dissent

安装量: 62
排名: #12122

安装

npx skills add https://github.com/open-horizon-labs/skills --skill dissent

/dissent

Structured disagreement that strengthens decisions. The insight: find flaws before the one-way door closes.

Dissent is not attack. It's the practice of actively seeking reasons you might be wrong. The devil's advocate is a role, not a personality.

When to Use

Invoke /dissent when:

About to lock in a one-way door - architecture choices, major hires, public API contracts, anything hard to reverse Confidence is high but stakes are higher - feeling certain is when you need dissent most Team is converging too quickly - unanimous agreement without debate is a warning sign You're defending a position - advocacy mode is the enemy of truth-seeking The path forward seems obvious - obvious paths have hidden assumptions

Do not use when: Gathering initial options, brainstorming, or exploring. Dissent is for stress-testing decisions, not generating them.

The Dissent Process Step 1: Steel-Man the Current Approach

Before attacking, fully articulate the position you're challenging:

"The current approach is [approach]. The reasoning is [reasoning]. The expected outcome is [outcome]. This is the strongest version of this position."

If you can't state the position charitably, you don't understand it well enough to challenge it.

Step 2: Seek Contrary Evidence

Actively search for information that contradicts the current approach:

What data would prove this approach wrong? Who disagrees with this? What's their strongest argument? What similar approaches have failed elsewhere? Why? What are we ignoring because it's inconvenient?

"If this approach were wrong, what would we expect to see? Are we seeing any of that?"

Step 3: Pre-Mortem

Imagine it's six months from now and this decision failed. Work backward:

"This failed because [reason]. The warning signs we ignored were [signs]. The assumption that broke was [assumption]."

Generate at least three plausible failure scenarios:

Technical failure - It doesn't work as expected Adoption failure - It works but nobody uses it / changes nothing Opportunity cost - It works but we should have done something else Step 4: Surface Hidden Assumptions

Every decision rests on assumptions. Most aren't stated. Find them:

What are we assuming about the user/customer? What are we assuming about the system/codebase? What are we assuming about the timeline/resources? What are we assuming won't change?

For each assumption:

Assumption: [what we're taking for granted] Evidence: [what supports this assumption] Risk if wrong: [what happens if this assumption breaks] Test: [how we could validate this before committing]

Step 5: Decide

Based on the dissent analysis, recommend one of:

PROCEED - Dissent found no critical flaws. Decision strengthened. ADJUST - Dissent surfaced issues that can be addressed. Modify the approach. RECONSIDER - Dissent revealed fundamental problems. Go back to solution space.

Always include the reasoning:

"PROCEED: The strongest counter-argument is [X], but it's addressed by [Y]. The key assumption is [Z], which we've validated by [how]."

ADR Generation

If the decision is a one-way door (hard to reverse) and you recommend PROCEED or ADJUST, offer to create an Architecture Decision Record (ADR). Future team members will ask "why did we do it this way?"

Offer to create an ADR when:

The decision affects system architecture Multiple valid approaches were considered and rejected The reasoning depends on current context that might not be obvious later Someone will likely question this in 6 months

The dissent report maps directly to ADR format:

Dissent Section ADR Section Decision under review Title Steel-Man Position Context Contrary Evidence + Pre-Mortem Options Considered Hidden Assumptions Consequences Decision + Reasoning Decision

If user accepts, write to: docs/adr/NNNN-.md (or project's ADR location)

ADR NNNN: [Decision Title]

Status

Accepted

Context

[From Steel-Man Position - why this decision needed to be made]

Decision

[From Decision section - what we decided and why]

Options Considered

[From Contrary Evidence - what alternatives were evaluated]

Consequences

[From Hidden Assumptions - what we're accepting by making this decision]

Notes

Generated from /dissent on [date]

Output Format

Always produce a dissent report in this structure:

Dissent Report

Decision under review: [what's being challenged] Stakes: [why this matters] Confidence before dissent: [HIGH/MEDIUM/LOW]

Steel-Man Position

[The strongest version of the current approach]

Contrary Evidence

  1. [Evidence point 1]
  2. [Evidence point 2]
  3. [Evidence point 3]

Pre-Mortem Scenarios

  1. [Failure mode]: [How this could fail]
  2. [Failure mode]: [How this could fail]
  3. [Failure mode]: [How this could fail]

Hidden Assumptions

Assumption Evidence Risk if Wrong Test
[Assumption 1] [Evidence] [Risk] [Test]
[Assumption 2] [Evidence] [Risk] [Test]

Decision

Recommendation: [PROCEED / ADJUST / RECONSIDER]

Reasoning: [Why this recommendation]

If ADJUST: [Specific modifications]

Confidence after dissent: [HIGH/MEDIUM/LOW]

Create ADR? [Yes - offer to write docs/adr/NNNN-slug.md / No - not a one-way door]

Examples Example 1: Architecture Decision /dissent

We're about to adopt a microservices architecture for the new payments system. The team agrees this is the right approach.

