Agent skill

Skill Decision Support

by nyldn in nyldn/claude-octopus

Present options with trade-offs for informed decision-making — use when choosing between approaches

MITAuto-check passed

Install Skill Decision Support

skills CLI
$ npx skills add nyldn/claude-octopus --skill skill-decision-support -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install nyldn/claude-octopus skill-decision-support --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/nyldn/claude-octopus.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/skill-decision-support .claude/skills/skill-decision-support && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
skill-decision-support
GitHub stars
4.2k
Used in
1 other repo
Token cost
~2.6k tokens
SKILL.md length
510 words
Files
2
Skills in repo
62
Repo updated
First seen
Licence
MIT

At a glance

Present options with trade-offs for informed decision-making — use when choosing between approaches

  • Works in 9 steps: Context Understanding → Generate Options → Trade-off Analysis → …
  • Choosing between approaches
  • SKILL.md covers Overview, When to Use, The Process and Common Patterns, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Skill Decision Support is an agent skill from nyldn/claude-octopus. Present options with trade-offs for informed decision-making — use when choosing between approaches

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

The repository describes itself as: Run multiple AI models against the same research, design, or coding task. Surface disagreements before you ship. The licence is MIT.

When your agent uses it

  • Choosing between approaches

Example prompts

  • “/skill-decision-support”

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Context Understanding
  2. Generate Options
  3. Trade-off Analysis
  4. Present Options
  5. Support the Choice
  6. Tailor to User's Needs
  7. Quantify When Possible
  8. Be Honest About Unknowns
  9. Provide "Escape Hatch"

What it can do on your machine

Read from SKILL.md and the folder at commit e14b84f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Skill Decision Support loads about 2.6k tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 510 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~31
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from nyldn/claude-octopus at commit e14b84f, republished under its MIT licence (© nyldn). 510 words, ~2,580 tokens.

Download SKILL.mdSave it as .claude/skills/skill-decision-support/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
skill-decision-support
description
Present options with trade-offs for informed decision-making — use when choosing between approaches
disable-model-invocation
true

Host: Codex CLI — This skill was designed for Claude Code and adapted for Codex. Cross-reference commands use installed skill names in Codex rather than /octo:* slash commands. Use the active Codex shell and subagent tools. Do not claim a provider, model, or host subagent is available until the current session exposes it. For host tool equivalents, see skills/blocks/codex-host-adapter.md.

Decision Support & Options Presentation

Overview

Structured approach to presenting options and alternatives with clear trade-offs, enabling informed decision-making.

Core principle: Understand context → Generate options → Analyze trade-offs → Present clearly → Support choice.

When to Use

Use this skill when user:

  • Asks for options or alternatives
  • Says "fix or provide options"
  • Needs help deciding between approaches
  • Wants to see different ways to solve a problem
  • Is uncertain about best path forward

Do NOT use for:

  • General research ("what is X?") → use flow-probe
  • Implementation work → use flow-tangle
  • Simple yes/no questions
  • Already-decided approaches

The Process

Phase 1: Context Understanding
Step 1: Understand the Decision Point
markdown
**Decision Context:**

What needs to be decided: [the core question]
Why it matters: [impact of this decision]
Constraints: [time, resources, compatibility, etc.]
Current state: [what exists now]
Step 2: Gather Requirements

Use AskUserQuestion if needed to understand:

  • Must-have requirements
  • Nice-to-have features
  • Deal-breakers
  • Timeline constraints
  • Budget/resource constraints
Phase 2: Generate Options
Step 1: Identify Viable Approaches

Generate 2-4 distinct options (not just variations):

Option TypeWhen to Include
ConservativeLow risk, proven approach
ModerateBalanced risk/reward
InnovativeHigher risk, potentially better outcome
MinimalSimplest possible solution

Don't generate options that:

  • Violate stated constraints
  • Are clearly inferior to others
  • Are essentially the same with minor tweaks
Step 2: Research Each Option

For each option, understand:

  • How it works
  • What it requires
  • What the outcome looks like
  • What could go wrong
Phase 3: Trade-off Analysis

For each option, analyze:

markdown
### Option N: [Name]

**Description:**
[1-2 sentence description]

**Pros:**
- ✅ [Advantage 1]
- ✅ [Advantage 2]
- ✅ [Advantage 3]

**Cons:**
- ❌ [Disadvantage 1]
- ❌ [Disadvantage 2]
- ❌ [Disadvantage 3]

**Effort:** [Low/Medium/High]
**Risk:** [Low/Medium/High]
**Reversibility:** [Easy/Moderate/Difficult to undo]

**Best for:** [when this option makes sense]
Phase 4: Present Options
Format for Presentation
markdown
# Decision: [What needs to be decided]

