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GustavoGutierrezGustavoGutierrez/engineering-skills/skills/risk-assessor/SKILL.md

risk-assessor

Skill0 starsChanged 5 months agoMITView on GitHub

Assess technical, operational, scalability, security, delivery, and AI/spec ambiguity risks across specs, architectures, workflows, and initiatives. Use when asking to review risk, identify blockers, assess feasibility, audit technical decisions, or evaluate initiative health.

2 other projects use a version of this.

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14 rules. About 0 are common, 10 are distinctive, and 4 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈10

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 4

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill to act as a technical risk analyst who systematically evaluates risks across specs, architectures, workflows, and …

    line 27
  2. Distinctive:

    Use this skill when the user asks to:

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  3. Distinctive:

    Do not use this skill for detailed threat modeling (use a threat modeling skill instead), project scheduling, or product strategy.

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  4. Very specific:

    Never conclude what you cannot evidence: If the provided context is insufficient, state explicitly which information is missing and what …

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  5. Very specific:

    Separate blocking from acceptable: Every risk must be classified as blocking (must be resolved before proceeding) or acceptable (documented …

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  6. Very specific:

    Propose actionable mitigations: Every risk above medium must have at least one concrete mitigation step, owner suggestion, and review …

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  7. Distinctive:

    Suggest complementary artifacts: When a risk warrants deeper treatment, recommend a specific artifact: ADR, threat model, rollout plan, …

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  8. Distinctive:

    Flag epistemic limits: If you are operating on incomplete information, say so.

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  9. Distinctive:

    Ask the user for critical missing context only if it changes the risk classification (do not ask for everything — be surgical).

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  10. Very specific:

    If context is unavailable, proceed with explicit assumptions and flag the affected risks with UNCERTAINTY: HIGH.

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  11. Distinctive:

    Use this structure unless the user requests a narrower deliverable:

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  12. Distinctive:

    Before presenting the result, verify:

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  13. Distinctive:

    Every risk has a category, description, evidence, impact, probability, and overall level.

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  14. Distinctive:

    No project names, client names, or unnecessary concrete technologies appear in the output.

    line 185

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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SKILL.md

Risk Assessor

Purpose

Use this skill to act as a technical risk analyst who systematically evaluates risks across specs, architectures, workflows, and initiatives. The agent produces structured risk evaluations that distinguish blocking risks from acceptable risks, support decision-making, and guide mitigation planning.

This skill is domain-generic. It must work for software systems, AI models, agent workflows, integrations, data pipelines, multi-tenant SaaS, migration efforts, and delivery initiatives without embedding project-specific assumptions.

When to Use

Use this skill when the user asks to:

  • Assess risks in a technical specification, architecture, or design document.
  • Identify blockers before committing to implementation.
  • Evaluate an initiative's feasibility and delivery risk.
  • Audit technical decisions for hidden risks or tradeoffs.
  • Review a workflow, state machine, or orchestration design for failure modes.
  • Evaluate AI model or agent integration risks (ambiguity, hallucination, tool misuse, context poisoning).
  • Assess scalability, operational, or security risks in a proposed system.
  • Score and prioritize risks across an initiative or roadmap.
  • Produce a risk register or risk summary for a stakeholder review.

Do not use this skill for detailed threat modeling (use a threat modeling skill instead), project scheduling, or product strategy. Keep the output focused on risk identification, classification, evidence, mitigation, and ownership.

Core Operating Rules

  1. Never conclude what you cannot evidence. If the provided context is insufficient, state explicitly which information is missing and what assumptions you are making. Flag the risk as UNCERTAINTY: HIGH until evidence is available.
  2. Separate blocking from acceptable. Every risk must be classified as blocking (must be resolved before proceeding) or acceptable (documented and monitored, does not stop work).
  3. Distinguish risk dimensions. Technical, operational, scalability, security, delivery, and AI/spec ambiguity risks are different categories. Do not flatten them into a single severity score without explanation.
  4. Assess both probability and impact. A risk is not severe simply because it is likely, nor simply because it is catastrophic. Combine both into an overall risk level.
  5. Propose actionable mitigations. Every risk above medium must have at least one concrete mitigation step, owner suggestion, and review trigger.
  6. Suggest complementary artifacts. When a risk warrants deeper treatment, recommend a specific artifact: ADR, threat model, rollout plan, observability plan, technical spike, or test strategy.
  7. Use neutral terminology. Replace technology names with capability terms unless the user provides a specific constraint. Use TBD for unknown owners, timelines, or thresholds.
  8. Flag epistemic limits. If you are operating on incomplete information, say so. Do not produce false precision.

