uno-reverse
Radical simplification, red-teaming, and Occam's Razor devil's advocate for software architecture, PRDs, agent workflows, and technical designs. Challenges feature creep, speculative abstractions, and bloated specifications by proposing minimum viable primitives that deliver 90%
Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add danicat/skills --skill uno-reverse
Maintenance
fresh
Pushed today
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
16
59/100 Quality · 63/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
Trust, audit, and install readiness at a glance
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Human review before install
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
16 GitHub stars
Repo activity
16 stars, 3 forks
Maintenance
Pushed today
License
Apache-2.0
Install
npx skills add danicat/skills --skill uno-reverse
Install safety
standard package or runtime install path
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Review before production
- SKILL.md does not explicitly state limitations or scenarios where the skill should not be applied, which could lead to misuse.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
Install readiness
Install path available
- Install path is available
- Repository evidence is available
- License is declared
- No Agent Proven outcome evidence yet
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Search sources
Suited agents
Install decision
- Command
- npx skills add danicat/skills --skill uno-reverse
- Policy
- block
- Human review
- yes
Trust and risk
- Trust
- 55/100
- Audit
- 71/100
- Risk level
- Needs review
Outcome loop
- Endpoint
- /api/agent/outcome
- Event ID
- resolve
- Outcomes
- 5
Install command
npx skills add danicat/skills --skill uno-reverseDo not use when
- teams that need a vendor-supported SLA
- production agents without a repository review
- Low GitHub adoption signal
- SKILL.md does not explicitly state limitations or scenarios where the skill should not be applied, which could lead to misuse.
- High-risk permission hints: Shell or command execution, Secrets or environment access
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Agent safety v2
27/100 · Avoid automatic install
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Shell or command execution
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Filesystem access
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Secrets or environment access
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
- High-risk permission hints: Shell or command execution, Secrets or environment access
- Dependency or permission surface needs review
Install targets
Install this skill in your agent workflow
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install danicat-uno-reverseAgent resolve plan
Let an agent verify fit before installing.
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20uno-reverse%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20uno-reverse%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/danicat-uno-reverse/install
Agent should check
- Task fit and alternatives from Resolve API.
- Audit score, trust score, and safety policy warnings.
- Install target compatibility for Codex, Claude Code, Cursor, or CLI.
Copy prompt
Task: Use uno-reverse in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20uno-reverse%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/danicat-uno-reverse/install
Install command: npx skills add danicat/skills --skill uno-reverse
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Give an agent the install path, not another directory page.
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/danicat-uno-reverse/install
LLM text format
/api/skills/danicat-uno-reverse/install?format=text
Find alternatives
/api/skills/search?q=uno-reverse&limit=3
Agent prompt
Use uno-reverse for this task. Review https://www.openagentskill.com/api/skills/danicat-uno-reverse/install, then install with: npx skills add danicat/skills --skill uno-reverseRegistry metadata
Agent-readable profile for automatic skill selection.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/danicat-uno-reverse
LLM text
/api/registry/manifest/danicat-uno-reverse?format=text
Install alias
/api/registry/install/danicat-uno-reverse
Recommend
/api/registry/recommend?task=Use%20uno-reverse%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
Needs review · 71/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
Install path
Command ready
Use when
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
Evidence
- recent repository activity
- install command or GitHub repo available
- 59/100 quality profile
- 1 OpenAgentSkill engagement events
review first
- Low GitHub adoption signal
- SKILL.md does not explicitly state limitations or scenarios where the skill should not be applied, which could lead to misuse.
Implementation path
- 1Install it in a sandbox agent and run one Research agents task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Trust profile
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub adoption
FIX16 GitHub stars
Stars/forks activity
FIX16 stars, 3 forks; issue activity unavailable in current metadata
Recent maintenance
PASSPushed today
License clarity
PASSApache-2.0
Good signals
- AI review approved
- Install path is available
- Repository evidence is available
- Recently maintained repository
- Install command has no obvious high-risk pattern
- Outcome loop is ready but needs first real agent run
Review before install
- SKILL.md does not explicitly state limitations or scenarios where the skill should not be applied, which could lead to misuse.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 16 GitHub stars
- Stars/forks activity: 16 stars, 3 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- No real agent outcome reports yet
- Human review required before unattended installation
Recommended action
Choose a stronger alternative or inspect the source manually before any install attempt.
Quality profile
Promising candidate for agent workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Use this skill in these scenarios
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Publish consistently
Content automation
I need my agent to turn research and product updates into useful content drafts.
Work with data stores
Database and SQL
I need my agent to inspect database schemas, write SQL, and explain query results.