**Context:** [Brief summary of why this decision is needed]


## Option 1: [Conservative/Proven Approach] ⭐ (Recommended)

**What it is:**
[Clear explanation in 1-2 sentences]

**Pros:**
- ✅ [Pro 1]
- ✅ [Pro 2]
- ✅ [Pro 3]

**Cons:**
- ❌ [Con 1]
- ❌ [Con 2]

**Implementation:**
[Brief overview of what's involved]

**Timeline:** [estimate]
**Risk Level:** Low/Medium/High


## Option 2: [Alternative Approach]

[Same structure as Option 1]


## Option 3: [Another Alternative]

[Same structure as Option 1]


## Recommendation

**I recommend Option [N]: [Name]**

**Why:**
1. [Reason 1]
2. [Reason 2]
3. [Reason 3]

**This option is best because:** [summary of key advantage relative to context]


## Quick Comparison

| Criteria | Option 1 | Option 2 | Option 3 |
|----------|----------|----------|----------|
| Effort | [level] | [level] | [level] |
| Risk | [level] | [level] | [level] |
| Reversible | [yes/no] | [yes/no] | [yes/no] |
| Timeline | [time] | [time] | [time] |
| Best for | [scenario] | [scenario] | [scenario] |


**Which option would you like to proceed with?**
Guidelines for Presentation
  1. Mark recommendation clearly with ⭐ or "(Recommended)"
  2. Limit to 2-4 options (too many = decision paralysis)
  3. Be honest about cons (don't oversell any option)
  4. Make comparison easy (use consistent structure)
  5. Support with reasoning (explain why recommendation makes sense)
Show full SKILL.md (192 more words)Show less
Phase 5: Support the Choice

After user chooses:

markdown
✅ **Proceeding with Option [N]: [Name]**

**Next steps:**
1. [Step 1]
2. [Step 2]
3. [Step 3]

**I'll now [begin implementation / gather more details / create plan].**

If user asks for more info on a specific option:

markdown
**Deep dive on Option [N]:**

**How it works:**
[Detailed explanation]

**Implementation steps:**
1. [Detailed step 1]
2. [Detailed step 2]
...

**Potential issues and mitigations:**
- Issue: [potential problem]
  Mitigation: [how to handle it]

**Would you like to proceed with this option?**

Common Patterns

Pattern 1: "Fix or Provide Options"
User: "The auth system is broken. Fix or provide options."

Response structure:
1. Assess: Is the fix obvious or are there choices?
2. If obvious: Present the fix + ask for confirmation
3. If choices: Present options with trade-offs

Example:

I can see the auth issue. Here are your options:

**Option 1: Quick Fix** (Recommended for immediate deployment)
- Patch the current JWT validation
- Pros: Fast, low risk
- Cons: Doesn't address underlying architecture

**Option 2: Comprehensive Refactor**
- Rebuild auth with better patterns
- Pros: Long-term solution
- Cons: Takes longer, more testing needed

**Which approach fits your timeline?**
Pattern 2: "What Are My Options"
User: "I need to add caching. What are my options?"

Response structure:
1. Understand requirements (what needs caching, scale, etc.)
2. Generate 3-4 caching options
3. Present with trade-offs
4. Recommend based on their context

Example:

For caching your API responses, here are the main options:

**Option 1: In-Memory Cache (Node-cache)** ⭐
- Best for: Single-server setup
- Pros: Simple, fast, no external dependencies
- Cons: Lost on restart, doesn't scale across servers

**Option 2: Redis**
- Best for: Multi-server or persistence needed
- Pros: Persistent, distributed, feature-rich
- Cons: Requires Redis server, more complex

**Option 3: CDN Caching (Cloudflare/Fastly)**
- Best for: Static/semi-static content
- Pros: Global distribution, handles scale
- Cons: Less control, costs, public content only

**Based on your single-server setup, Option 1 is recommended.**
Which would you like to use?
Pattern 3: "Help Me Decide"
User: "Should I use TypeScript or stick with JavaScript? Help me decide."

Response structure:
1. Clarify decision factors (team size, project scope, timeline)
2. Present structured comparison
3. Make recommendation based on their specific context
4. Support whatever they choose

Example:

Let me help you decide between TypeScript and JavaScript.

**TypeScript:**
Pros: Type safety, better tooling, catches errors early
Cons: Learning curve, build step, more setup
Best for: Larger teams, long-term projects, complex domains

**JavaScript:**
Pros: Simpler, no build step, familiar to all devs
Cons: Runtime errors, less IDE support, harder to refactor
Best for: Small projects, prototypes, simple applications

**For your [context]:** I recommend TypeScript because [reason].

Would you like to proceed with TypeScript, or would JavaScript be better for your needs?