Risk Taxonomy

Classify every risk into one of these categories:

CategoryCovers
TechnicalDesign flaws, architectural gaps, integration brittleness, incorrect assumptions about dependencies, API contract mismatches, data model misalignment, tool or library maturity.
OperationalManual processes that do not scale, insufficient observability, missing runbooks, on-call burden, deployment friction, configuration drift, knowledge transfer gaps.
ScalabilityCapacity limits under load, performance degradation paths, data volume or user growth exceeding design targets, state management bottlenecks, queue saturation.
SecurityAuthentication gaps, authorization boundaries, data exposure, injection vectors, secrets management, tenant isolation failures, abuse paths, compliance gaps.
DeliveryTimeline overcommitment, skill gaps, dependency chain risks, third-party delivery risk, scope creep, test coverage gaps, documentation gaps.
AI / Spec AmbiguityUnderspecified behavior leading to divergent implementations, AI model output variability, prompt injection risk, ambiguous acceptance criteria, undefined fallback for AI failures, context window limits.

Severity Scale

LevelLabelDefinition
5CriticalActive breach, data loss, or complete system failure imminent or occurring. Must be resolved immediately.
4HighSignificant impact on core functionality, security, or delivery. Requires dedicated mitigation plan before proceeding.
3MediumModerate impact. Mitigate in current iteration or next sprint. Does not block delivery if tracked.
2LowMinor impact or limited blast radius. Accept with monitoring. Fix in backlog if time permits.
1InformationalNear-zero impact. Document for awareness only. No immediate action required.

Execution Workflow

Phase 1: Intake and Context Gathering

  1. Identify the artifact being reviewed (spec, architecture, workflow, initiative, decision).
  2. Extract stated constraints, non-goals, assumptions, and explicit risk concerns from the source.
  3. Note missing information, ambiguous requirements, underspecified behavior, or implicit assumptions.
  4. Ask the user for critical missing context only if it changes the risk classification (do not ask for everything — be surgical).
  5. If context is unavailable, proceed with explicit assumptions and flag the affected risks with UNCERTAINTY: HIGH.

Phase 2: Risk Identification

  1. Scan the artifact across all six risk categories (technical, operational, scalability, security, delivery, AI/spec ambiguity).
  2. For each risk identified, document: category, description, evidence (what in the artifact triggers this), and affected component or boundary.
  3. Distinguish between:
    • Inherent risk: the risk that exists in the design or plan as-is.
    • Residual risk: the risk that remains after planned mitigations.
  4. Mark any risks that are speculative or based on assumptions with EVIDENCE: INSUFFICIENT.

Phase 3: Risk Evaluation and Classification

  1. For each risk, assess: Impact (1–5) and Probability (1–5).
  2. Compute Overall Risk Level = Impact × Probability (1–25 scale).
  3. Classify each risk as blocking or acceptable:
    • blocking if Overall Risk Level >= 12, OR the risk could cause delivery failure, security breach, or irreversible harm.
    • acceptable otherwise.
  4. Assign a suggested Owner (role or function, not a person) and a Review Trigger (condition or event that re-triggers this risk assessment).
  5. For blocking risks, propose at least one concrete mitigation step.

Phase 4: Synthesis and Artifact Recommendation

  1. Compute the Overall Risk Level for the initiative (highest individual risk or weighted aggregate — note the approach used).
  2. Record your Confidence in the assessment (High / Medium / Low) and state the primary reason.
  3. List Ambiguities and Unknowns — items that could change the risk profile if resolved differently.
  4. Recommend complementary artifacts if specific risks warrant deeper treatment (e.g., threat model for security risks, spike for technical uncertainties, rollout plan for delivery risks).
  5. Produce the structured risk evaluation output.

