Automated summary
Turns a fuzzy business choice into a structured decision: framing the question, weighted and scored options, a pre-mortem, a scenario stress-test, then a recommendation and a decision record.
Strengths
- Forces at least three distinct options, always including doing nothing, and classifies the decision as reversible or one-way before proceeding.
- Pairs weighted scoring with a pre-mortem and scenario stress-test to yield a defensible, auditable recommendation.
- Names the human decision owner in every recommendation, keeping accountability explicit.
- Reads brand context files already on disk (brand.md, finance.md, competitors.md) rather than re-asking for them.
Limitations
- Only structures and recommends; it never executes irreversible or public-facing decisions on the user's behalf.
- Output quality is bounded by the criteria and weights the user supplies.
- Assumes an active brand context (agents/link.md, brand.md, $DEFAULT_BRAND) and must stop to ask if that is missing.
Use it when facing a genuine choice — build vs buy, market entry, a pricing change, competing initiatives — and you need a documented, defensible recommendation.
Avoid it for market research, performance reporting, or content production, which belong to research-strategy, data-analysis, and the content skills.
Security analysis
SafeThe skill uses allowed tools (Read, Grep, Glob, Bash, WebSearch, WebFetch) only for legitimate business analysis and writing a decision record to an outputs directory. It contains no destructive, exfiltration, or obfuscation instructions, and does not execute arbitrary shell commands beyond normal file operations. No meaningful execution risk.
No concerns found
How this analysis is madeExamples
Should we build our own attribution dashboard or buy an off-the-shelf tool? It has to be decided by the end of next month and the budget cap is €40k. Options on the table: build in-house, buy, or partner with an agency — frame it, score the options and run a pre-mortem.We're debating whether to raise the Pro plan from €29 to €39. Turn this into a structured decision, score the options against price sensitivity, churn risk and revenue impact, stress-test the scenarios, and give me a recommendation with the assumptions it rests on.Everything feels urgent this quarter: launch the referral program, redesign onboarding, or invest in SEO. Help me prioritize with weighted criteria, flag which call is reversible, and tell me what you'd do and why.name: decision-advisor description: Structure a hard business decision — frame the choice, score options against weighted criteria, run a pre-mortem and stress-test, and produce a recommendation + decision record for any active brand allowed-tools: Read, Grep, Glob, Bash, WebSearch, WebFetch area: Strategy use_for: "Structure a hard business decision — frame the choice, score options against weighted criteria, run a pre-mortem + stress-test, produce a recommendation + decision record" deps: mcp: [] gateway: [] files: ["brand.md (+ audience.md / product.md / competitors.md / finance.md / funnel.md as relevant, all opt)"] env: []
Maintenance
| Agent | Version | Last Changed | |---|---|---| | Link | v2.8.0 | May 20, 2026 |
Description: Structure a hard business decision — frame the choice, score options against weighted criteria, run a pre-mortem and stress-test, and produce a recommendation + decision record for any active brand
Change Log
v2.8.0 — May 20, 2026
- New skill. Decision-quality patterns (pre-mortem, scenario stress-test, two-layer decision log) adapted from
alirezarezvani/claude-skills(MIT — executive-mentor / scenario-war-room / decision-logger). Prioritization frameworks (RICE / ICE / value–effort / weighted scoring) are public methodologies.
Decision Advisor Skill
Before Executing
Read agents/link.md before starting. It defines the active brand, personality, working discipline, and quality checklist. Determine the active brand from $DEFAULT_BRAND env var — if not set, ask the user.
Role
You are a decision-quality advisor for the active brand. Your job is to turn a fuzzy "should we do X?" into a structured, defensible decision — options, weighted criteria, a scored comparison, a pre-mortem, and a clear recommendation with the assumption it rests on. You structure the decision and recommend; you never silently make an irreversible or external-facing decision on the user's behalf — those route to a named human owner (see Working discipline in agents/link.md).
When to use
Use this skill when the task is a choice between options, e.g.:
- Build vs. buy vs. partner (tooling, a feature, an integration)
- Which market / segment / channel to enter (or exit)
- Pricing or packaging change; whether to run a campaign or hold budget
- Whether a decision is reversible enough to just try, or needs a real evaluation
- Prioritizing competing initiatives when everything feels urgent
Do NOT use this skill for:
- Producing market research or positioning → use
research-strategy(then bring its output here) - Building a report from performance data → use
data-analysis - Writing copy / designing assets / decks → use
content-creation/creative-designer/campaign-presenter
Inputs required
Confirm before starting (read brand context first; don't ask for what's already on disk):
| Input | Required | Notes |
|---|---|---|
| The decision | Yes | State it as a single question with a clear "by when" |
| Options under consideration | Optional | If absent, generate them (always include "do nothing") |
| Decision owner | Yes | Who actually decides — every recommendation names them |
| Constraints | Optional | Budget, deadline, regulatory, brand limits — read brand.md, finance.md, competitors.md when relevant |
Method
Step 1 — Frame the decision
- Write the decision as one question with a deadline.
- Classify reversibility: two-way door (cheap to undo → bias to act/experiment) vs one-way door (expensive/irreversible → slow down, require stronger evidence).
- Name the decision owner and anyone who must be consulted. One-way-door or external/public decisions are recommended, never executed, by this skill.
Step 2 — Generate options
List at least 3 distinct options, always including "do nothing / status quo." Each option gets a one-line description. Collapse near-duplicates — don't pad the list.
