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 vignesh2027/AI-AGENT-SKILLS --skill risk-assessmentgit clone --depth 1 https://github.com/vignesh2027/AI-AGENT-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/vignesh2027/ai-agent-skills/risk-assessment)<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/risk-assessment"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/risk-assessment/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/vignesh2027/ai-agent-skills/risk-assessment"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/risk-assessment.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 | $0.00016 | $0.00408 |
| Opus 5.5 | $0.00006 | $0.00163 |
| Sonnet 5.5 | $0.00003 | $0.00082 |
| Haiku 4.5 | $0.00002 | $0.00041 |
Grade A, and why
risk-assessment 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 2mo 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.
What it actually says
Overview
Risk not identified is risk not managed. This skill makes technical and delivery risk explicit so it can be mitigated, accepted, or escalated — before it surprises you.
When to Use
- Before estimating a project
- Before starting a large migration or refactor
- Before a major deployment
- When a project has been "almost done" for two weeks
Process
Step 1: List all risks
Brainstorm: what could go wrong? Include: technical unknowns, external dependencies, team knowledge gaps, deadline pressure, infrastructure, security, data integrity.
Step 2: Score each risk
For each risk: Likelihood (1–3) × Impact (1–3) = Risk Score (1–9). Prioritize scores ≥ 6.
Step 3: Classify risks
- Known known: You know about it and understand it. Mitigate.
- Known unknown: You know there's something you don't know. Research it.
- Unknown unknown: You don't know what you don't know. Add buffer and run pre-mortems.
Step 4: Design mitigations
For each high-score risk: what is the mitigation? What is the fallback? Who owns it?
Step 5: Pre-mortem
Imagine it's 3 months from now and the project failed. What went wrong? Work backwards. This surfaces unknown unknowns.
Step 6: Define go/no-go criteria
For the deployment or launch: what conditions must be true to proceed? What conditions force a delay?
Verification Requirements
- All risks identified and scored
- High-score risks have documented mitigations
- Pre-mortem conducted
- Go/no-go criteria defined
- Risk log tracked throughout the project
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.
- 2mo ago First seen · 48 lines · 16 tokens per session scan A 178221f7d820
risk-assessment is a skill published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 2mo ago), licensed MIT. It adds 16 tokens to every session and 408 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
risk-assessment compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| risk-assessmentvignesh2027/AI-AGENT-SKILLS | Skill | 2 | 2mo ago | MIT | A | 16 | Claude Code, Codex |
| cw-gatesHmbown/Codewhale | Skill | 41k | 1mo ago | MIT | A | 48 | Claude Code, Codex |
| feishuHmbown/CodeWhale | Skill | 41k | 6d ago | MIT | A | 33 | Claude Code, Codex |
| eval-dataset-designbojieli/ai-agent-book | Skill | 57k | 1mo ago | Apache-2.0 | A | 138 | Claude Code, Codex |
| memory-systembojieli/ai-agent-book | Skill | 57k | 1mo ago | Apache-2.0 | A | 95 | Claude Code, Codex |
| release-helperSahir619/fable-method | Skill | 2.3k | 1mo ago | MIT | A | 43 | Claude Code |
| loongsuite-pilot-insightalibaba/loongsuite-pilot | Skill | 242 | yesterday | Apache-2.0 | A | 111 | Claude Code, Codex |