consequences
Scannednpx machina-cli add skill rana/yogananda-skills/consequences --openclawRead all project markdown documents to ground in the project's actual state.
Consequence Mapping
Given the proposed change:
$ARGUMENTS
Propagate forward through these dimensions:
- Direct effects — What immediately changes? Code, behavior, data, interfaces.
- Behavioral shifts — How does system behavior change for users, operators, developers? What works differently?
- Maintenance burden — Does this make the system easier or harder to maintain? What new complexity is introduced? What's simplified?
- Adjacent systems — What other components, services, or workflows are affected? What contracts change?
- Feedback loops — Does this change create any self-reinforcing dynamics? Positive or negative spirals?
- Optionality — What future choices does this enable? What does it foreclose? Are any changes irreversible?
- Failure modes — What new ways can the system fail? What existing failure modes change?
For every consequence identified:
- Which order it is (1st, 2nd, 3rd)
- Whether it's reversible or irreversible
- Likelihood (certain, likely, possible)
- Where it manifests (code, docs, ops, user experience)
- Whether it needs to be addressed now or can be deferred
Present as a propagation chain, not a flat list. Show how effects cascade.
Output Management
Hard constraints:
- Segment output into groups of up to 8 consequences, ordered by impact and irreversibility.
- Write each segment incrementally. Do not accumulate a single large response.
- After completing each segment, continue immediately to the next. Do not wait for user input.
- Continue until ALL consequences are reported. State the total count when complete.
- If the analysis surface is too large to complete in one session, state what was covered and what remains.
What questions would I benefit from asking?
What am I not asking?
Source
git clone https://github.com/rana/yogananda-skills/blob/main/skills/consequences/SKILL.mdView on GitHub Overview
Consequence Mapping performs forward-propagation analysis on a proposed change to surface ripple effects across the seven dimensions: Direct effects, Behavioral shifts, Maintenance burden, Adjacent systems, Feedback loops, Optionality, and Failure modes. Grounded in the current project state, it reveals direct, indirect, and irreversible consequences before committing to a major decision.
How This Skill Works
Start with the proposed change as the argument. The analysis propagates through seven dimensions, building a chain of 1st, 2nd, and 3rd-order consequences. For each item, you record order, reversibility, likelihood, where it manifests, and whether it must be addressed now or can be deferred; results are presented as a propagation chain, segmented into groups of up to eight consequences.
When to Use It
- Before committing to a major feature change or API revision that touches multiple components
- When planning a refactor that will ripple across modules, services, or data flows
- Prior to a deployment or configuration change that could affect users or operators
- During data-model or schema changes where contracts or interfaces may be affected
- When evaluating new automation, maintenance processes, or optional features that alter future options
Quick Start
- Step 1: Define the proposed change or decision
- Step 2: Run consequence mapping across the seven dimensions, recording order, reversibility, likelihood, and manifestation
- Step 3: Review the propagation chain with stakeholders and decide on mitigations or deferments
Best Practices
- Start with a precise proposed change or decision as the argument
- Map across all seven dimensions to avoid missing ripple effects
- Assign 1st/2nd/3rd order, reversibility, likelihood, and where each consequence manifests
- Present the results as a chain, not a flat list, to visualize cascading effects
- Document mitigations, decisions, or deferments to preserve traceability
Example Use Cases
- Upgrading an API version in a microservices system and tracing downstream contract changes
- Redesigning the onboarding flow in a web app and evaluating UX, analytics, and support processes
- Changing a data retention policy and mapping impacts on storage, compliance, and user data handling
- Introducing a feature flag to enable gradual rollout and examining feedback loops and maintenance burden
- Migrating to a new logging framework and assessing performance, operators' training, and failure modes
