skills/deliberation-debate-red-teaming/SKILL.md
Challenges plans, designs, and decisions from multiple adversarial perspectives to surface blind spots, hidden assumptions, and vulnerabilities before they cause real damage. Use when testing plans or decisions for blind spots, need adversarial review before launch, validating strategy against worst-case scenarios, building consensus through structured debate, identifying attack vectors or vulnerabilities, or when groupthink or confirmation bias may be hiding risks.
npx skillsauth add lyndonkl/claude deliberation-debate-red-teamingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Copy this checklist and track your progress:
Deliberation & Red Teaming Progress:
- [ ] Step 1: Define the proposal and stakes
- [ ] Step 2: Assign adversarial roles
- [ ] Step 3: Generate critiques and challenges
- [ ] Step 4: Synthesize findings and prioritize risks
- [ ] Step 5: Recommend mitigations and revisions
Step 1: Define the proposal and stakes
Ask user for the plan/decision to evaluate (specific proposal, not vague idea), stakes (what happens if this fails), current confidence level (how certain are they), and deadline (when must decision be made). Understanding stakes helps calibrate critique intensity. See Scoping Questions.
Step 2: Assign adversarial roles
Identify critical perspectives that could expose blind spots. Choose 3-5 roles based on proposal type (security, legal, operations, customer, competitor, etc.). Each role has different incentives and concerns. See Adversarial Role Types and resources/template.md for role assignment guidance.
Step 3: Generate critiques and challenges
For each role, generate specific critiques: What could go wrong? What assumptions are questionable? What edge cases break this? Be adversarial but realistic (steelman, not strawman arguments). For advanced critique techniques → See resources/methodology.md for red team attack patterns.
Step 4: Synthesize findings and prioritize risks
Collect all critiques, identify themes (security gaps, operational risks, customer impact, etc.), assess severity and likelihood for each risk. Distinguish between showstoppers (must fix) and acceptable risks (monitor/mitigate). See Risk Prioritization.
Step 5: Recommend mitigations and revisions
For each critical risk, propose concrete mitigation (change the plan, add safeguards, gather more data, or accept risk with monitoring). Present revised proposal incorporating fixes. See Mitigation Patterns for common approaches.
To define the proposal:
To understand stakes:
To calibrate critique:
Choose 3-5 roles that are most likely to expose blind spots for this specific proposal:
Competitor:
Malicious Actor (Security):
Regulator/Auditor:
Investigative Journalist:
Operations/SRE:
Customer/User:
Finance/Budget:
Legal/Compliance:
Engineering/Technical:
Pessimist:
Contrarian:
Long-term Thinker:
After generating critiques, prioritize by severity and likelihood:
Critical (5): Catastrophic failure (data breach, regulatory fine, business shutdown) High (4): Major damage (significant revenue loss, customer exodus, reputation hit) Medium (3): Moderate impact (delays, budget overrun, customer complaints) Low (2): Minor inconvenience (edge case bugs, small inefficiency) Trivial (1): Negligible (cosmetic issues, minor UX friction)
Very Likely (5): >80% chance if we proceed Likely (4): 50-80% chance Possible (3): 20-50% chance Unlikely (2): 5-20% chance Rare (1): <5% chance
Showstoppers (score ≥ 15): Must address before proceeding High Priority (score 10-14): Should address, or have strong mitigation plan Monitor (score 5-9): Accept risk but have contingency Accept (score < 5): Acknowledge and move on
| Severity ↓ / Likelihood → | Rare (1) | Unlikely (2) | Possible (3) | Likely (4) | Very Likely (5) | |---------------------------|----------|--------------|--------------|------------|-----------------| | Critical (5) | 5 (Monitor) | 10 (High Priority) | 15 (SHOWSTOPPER) | 20 (SHOWSTOPPER) | 25 (SHOWSTOPPER) | | High (4) | 4 (Accept) | 8 (Monitor) | 12 (High Priority) | 16 (SHOWSTOPPER) | 20 (SHOWSTOPPER) | | Medium (3) | 3 (Accept) | 6 (Monitor) | 9 (Monitor) | 12 (High Priority) | 15 (SHOWSTOPPER) | | Low (2) | 2 (Accept) | 4 (Accept) | 6 (Monitor) | 8 (Monitor) | 10 (High Priority) | | Trivial (1) | 1 (Accept) | 2 (Accept) | 3 (Accept) | 4 (Accept) | 5 (Monitor) |
For each identified risk, choose mitigation approach:
1. Revise the Proposal (Change Plan)
2. Add Safeguards (Reduce Likelihood)
3. Reduce Blast Radius (Reduce Severity)
4. Contingency Planning (Prepare for Failure)
5. Gather More Data (Reduce Uncertainty)
6. Accept and Monitor (Informed Risk)
7. Delay/Cancel (Avoid Risk Entirely)
Skip red teaming if:
Use instead:
Process:
Common adversarial roles:
Risk prioritization:
Resources:
Deliverable: deliberation-debate-red-teaming.md with critiques, risk assessment, and mitigation recommendations
testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.