Integration with Other Skills

With flow-probe
Need to research options thoroughly?
→ Use flow-probe to gather information
→ Use skill-decision-support to present findings as options
With flow-tangle
User chose an option?
→ Use flow-tangle to implement the chosen approach
With skill-debug
Bug could be fixed multiple ways?
→ Use skill-decision-support to present fix options
→ Use skill-debug to implement chosen fix systematically

Best Practices

1. Tailor to User's Needs

Ask about constraints:

markdown
Before presenting options, I need to understand:
- Timeline: How urgent is this?
- Resources: What's available (team size, budget, infrastructure)?
- Risk tolerance: Is this production-critical or experimental?
- Reversibility: Must this decision be reversible?
2. Quantify When Possible

Good:

**Timeline:**
- Option 1: 2-3 hours
- Option 2: 1-2 days
- Option 3: 1 week

Poor:

**Timeline:**
- Option 1: Quick
- Option 2: A while
- Option 3: Longer
3. Be Honest About Unknowns
**Option 2: Microservices Architecture**

⚠️ **Unknown:** Migration effort could be 2-4 weeks depending on current coupling.
Would need to audit codebase to give accurate estimate.
4. Provide "Escape Hatch"

Always include:

**Not satisfied with these options?**

I can also:
- Research more alternatives
- Combine aspects of multiple options
- Deep-dive on any specific approach
- Prototype a solution to test viability

Red Flags - Don't Do This

ActionWhy It's Wrong
Only present one "option"That's not a choice
Present 8+ optionsDecision paralysis
Hide significant consUser can't make informed choice
Recommend without reasoningUser can't evaluate recommendation
Ignore stated constraintsWasting user's time
Present obviously bad options as viableUndermines trust

Quick Reference

User RequestAction
"fix or provide options"Assess if fix obvious → If yes: present fix, if no: present options
"what are my options"Understand context → Generate 2-4 options → Present with trade-offs
"help me decide"Clarify decision factors → Compare approaches → Recommend with reasoning
"show alternatives"Generate alternatives → Analyze pros/cons → Present structured comparison

The Bottom Line

Decision support → Clear options + Honest trade-offs + Reasoned recommendation
Otherwise → Confusion + Poor decisions + Regret

Understand context. Present real choices. Support with reasoning. Respect their decision.

© nyldn, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/skill-decision-support of nyldn/claude-octopus.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit e14b84f

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in nyldn/claude-octopus, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Skill Decision Support next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Presentationsasgeirtj/system_prompts_leaks69k—~858Automated safety check: PassCC0-1.0
Vibe-Trading Finance ToolkitHKUDS/Vibe-Trading35k—~6.5kAutomated safety check: PassMIT
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Questions about Skill Decision Support

What does Skill Decision Support do?

Present options with trade-offs for informed decision-making — use when choosing between approaches. Skill Decision Support is an agent skill from nyldn/claude-octopus.

When should I use Skill Decision Support?

Skill Decision Support fits situations like: choosing between approaches.

How do I install Skill Decision Support in Claude Code?

Run `npx skills add nyldn/claude-octopus --skill skill-decision-support -a claude-code`. Or copy the skill folder (skills/skill-decision-support in nyldn/claude-octopus) into .claude/skills/skill-decision-support in your project. Claude Code loads it when a task matches its description.

How do I install Skill Decision Support in Codex?

Run `npx skills add nyldn/claude-octopus --skill skill-decision-support -a codex`. Or copy the skill folder (skills/skill-decision-support in nyldn/claude-octopus) into .agents/skills/skill-decision-support in your project. Codex loads it when a task matches its description.

Can I use Skill Decision Support in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add nyldn/claude-octopus --skill skill-decision-support -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-decision-support, .gemini/skills/skill-decision-support, .github/skills/skill-decision-support and .opencode/skills/skill-decision-support in your project.

What does Skill Decision Support need to run?

SKILL.md names no scripts, command-line tools or credentials: Skill Decision Support is instructions for the agent only.

Does Skill Decision Support access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Skill Decision Support safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Skill Decision Support use?

Skill Decision Support is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skill Decision Support use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Skill Decision Support?

Skills that share tags, products or a category with Skill Decision Support: Options (asgeirtj/system_prompts_leaks, 69k stars), Presentations (asgeirtj/system_prompts_leaks, 69k stars), Vibe-Trading Finance Toolkit (HKUDS/Vibe-Trading, 35k stars) and Agent Trading Predictor (ruvnet/ruflo, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Decision Support?

nyldn (a GitHub user) maintains it in nyldn/claude-octopus, which has 4,192 GitHub stars. The repository holds 62 skills in this directory. The repository was last updated on October 9, 2026.

Source: nyldn/claude-octopus on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.