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 paruff/uFawkesAI --skill risk-identificationgit clone --depth 1 https://github.com/paruff/uFawkesAIWrote 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/paruff/ufawkesai/risk-identification)<a href="https://agentmods.dev/skills/paruff/ufawkesai/risk-identification"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/risk-identification/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/paruff/ufawkesai/risk-identification"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/risk-identification.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 | $0.00027 | $0.00485 |
| Opus 5 | $0.00014 | $0.00243 |
| Sonnet 5 | $0.00005 | $0.00097 |
| Haiku 4.5 | $0.00003 | $0.00049 |
Grade A, and why
risk-identification 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 8d 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
Skill: Risk Identification
Load trigger:
"load risk-identification skill"> DORA: Cap 3 (AI-Accessible Internal Data) Token cost: Low
Purpose
Identify risks early in the planning process.
Responsibilities
- Identify technical risks
- Identify architectural risks
- Identify security risks
- Identify compliance risks
- Produce a risk report with mitigations
Inputs
specification.mddesign.mdtasks.json
Outputs
risk-report.json
Risk Categories
Technical Risks
- New or unfamiliar technology stack
- Performance-sensitive code paths
- Concurrency or race condition potential
- Data migration or state transition risks
- External API availability or reliability
Architectural Risks
- Changes to shared libraries or interfaces
- Breaking changes to public APIs
- Circular dependencies introduced
- Single points of failure added
- Scalability bottlenecks
Security Risks
- Authentication or authorization changes
- Secret or credential handling
- Input validation requirements
- Data exposure risks
- Privilege escalation paths
Compliance Risks
- Regulatory requirements affected
- Data residency or privacy concerns
- Audit trail requirements
- Policy violations introduced
Risk Format
{
"risk_id": "RISK-NNN",
"category": "technical | architectural | security | compliance",
"severity": "high | medium | low",
"description": "Description of the risk",
"affected_tasks": ["TASK-001"],
"mitigation": "Recommended mitigation strategy",
"residual_risk": "What remains after mitigation"
}
Success Criteria
- Risks are clearly identified and categorized
- Each risk has a mitigation strategy
- High-severity risks flagged for human review
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.
- 8d ago First seen · 90 lines · 27 tokens per session scan A 5ba0fdc1823e
risk-identification is a skill published in the GitHub repository paruff/uFawkesAI (2 stars, last pushed 19d ago), licensed MIT. It adds 27 tokens to every session and 485 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps taskplan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and…
infrastructure-overview
Top-level skill for the research template infrastructure layer. Use in Cursor, Claude Code, or similar agents when editing or importing anything under infrastructure/, understanding the two-layer architecture, or wiring build/validation/rendering/publishing. Covers module discovery, import patterns, thin…
infrastructure-validation
Skill for the validation infrastructure module providing PDF validation, markdown validation, output integrity checks, link verification, documentation audits, issue categorization, and repository scanning. Use when validating research outputs, checking document quality, running audits, or verifying cross-references.
template-academic-paper
Template-native manuscript planning, outline, drafting, revision, formatting, citation check, and AI-use disclosure routing. USE WHEN the user asks to write, outline, revise, format, or prepare a paper inside the Research Project Template.
infrastructure-llm
Skill for the LLM infrastructure module providing local Large Language Model integration via Ollama. Covers client initialization, prompt templates, output validation, manuscript review generation, conversation context, and CLI usage. Use when querying LLMs, generating manuscript reviews, validating LLM outputs, or…
research-workflow
Seven-stage research workflow (SCOPE→LITERATURE→REASON→DESIGN→COMPUTE→SYNTHESIZE→WRITE). Use for: structuring an AI agent's research process, generating literature review prompts, scoping methodology. Usage: from infrastructure.research import ResearchWorkflow; ResearchWorkflow.describe() Config: set stage overrides…