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 orcasecurity/orca-skills --skill orca-identity-reviewgit clone --depth 1 https://github.com/orcasecurity/orca-skillsWrote 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/orcasecurity/orca-skills/orca-identity-review)<a href="https://agentmods.dev/skills/orcasecurity/orca-skills/orca-identity-review"><img src="https://agentmods.dev/badge/skills/orcasecurity/orca-skills/orca-identity-review/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/orcasecurity/orca-skills/orca-identity-review"><img src="https://agentmods.dev/badge/skills/orcasecurity/orca-skills/orca-identity-review.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 | $0.00074 | $0.03958 |
| Opus 5 | $0.00037 | $0.01979 |
| Sonnet 5 | $0.00015 | $0.00792 |
| Haiku 4.5 | $0.00007 | $0.00396 |
Grade A, and why
orca-identity-review 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 11d 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 — 448 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orca Identity Review Skill
Answers the question: "Is this identity overprivileged, and what's the blast radius if it's compromised?"
Given an IAM role, user, or service account, analyzes effective permissions vs actual usage from CloudTrail, identifies overprivileged access, maps lateral movement potential, and generates a least-privilege recommendation.
Usage
/orca-identity-review admin-role
/orca-identity-review arn:aws:iam::123456789012:role/bastion-admin-role
Or natural language:
- "review the permissions on admin-role"
- "is terraform-deploy overprivileged?"
- "identity risk for orca-scanner-role"
- "what can this role access?"
Processing Logic
Step 1: Find the Identity
| Input Pattern | Tool | Parameter |
|---|---|---|
ARN format arn:aws:iam::... |
get_asset_by_id |
asset_id with appropriate model_type |
| Role/user name | get_asset_by_name |
asset_name, model_type: "AwsIamRole" or "AwsIamUser" |
| Name (ambiguous) | Try get_asset_by_name with each IAM type, or discovery_search |
If multiple results, show list and ask user to pick.
Extract: ARN, identity type (Role/User/ServiceAccount), account, creation date, tags, attached policies.
Step 2: Gather Data (run ALL in parallel)
Query 1: Effective permissions
get_aws_effective_permissions_policy_on_asset:
asset_arn: "<identity ARN>"
Returns the current effective permissions AND a recommended least-privilege policy.
Query 2: Alerts on this identity
get_asset_related_alerts_summary:
asset_id: <UUID>
Query 3: Alert severity breakdown
get_asset_alerts_count_grouped_by_risk_level:
asset_id: <UUID>
Query 4: What this identity has DONE (CloudTrail)
search_cdr_events:
actors: ["<identity ARN>"]
time_range: "last_30_days"
limit: 100
Query 5: Action summary
get_cdr_events_grouped_by_event_name:
actors: ["<identity ARN>"]
time_range: "last_30_days"
Query 6: Attack paths
get_asset_related_attack_paths_summary:
asset_id: <UUID>
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.
- 11d ago First seen · 448 lines · 74 tokens per session scan A cd824940331d
orca-identity-review is a skill published in the GitHub repository orcasecurity/orca-skills (50 stars, last pushed 5d ago), licensed MIT. It adds 74 tokens to every session and 3,958 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.
Other skills, from other repositories
happiness-skill
A Chinese-language guide to happiness based on reducing unmet wants, focusing on the present, and treating happiness as a trainable skill.
docx-comment-reply
Reply to comments (批注) in Word .docx/.doc files: extract comment context, draft replies, write threaded replies back, and validate OOXML.
asc-subscription-localization
Bulk-localize subscription, subscription-group, and in-app purchase display names across App Store locales using asc, including API 4.4.1 version-scoped v2 resources. Use when filling or updating subscription/IAP names and descriptions without App Store Connect UI work.
pcbway
PCBWay PCB fabrication and assembly — turnkey/consigned assembly, design rules, ordering workflow. Alternative to JLCPCB for manufacturing. Use with KiCad. Use this skill when the user mentions PCBWay, needs turnkey assembly (PCBWay sources parts by MPN), has parts not available on LCSC, needs assembled boards with…
explaining-machine-learning-models
Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.
influence-psychology
Apply the seven principles of ethical persuasion (reciprocity, commitment, social proof, authority, liking, scarcity, unity) to product design, copy, and sales. Use when the user mentions "social proof", "persuasive copy", "why users dont convert", "ethical persuasion", "reciprocity", "scarcity tactics", "commitment…