Workflow fit
Add it to a complete workflow
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Alternative shortlist
Compare before you install
Similar skills that may fit this task.
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GPT Researcher
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Overview
--- name: uno-reverse description: > Radical simplification, red-teaming, and Occam's Razor devil's advocate for software architecture, PRDs, agent workflows, and technical designs. Challenges feature creep, speculative abstractions, and bloated specifications by proposing minimum viable primitives that deliver 90% of value with 10% of moving parts. Activate when reviewing complex technical proposals, pruning bloated architectures, red-teaming design docs, eliminating speculative features, or seeking the simplest possible path to production. license: Apache-2.0 metadata: category: agents tags: "simplification, red-team, architecture, occams-razor, minimalism, review, refactoring" author: Daniela Petruzalek (daniela@danicat.dev) version: "0.1.0" catalog: https://skills.danicat.dev ---
# Uno-Reverse: Radical Simplification & Red-Teaming
> *"Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away."* — Antoine de Saint-Exupéry
The `uno-reverse` skill provides a rigorous **contrarian simplification and red-teaming framework**. While standard engineering processes naturally drift toward feature accretion, defensive layering, and speculative generalization, `uno-reverse` forces the opposite trajectory: **aggressive subtraction, primitive collapsing, and minimum viable execution**.
---
## 🎯 The 4 Inversion Principles
``` ┌─────────────────────────────────────────────────────────────┐ │ THE UNO-REVERSE RAZOR │ ├─────────────────────────────────────────────────────────────┤ │ 1. Subtraction Before Addition: "Can we delete our way out?" │ │ 2. Primitive Collapsing: "Can 1 composable primitive do 5 jobs?"│ │ 3. Zero-Speculation (YAGNI): "Are we solving an imagined problem?"│ │ 4. Failure-Surface Inversion: "How will this complexity break?" │ └─────────────────────────────────────────────────────────────┘ ```
1. **Subtraction Before Addition**: Before designing a new subsystem, caching layer, or protocol, ask: *What existing assumption or artificial constraint can be deleted to make this entire feature unnecessary?* 2. **Primitive Collapsing**: Engineers frequently add flags, endpoints, and micro-abstractions for every sub-case. Identify the single underlying mathematical or conceptual primitive that subsumes all sub-cases without bespoke code. 3. **Zero-Speculation (Strict YAGNI)**: Reject all "future-proofing", pluggable abstraction layers for single implementations, and configurable policies where a single sensible constant or deterministic convention works. 4. **Failure-Surface Inversion**: Evaluate a system by its total attack, bug, and maintenance surface: $$\text{Reliability} \propto \frac{1}{\text{Moving Parts}^2}$$ Every added cache, lock, daemon, state file, and flag represents a new failure mode and cognitive tax.
---
## 🚩 Speculative Language Red-Flag Filter (PRDs & Specs)
When auditing technical proposals or PRDs, immediately flag these weasel phrases:
| Speculative Phrase ❌ | Underlying Reality | Uno-Reverse Action ✅ | | :--- | :--- | :--- | | *"Future-proof design"* | Unused code and speculative abstractions today | Delete the abstraction; write the concrete implementation. | | *"Pluggable provider model"* | Only 1 provider exists | Hardcode the single provider until a 2nd concrete provider is built. | | *"Flexible policy engine"* | Author avoided making a design decision | Pick the single sensible default convention. | | *"Event-driven microservices"* | Synchronous calls disguised as message queues | Use direct in-process function calls. | | *"Highly configurable"* | Shifting architectural choices to end-users | Ship zero flags; make opinionated choices. | | *"Generic abstraction layer"* | Premature DRY before seeing 3 distinct patterns | Duplicate the 5 lines of code; wait for Rule of Three. |
---
## 🔬 Accidental Complexity Code Smells (Codebase Audits)
When reviewing code, actively hunt down and prune these structural anti-patterns:
1. **Single-Implementation Interfaces**: * *Smell*: An interface `FooService` with only one concrete struct `fooServiceImpl`. * *Fix*: Delete the interface. Export the concrete struct directly. Introduce interfaces only when consumers need mocking at architectural boundaries. 2. **Passthrough Wrapper Functions**: * *Smell*: Function `GetUserData(id)` that does nothing except call `db.FetchUser(id)`. * *Fix*: Eliminate the middleman. Call the underlying operation directly. 3. **State Machine Inflation**: * *Smell*: A 7-state lifecycle machine (`PENDING_APPROVAL`, `READY_FOR_QUEUE`, `QUEUED`, ...) with 15 transition validation functions. * *Fix*: Collapse to 2 boolean flags or an active/done state. 4. **Relational Over-Normalization for Small Datasets**: * *Smell*: A 6-table normalized schema with foreign keys and joins for $< 10,000$ total records. * *Fix*: Store as a single flat SQLite table, JSON document, or in-memory map.
