ -o table ```
If `az costmanagement` is unavailable, use the REST API: ```bash az rest --method post \ --url "https://management.azure.com/subscriptions/<sub-id>/providers/Microsoft.CostManagement/query?api-version=2023-11-01" \ --body '{"type":"ActualCost","timeframe":"MonthToDate","dataset":{"aggregation":{"totalCost":{"name":"Cost","function":"Sum"}},"grouping":[{"type":"Dimension","name":"ServiceName"}]}}' ```
Summarize: - Total spend this month vs. last month (% change) - Top 5 services by spend - Top 5 resource groups by spend - Any spending anomalies (day-over-day spikes > 20%)
### Step 2: Orphaned resources (waste) Find resources consuming cost with no active use.
| Check | Command | Savings signal | |-------|---------|----------------| | Unattached managed disks | `az disk list --query "[?managedBy==null]"` | Disk cost per month | | Unused public IPs | `az network public-ip list --query "[?ipConfiguration==null]"` | ~$3.65/month each | | Stopped but allocated VMs | `az vm list -d --query "[?powerState=='VM deallocated']"` | Disk + IP cost still billed | | Unused App Service plans | `az appservice plan list --query "[?numberOfSites==0]" --resource-group <rg>` | Full plan cost | | Old snapshots (>90 days) | `az snapshot list --query "[?timeCreated<'$(date -u -d '90 days ago' +%Y-%m-%dT%H:%M:%SZ)']"` | Storage cost | | Unused NAT Gateways | `az network nat gateway list` cross-ref with subnets | ~$32/month each | | Empty resource groups | `az group list` then check member count | Organizational waste |
Note on date handling: Use shell variable substitution for date comparisons: - Linux/macOS: `$(date -u -d '90 days ago' +%Y-%m-%dT%H:%M:%SZ)` - The SRE Agent sandbox runs Linux, so the above syntax is valid.
### Step 3: Rightsizing opportunities Identify over-provisioned resources.
1. **VMs with low CPU** (< 5% avg over 14 days): Query Azure Monitor metrics for `Percentage CPU` across all VMs 2. **VMs with low memory** (< 10% avg): Query Log Analytics for memory counters if available 3. **Over-provisioned App Service plans**: Check CPU and memory % across the plan — if consistently < 20%, suggest downgrade 4. **Over-provisioned databases**: Check DTU/vCore utilization — if < 20%, suggest lower tier
For each, calculate: - Current SKU and monthly cost - Recommended SKU and monthly cost - **Estimated monthly savings**
### Step 4: Reservation & savings plan opportunities 1. List VMs running 24/7 for > 30 days — candidates for Reserved Instances (up to 72% savings) 2. List databases running 24/7 — candidates for reserved capacity 3. Check if Azure Savings Plans could apply to compute spend
### Step 5: Storage optimization 1. Check storage accounts for access tier usage: ```bash az storage account list --query "[].{name:name, accessTier:accessTier, kind:kind}" -o table ``` 2. Identify blobs that haven't been accessed in 90+ days — candidates for Cool/Archive tier 3. Check for lifecycle management policies — suggest if missing 4. Check for redundancy over-provisioning (GRS when LRS would suffice for non-critical data)
### Step 6: Cost allocation (chargeback/showback) Group costs by the user's chosen allocation model.
**By tag** (preferred): - Group all resources by the chosen tag value - Aggregate costs per tag value - Track untagged resources separately as "Unallocated"
**By resource group** (fallback): - Parse resource group names for team/project identifiers - Group and aggregate accordingly
**Shared costs** (identify and handle): - Resources used by multiple teams (e.g., shared AKS cluster, shared networking) - Flag these separately — suggest allocation keys (even split, usage-based, or headcount-based)
For each team/project: - This period vs. previous period: $ change and % change - Top cost driver (which service drove the change?) - Flag anomalies (> 30% increase without known cause)
### Step 7: Efficiency metrics Calculate per-team efficiency indicators: - **Cost per resource**: Total spend / number of resources - **Compute waste ratio**: Cost of idle/underutilized resources / total compute cost - **Tag compliance**: % of team's resources properly tagged
## Accepted exceptions (optional)
If the user provides a list of accepted exceptions, do not flag those items. Instead, note them in the report as **Accepted Exception** with the reason provided.