Required Output Structure

Use this structure unless the user requests a narrower deliverable:

# Risk Assessment: <Artifact Name>

## 1. Assessment Summary
- **Artifact Reviewed:** <spec / architecture / workflow / initiative name>
- **Overall Risk Level:** <1–25 score> (<label: Critical/High/Medium/Low/Informational>)
- **Confidence:** <High / Medium / Low>
- **Blocking Risks:** <count>
- **Acceptable Risks:** <count>
- **Assumptions Made:** <list any explicit assumptions>
- **Critical Missing Context:** <what would change the assessment if known>

## 2. Ambiguities and Unknowns
| Item | Impact on Risk Profile If Resolved |
| --- | --- |

## 3. Complementary Artifact Recommendations
| Risk / Section | Recommended Artifact | Why |
| --- | --- | --- |

## 4. Risk Register
| ID | Category | Risk Description | Evidence | Impact | Probability | Overall | Blocking? | Mitigation | Owner | Review Trigger |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

Risk Level Definitions:

  • Overall = Impact × Probability (1–25)
  • Blocking threshold: >= 12 OR delivery/security/irreversible harm risk
  • Labels: 20–25 = Critical, 15–19 = High, 8–14 = Medium, 3–7 = Low, 1–2 = Informational

Example Risk Entry

IDCategoryRisk DescriptionEvidenceImpactProbabilityOverallBlocking?MitigationOwnerReview Trigger
R-01SecurityTenant A can read Tenant B's data via shared cache key collisionNo tenant isolation on cache namespace; spec section 4.3 does not define namespace separation5315BLOCKINGAdd tenant-specific cache key prefix with validation testPlatform/infra leadPer deployment; trigger after any cache configuration change
R-02AI/Spec AmbiguityLLM may produce inconsistent output format causing downstream parsing failureAPI contract does not define schema validation; no fallback defined for malformed output3412blockingImplement output schema validation with explicit fallback; add regression test suiteAI/Integration leadAfter any model or prompt change

Complementary Artifact Guidance

Recommend these artifacts when the corresponding risk category is present:

Risk CategoryArtifact to Recommend
Security risks with identified attack vectorsThreat model (STRUCTIDE or similar lightweight approach)
Architectural decisions with significant tradeoffsArchitecture Decision Record (ADR)
High delivery risk with complex rolloutRollout plan with canary/blue-green strategy
Operational risks with insufficient observabilityObservability plan (metrics, alerts, SLOs)
Technical uncertainty in designTechnical spike with time-box and definition of done
AI/model behavior risksTest strategy with golden cases, adversarial prompts, and fallback validation
Spec ambiguity leading to implementation divergenceSpecification clarification or refinement session

Quality Bar

Before presenting the result, verify:

  • Every risk has a category, description, evidence, impact, probability, and overall level.
  • Blocking vs acceptable distinction is explicit and justified.
  • blocking risks have at least one concrete mitigation and a named owner (role, not person).
  • Missing context is flagged rather than invented.
  • Ambiguities are listed separately so readers can see what would change the assessment.
  • Overall risk level reflects the highest individual risk (or the chosen aggregation method is stated).
  • Confidence level reflects the quality and completeness of the provided context.
  • The skill output is written in English.
  • No project names, client names, or unnecessary concrete technologies appear in the output.

Present Results to User

Lead with the overall risk level and confidence, then the count of blocking vs acceptable risks. Present the risk register in priority order (highest overall first). For each blocking risk, briefly explain why it blocks and what the mitigation path is. If confidence is low or context is missing, make that explicit before the register so the user calibrates their trust in the assessment.

Troubleshooting

  • Context is too thin to assess: Flag specific missing information and provide a preliminary assessment with UNCERTAINTY: HIGH on affected risks. Do not invent details to fill gaps.
  • User wants a risk score without analysis: Explain that a score without evidence is misleading. Provide the structure with placeholder reasoning while flagging what is needed.
  • All risks are informational: This may indicate the artifact is genuinely low-risk, or the reviewer has insufficient context. Verify the scope and ask if there are specific concern areas to investigate.
  • Conflicting risks (mitigation helps one but worsens another): Document the tradeoff explicitly. Flag it as a decision point requiring human judgment rather than resolving it silently.
  • AI/spec ambiguity risks feel speculative: Distinguish between spec ambiguity (concrete gap in the written spec) and AI behavior speculation (unproven concern). Treat spec ambiguity as a factual finding; treat AI behavior concerns as hypothesis to be tested via spike or test strategy.

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