---
## 🤖 The Agent & AI Workflow Razor
AI systems are especially prone to multi-agent and prompt bloat. Apply these rules:
| Bloated Agent Pattern ❌ | Collapsed Alternative ✅ | Rationale | | :--- | :--- | :--- | | **5-Agent Swarm for sequential task** | Single agent with 1 clear prompt | Multi-agent handoffs add latency, token cost, and lossy context degradation. | | **Intermediate Summarizer Agents** | Direct downstream consumption | "Telephone game" summarization strips critical nuance. | | **Micro-Tool Sprawl (10 single-action tools)** | 1 Polymorphic Tool with clear args | Decreases tool selection entropy and LLM routing hallucinations. | | **Autonomous Loop without Guardrails** | Deterministic script + LLM leaf node | Use code for control flow and LLMs only for fuzzy transformation. |
---
## 🔍 The 4-Step Uno-Reverse Audit Workflow
When invoked on a design document, PRD, or proposed codebase change, execute this 4-step inversion protocol:
```mermaid flowchart TD A[Incoming Proposal / Complex Spec] --> B[Step 1: The Subtraction Test] B --> C[Step 2: Primitive Collapsing & Flag Pruning] C --> D[Step 3: The Cache & State Invalidation Probe] D --> E[Step 4: The 10% Minimum Viable Proposal] E --> F[Output: Simplification Scorecard & Minimalist Spec] ```
### Step 1: The Subtraction Test (The 3 Deadly Questions) Apply these questions to every component in the proposal: 1. **The Ghost Problem Test**: If we do *nothing* and ship zero lines of code, what *actually* breaks in production today? 2. **The 90/10 Rule**: What 10% of this proposal delivers 90% of the actual user value? Can we discard the remaining 90% of the spec? 3. **The Accidental Complexity Probe**: Is this feature solving a real user problem, or is it solving a problem introduced by an earlier bad abstraction?
### Step 2: Primitive Collapsing & Surface Pruning Collapse multiple flags, commands, or data structures into single composable primitives:
| Bloated Pattern (Before ❌) | Collapsed Primitive (After ✅) | Rationale | | :--- | :--- | :--- | | `--page`, `--section`, `--index`, `--toc`, `#slug` | Positional URI path `doc[#section]` | Unified resource addressability replaces 5 separate flags. | | Separate `read`, `load`, `refs`, `peek` commands | Single polymorphic `load <target>` | Reduces agent decision entropy and CLI verb sprawl. | | Configuration file + 12 env vars + CLI flags | Deterministic convention over configuration | Eliminates configuration drift and precedence bugs. | | In-memory LRU cache + Disk cache + Remote sync | Fast on-the-fly streaming | Raw operations in RAM (< 1 ms) make caching slower than compute. |
### Step 3: The Cache & State Invalidation Probe Whenever a proposal introduces caching, local state files, or background workers: - **Calculate the Cache Paradox**: Measure the cost of on-the-fly computation vs. cache serialization, disk I/O, hash verification, and invalidation race conditions. - **Enforce Ephemeral Execution**: If in-memory computation takes $< 1\text{ ms}$, **strictly forbid persistent caching layers**.
### Step 4: The 10% Minimum Viable Proposal (MVP) Draft an alternative "Uno-Reverse Specification" that: - Achieves the core objective in $\le 20\%$ of the proposed lines of code. - Uses zero external dependencies or heavy frameworks. - Requires zero background daemons, zero state migrations, and zero cache management.