Example format the user may provide:
| Check | Reason | |-------|--------| | 2.1 Unattached managed disks | Kept for disaster recovery snapshots, reviewed monthly | | 3.2 Over-provisioned App Service plans | Pre-scaled for upcoming product launch next week | | 4.1 Reserved instances | Short-term project, reservations not cost-effective |
When exceptions are provided: - Skip the flagged checks in scoring - List them in a separate "Accepted Exceptions" section at the end of the report - Recalculate the overall score excluding excepted checks
## Expected output
### Report header (mandatory — use this exact format)
## FinOps Cost Optimization & Chargeback Report
| Field | Value | |-------|-------| | Subscription | (name + ID) | | Report Date | YYYY-MM-DD | | Total Spend (MTD) | $X,XXX | | Projected Full Month | $X,XXX | | Month-over-month Change | +/-$X (+/-X%) | | Waste Identified (recoverable) | ~$X/month | | Resource Groups Tracked | N (M with spend) | | Tag Compliance | X% | | Issues Found | X Critical, Y High, Z Medium |
### Savings breakdown table
| Category | Finding | Current Cost/mo | Savings/mo | Priority | Action | |----------|---------|----------------|------------|----------|--------| | Orphaned | 3 unattached disks | $45 | $45 | High | Delete or snapshot+delete | | Rightsizing | 2 VMs at < 5% CPU | $380 | $190 | High | Resize D4s_v5 → B2ms | | Reservations | 5 VMs running 24/7 | $1,200 | $864 | Medium | 3yr RI | | Storage | No lifecycle policies | $200 | $80 | Medium | Add cool tier policy | | **TOTAL RECOVERABLE** | — | — | **~$X,XXX/mo** | — | — |
### Cost allocation table
| Team / Project | This Period | Last Period | Change | % Change | Top Service | % of Total | |----------------|------------|-------------|--------|----------|-------------|------------| | Platform | $5,200 | $4,800 | +$400 | +8.3% | Compute | 32% | | Product API | $3,800 | $3,100 | +$700 | +22.6% ⚠️ | Databases | 24% | | Unallocated | $1,300 | $800 | +$500 | +62.5% 🔴 | Mixed | 8% | | **Total** | **$16,000** | **$14,300** | **+$1,700** | **+11.9%** | | **100%** |
### Quick wins (implement today) Top 3 actions that save the most with the least effort.
### Requires planning Actions that need architecture review or stakeholder approval.
### Unallocated cost remediation List untagged resources with suggested owner and tagging command: ```bash az resource tag --ids <resource-id> --tags team=<team> cost-center=<cc> ```
### Remediation guidance For each cost finding, include in the output: 1. The specific `az` CLI command to remediate (suggest only — do not execute) 2. Use `GetAzCliHelp` to validate the command syntax before suggesting 3. The official Microsoft Learn documentation link
### References - Cost Management: https://learn.microsoft.com/en-us/azure/cost-management-billing/costs/overview-cost-management - Azure Advisor Cost: https://learn.microsoft.com/en-us/azure/advisor/advisor-cost-recommendations - Reserved Instances: https://learn.microsoft.com/en-us/azure/cost-management-billing/reservations/save-compute-costs-reservations - Orphaned Resources: https://learn.microsoft.com/en-us/azure/advisor/advisor-reference-cost-recommendations
## Sample output
> The following is a redacted example of what the report looks like when run against a subscription.