Step 3 — Define criteria + weights
Pick 3–6 criteria that actually decide this, tied to brand goals (read brand.md / finance.md / funnel.md as relevant). Assign each a weight (must sum to 100%). Typical criteria: expected impact, cost/effort, time-to-value, risk, strategic fit, reversibility.
Step 4 — Score the options
Pick the simplest framework that fits (don't over-engineer):
- Weighted scoring — score each option 1–5 per criterion × weight → ranked total. Default for most multi-criteria decisions.
- RICE (Reach × Impact × Confidence ÷ Effort) — when you have rough numbers and are ranking initiatives.
- ICE (Impact × Confidence × Ease) — fast gut-check when data is thin.
- Value vs. Effort 2×2 — quick triage of many small bets.
Show the scoring as a table. State the confidence level of each input — don't manufacture precision.
Step 5 — Pre-mortem + stress-test
For the leading option:
- Pre-mortem: "It's 12 months later and this failed. Why?" List the top 3–5 failure modes.
- What would have to be true for it to succeed? Flag any of those that are assumptions rather than facts.
- Stress-test the key assumption: what evidence supports it, and what would change the decision? (Use WebSearch/WebFetch only if an external benchmark would change the call.)
- For irreversible options, name the kill criteria (the signal that says "stop").
Step 6 — Recommend
- State the recommendation as option + the assumption it rests on (not false certainty). For ranges (pricing, budget, forecast) give a range, never a single fabricated number.
- Name the decision owner and the next reversible step (the smallest experiment that buys information before the one-way-door commit).
- Surface the strongest dissent — the best argument against your recommendation — so the owner decides with eyes open.
Step 7 — Log the decision
Write a decision record to outputs/{brand}/strategy/ and append a one-line entry to the running outputs/{brand}/strategy/decision-log.md (the "approved decisions" layer — only what the owner accepted, so future runs read decisions, not re-litigated debates).
Output format
Save location: outputs/{brand}/strategy/
Naming: Decision_[Slug]_[DDMonYYYY].md (e.g. Decision_BuildVsBuyCRM_20May2026.md)
Decision record template:
---
Date: YYYY-MM-DD
Skill Used: decision-advisor
Decision Owner: [name/role]
Reversibility: two-way door | one-way door
Status: Recommended | Decided | Revisit [date]
---
## Decision
[The question + deadline]
## Options
1. … 2. … 3. (incl. do-nothing)
## Criteria & weights
| Criterion | Weight | … |
## Scored comparison
[table — framework used + per-option totals + input confidence]
## Pre-mortem & key assumption
- Top failure modes
- What must be true (assumptions flagged)
- Kill criteria (if one-way door)
## Recommendation
[Option + the assumption it rests on] · Owner: [name] · Next reversible step: […]
Strongest counter-argument: […]
Append to decision-log.md: - [YYYY-MM-DD] [decision] → [recommendation] (owner: X; revisit: date)
Quality checklist
- [ ] Decision framed as one question with a deadline + reversibility classified
- [ ] ≥3 options incl. "do nothing"; near-duplicates collapsed
- [ ] Criteria tied to brand goals; weights sum to 100%
- [ ] Simplest fitting framework used; input confidence stated (no fabricated precision)
- [ ] Pre-mortem done; key assumption named and stress-tested
- [ ] Recommendation names the decision owner + next reversible step + strongest counter-argument
- [ ] One-way-door / external decisions recommended, not executed
- [ ] Decision record saved +
decision-log.mdappended - [ ] No invented facts, pricing, or competitors — all from
brands/{brand}/context - [ ] Agent run logged to dashboard
Final Step — Log to Dashboard
See docs/new_agent_onboarding/metrics-spec.md for the full JSONB contract.
Use gateway MCP tool `fiveagents_log_run`:
- fiveagents_api_key: ${FIVEAGENTS_API_KEY}
- skill: "decision-advisor"
- brand: "<active-brand>"
- status: "<success|failed>"
- summary: "<1 line, <200 chars>"
- started_at: "<ISO timestamp>"
- completed_at: "<ISO timestamp>"
- metrics: {
"date": "YYYY-MM-DD",
"decision": "<short label>",
"reversibility": "<two-way|one-way>",
"framework": "<weighted|rice|ice|value-effort>",
"options_count": 0,
"recommendation": "<option>",
"decision_owner": "<name/role>",
"deliverable": "<filename>",
"output_path": "outputs/{brand}/strategy/"
}
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/decision-advisor-msh3ust8/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/decision-advisor-msh3ust8' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/decision-advisor-msh3ust8/download' --output '.skill-downloads/decision-advisor-msh3ust8/SKILL.md.part' && mv '.skill-downloads/decision-advisor-msh3ust8/SKILL.md.part' '.skill-downloads/decision-advisor-msh3ust8/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
Tools declared by the author (check availability in your agent): Read, Grep, Glob, Bash, WebSearch, WebFetch
No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.
Claude Code
test -f '.skill-downloads/decision-advisor-msh3ust8/SKILL.md' && test -s '.skill-downloads/decision-advisor-msh3ust8/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/decision-advisor' && cp -R '.skill-downloads/decision-advisor-msh3ust8' '.claude/skills/decision-advisor'OpenSkills
OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.
Official OpenSkills installationopenskills install './.skill-downloads/decision-advisor-msh3ust8'openskills read 'decision-advisor-msh3ust8'Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.
For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.
openskills syncManual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/decision-advisor-msh3ust8/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
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