---
## 🛡️ Chesterton’s Fence: When NOT to Simplify
Radical simplification is **not** reckless deletion. Before eliminating a mechanism, identify whether it represents **Essential** or **Accidental** complexity:
``` ┌───────────────────────────────────────┬───────────────────────────────────────┐ │ NEVER PRUNE (Essential Safety) │ ALWAYS PRUNE (Accidental Bloat) │ ├───────────────────────────────────────┼───────────────────────────────────────┤ │ • Concurrency locks & race guards │ • Unbenchmarked caching layers │ │ • Authentication & permission checks │ • Generic abstract factories │ │ • Input validation & sanitization │ • Pluggable drivers for 1 provider │ │ • Idempotency tokens & rollbacks │ • Config flags for internal decisions │ │ • Explicit error handling boundaries │ • Micro-agent coordination swarms │ └───────────────────────────────────────┴───────────────────────────────────────┘ ```
> **The Chesterton Gate**: *If you cannot explain why a defensive check or data field was originally added, you are forbidden from deleting it until you understand its failure mode.*
---
## 📋 The Simplification Audit Scorecard
Deliver all audit results in this standardized, high-signal markdown format:
```markdown # 🔄 Uno-Reverse Simplification Audit: [Topic / Proposal]
## 1. Executive Inversion Summary - **Proposed Complexity**: [Summary of moving parts, services, and flags in original design] - **Recommended Verdict**: [Prune / Collapse / Re-architect] - **Potential Code Reduction**: ~X% (from ~Y LOC to ~Z LOC)
## 2. The Cut List (Items to Eliminate Immediately) | Proposed Feature / Component | Reason for Elimination | What Happens Without It | | :--- | :--- | :--- | | [Feature A] | Speculative generalization | Nothing; solve with a single constant | | [Feature B] | Cache paradox (I/O > Compute) | Scan on the fly in < 0.1 ms |
## 3. Collapsed Primitives - **Instead of**: [List of disparate flags / commands] - **Use**: [Single elegant primitive]
## 4. The Minimalist Reference Design [Concrete, ultra-compact specification / code snippet implementing the 10% MVP]
## 5. Chesterton Boundary Assessment - **Essential Complexity Retained**: [Security, safety, or concurrency checks kept intact] - **Risk & Trade-off Assessment**: [Edge cases intentionally omitted and why the trade-off is sound] ```
---
## 🚫 Common Engineering Traps to Call Out
1. **"What if the user wants X?" (Speculative Customization)**: - *Uno-Reverse Response*: "Wait until 3 distinct users actively request it in production before writing code for it." 2. **"We might support other backends later" (Premature Extensibility)**: - *Uno-Reverse Response*: "Implement the concrete backend directly. Refactoring clean concrete code is 10x faster than maintaining unused abstractions." 3. **"Let's add a cache for performance" (Unbenchmarked Caching)**: - *Uno-Reverse Response*: "Benchmark the raw in-memory operation first. If it takes $< 5\text{ ms}$, a cache is tech debt, not an optimization." 4. **"Let's add a configuration flag" (Passing Design Decisions to Users)**: - *Uno-Reverse Response*: "Make the right design decision in the code. Every configuration flag is an abdication of architectural responsibility." 5. **"Let's spawn an agent swarm for this" (Multi-Agent Vanity)**: - *Uno-Reverse Response*: "If the steps are sequential, write a 15-line deterministic script. Keep agents for non-deterministic reasoning."
Technical details
- Version
- 1.0.0
- License
- Apache-2.0
- Last updated
- Aug 24, 2026
- Published
- Aug 24, 2026
Decision snapshot
Fallback candidate
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 69/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- Success rate
- —
- Recent failure
- —
- Outcomes
- 0
- Output quality
- —
- Failed
- 0
- Not relevant
- 0
- Installs
- 0
- Risk blocked
- 0
- Setup needed
- 0
- Production
- 0
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Add to agent workflow
Free and open source. Review the report before installing into production agents.
Growth loop
Share kit
Scenario-led draft for uno-reverse, ready for a manual X post.
A practical pick for design or creative work: uno-reverse: Radical simplification, red-teaming, and Occam's Razor devil's advocate for software architecture, PRDs, agent workflows, a... 16 stars https://www.openagentskill.com/skills/danicat-uno-reverse?ref=x
Optional reply with install command
Listing + install path for uno-reverse: https://www.openagentskill.com/skills/danicat-uno-reverse?ref=x Install: npx skills add danicat/skills --skill uno-reverse
Listing source
Registry indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- danicat
- Source
- danicat/skills
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
This Registry indexed listing is attributed to danicat but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Add the evidence badges to your README
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/danicat-uno-reverse)
[](https://www.openagentskill.com/skills/danicat-uno-reverse)
[](https://www.openagentskill.com/skills/danicat-uno-reverse/audit)
[](https://www.openagentskill.com/skills/danicat-uno-reverse)Author
danicat
@danicat
Tags
Platform fit
Health signals
- GitHub stars
- 16
- Quality score
- 32/100
- Last GitHub push
- Aug 23, 2026
- Framework hints
- Unknown
- OpenAgentSkill views
- 1
- Install copies
- 0
- Outbound clicks
- 0
Community signal
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Trust & safety
Do not auto-install
- GitHub adoption16 GitHub starsFIX
- Stars/forks activity16 stars, 3 forks; issue activity unavailable in current metadataFIX
- Recent maintenancePushed todayPASS
- License clarityApache-2.0PASS
- README/SKILL.md completenessPublic metadata needs stronger README/SKILL.md contextINFO
- Dependency/runtime riskcommand execution surface, credential or environment accessCHECK
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