## FinOps Cost Optimization & Chargeback Report
| Field | Value | |-------|-------| | Subscription | contoso-prod-001 (a1b2c3d4-e5f6-7890-abcd-ef1234567890) | | Report Date | 2026-07-15 | | Total Spend (MTD) | $12,340 | | Projected Full Month | $16,450 | | Month-over-month Change | +$1,700 (+11.5%) | | Waste Identified (recoverable) | ~$1,179/month | | Resource Groups Tracked | 14 (12 with spend) | | Tag Compliance | 78% | | Issues Found | 2 Critical, 3 High, 4 Medium |
### Savings breakdown table
| Category | Finding | Current Cost/mo | Savings/mo | Priority | Action | |----------|---------|----------------|------------|----------|--------| | Orphaned | 3 unattached disks in `rg-app-prod` | $45 | $45 | High | Delete or snapshot+delete | | Orphaned | 2 unused public IPs | $7 | $7 | Medium | Delete | | Rightsizing | `vm-batch-01` at 3% CPU avg | $380 | $190 | High | Resize D4s_v5 → B2ms | | Rightsizing | `sql-staging` at 8% DTU | $250 | $125 | Medium | Downgrade S3 → S1 | | Reservations | 5 VMs running 24/7 for 90+ days | $1,200 | $864 | Medium | 3yr RI | | **TOTAL RECOVERABLE** | — | — | **~$1,179/mo** | — | — |
### Cost allocation table
| Team / Project | This Period | Last Period | Change | % Change | Top Service | % of Total | |----------------|------------|-------------|--------|----------|-------------|------------| | Platform | $5,200 | $4,800 | +$400 | +8.3% | Compute | 32% | | Product API | $3,800 | $3,100 | +$700 | +22.6% ⚠️ | Databases | 24% | | Data Team | $2,700 | $2,600 | +$100 | +3.8% | Storage | 17% | | Unallocated | $1,300 | $800 | +$500 | +62.5% 🔴 | Mixed | 8% | | **Total** | **$16,450** | **$14,750** | **+$1,700** | **+11.5%** | | **100%** |
### Remediation guidance (sample)
```bash # Delete unattached managed disks az disk delete --name disk-old-backup-01 --resource-group rg-app-prod --yes
# Resize over-provisioned VM az vm resize --name vm-batch-01 --resource-group rg-batch-prod --size Standard_B2ms
# Tag unallocated resources az resource tag --ids /subscriptions/.../resourceGroups/rg-shared/providers/Microsoft.Storage/storageAccounts/stcontososhared --tags team=platform cost-center=CC-1234 ```
Technical details
- Version
- 1.0.0
- License
- MIT
- Last updated
- Aug 25, 2026
- Published
- Aug 25, 2026
Decision snapshot
Fallback candidate
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 74/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- Success rate
- —
- Recent failure
- —
- Outcomes
- 0
- Output quality
- —
- Failed
- 0
- Not relevant
- 0
- Installs
- 0
- Risk blocked
- 0
- Setup needed
- 0
- Production
- 0
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Add to agent workflow
Free and open source. Review the report before installing into production agents.
Growth loop
Share kit
Scenario-led draft for finops-intelligence, ready for a manual X post.
finops-intelligence: Comprehensive FinOps analysis combining cost optimization, waste identification, and chargeba... 70 stars https://www.openagentskill.com/skills/ricmmartins-finops-intelligence?ref=x
Optional reply with install command
Listing + install path for finops-intelligence: https://www.openagentskill.com/skills/ricmmartins-finops-intelligence?ref=x Install: npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence
Listing source
Registry indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- ricmmartins
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
This Registry indexed listing is attributed to ricmmartins but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Add the evidence badges to your README
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/ricmmartins-finops-intelligence)
[](https://www.openagentskill.com/skills/ricmmartins-finops-intelligence)
[](https://www.openagentskill.com/skills/ricmmartins-finops-intelligence/audit)
[](https://www.openagentskill.com/skills/ricmmartins-finops-intelligence)Author
ricmmartins
@ricmmartins
Tags
Platform fit
Health signals
- GitHub stars
- 70
- Quality score
- 36/100
- Last GitHub push
- Aug 24, 2026
- Framework hints
- Unknown
- OpenAgentSkill views
- 0
- Install copies
- 0
- Outbound clicks
- 0
Community signal
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Trust & safety
Do not auto-install
- GitHub adoption70 GitHub starsCHECK
- Stars/forks activity70 stars, 14 forks; issue activity unavailable in current metadataCHECK
- Recent maintenancePushed todayPASS
- License clarityMITPASS
- README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
- Dependency/runtime riskcommand execution surface, network or browser surfaceCHECK
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