Output:

Dissent Report

Decision under review: Microservices architecture for payments Stakes: 6+ month commitment, affects entire system Confidence before dissent: HIGH (team unanimous)

Steel-Man Position

Microservices will allow independent scaling of payment processing, enable different teams to deploy independently, and provide fault isolation so one service failure doesn't bring down payments.

Contrary Evidence

  1. We have 4 engineers. Netflix had 100+ when they went microservices.
  2. Our deployment pipeline takes 45 minutes. We'll multiply that by N services.
  3. The "independent teams" benefit requires teams we don't have.

Pre-Mortem Scenarios

  1. Distributed systems complexity: We spend 60% of time debugging cross-service issues instead of building features.
  2. Operational overhead: We can't hire SREs fast enough. System is down because nobody understands the full architecture.
  3. Premature optimization: The "scaling" benefit never materializes because we never got enough users to need it.

Hidden Assumptions

Assumption Evidence Risk if Wrong Test
We'll need to scale components independently None - projected Waste of complexity Check actual load patterns
Team will grow to support this Hiring plan exists Stuck with complexity, no staff Validate funding/timeline
Network latency acceptable Not measured Payment failures Prototype and benchmark

Decision

Recommendation: RECONSIDER

Reasoning: The benefits of microservices assume scale and team size we don't have. The strongest argument for microservices (independent scaling) has no evidence supporting the need. A modular monolith gives us the code organization benefits without the operational complexity.

Confidence after dissent: HIGH (clear mismatch)

Create ADR? No - we're reconsidering, not committing

Example 2: Feature Decision /dissent

We're about to ship a recommendation system that suggests similar items based on purchase history. Built over 3 sprints.

Output:

Dissent Report

Decision under review: Ship purchase-history recommendation system Stakes: 3 sprints invested, prominent homepage placement Confidence before dissent: MEDIUM

Steel-Man Position

Users who bought X often want Y. Amazon proved this works. We have the purchase data. The model is trained and performs well in testing.

Contrary Evidence

  1. Our testing used historical data. Users might behave differently when seeing recommendations in real-time.
  2. We have 10K users. Amazon's model works with billions of data points.
  3. Competitor tried this, removed it after 6 months (no lift).

Pre-Mortem Scenarios

  1. Cold start problem: New users see garbage recommendations, leave before making a purchase we could learn from.
  2. Filter bubble: System recommends what users already buy, doesn't drive discovery of new product categories.
  3. Wrong metric: Recommendations get clicks but not purchases. We optimized for engagement, needed revenue.

Hidden Assumptions

Assumption Evidence Risk if Wrong Test
Purchase history predicts intent Industry standard Wasted real estate A/B test against simple "popular items"
10K users is enough data None Poor recommendations Check minimum viable data in literature
Homepage placement is right None Users ignore it Heatmap existing traffic patterns

Decision

Recommendation: ADJUST

Reasoning: The approach is sound but we're shipping without validation. Risk is manageable with modifications.

Modifications: 1. Ship with A/B test against "popular items" baseline 2. Add fallback to curated picks for users with <5 purchases 3. Define success metric (revenue lift, not clicks) before launch

Confidence after dissent: MEDIUM (reduced risk with A/B)

Create ADR? Yes - shall I write docs/adr/0012-recommendation-system-rollout.md? Documents why we chose A/B testing and what success metrics we committed to.

Session Persistence

When invoked with a session name (/dissent ), this skill reads and writes to .oh/.md.

Reading: Check for existing session file. Read Aim, Problem Statement, Solution Space to understand the decision being challenged.

Writing: After producing the dissent report:

Dissent

Updated: Decision: [PROCEED | ADJUST | RECONSIDER]

[dissent report content]

Adaptive Enhancement Base Skill (prompt only)

Works anywhere. Produces dissent report for manual review. No persistence.

With .oh/ session file Reads .oh/.md for context on the decision Writes dissent report to the session file Subsequent skills see that dissent was performed With .wm/ (working memory) Also reads/writes .wm/sessions/ Session file and working memory can coexist With Open Horizons MCP Queries past decisions on similar topics Retrieves relevant guardrails and tribal knowledge Logs dissent decision for future reference Session file serves as local cache With Team Input Aggregates dissent from multiple reviewers Tracks which assumptions different team members question Leads To

After dissent, typically:

PROCEED - Continue to /execute or /ship with strengthened confidence ADJUST - Update the approach, then /review the modifications RECONSIDER - Return to /solution-space with new constraints

Remember: Dissent is not doubt. It's the discipline of seeking truth before comfort. The goal isn't to stop decisions—it's to make better ones.

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