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Skill

/feasibility-study

Feasibility analysis from first principles. Use when: evaluating solutions before tech-spec, comparing approaches, risk assessment. Not for: implementation (use feature-dev), architecture advice (use codex-architect). Output: quantitative comparison + recommendation.

From plugin
sd0x-dev-flow
18899 skills16 agents5 hooks
Install
$ npx -y skills add sd0xdev/sd0x-dev-flow --skill feasibility-study --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/feasibility-study

Context preview

The summary Claude sees to decide when to auto-load this skill.

Feasibility analysis from first principles. Use when: evaluating solutions before tech-spec, comparing approaches, risk assessment. Not for: implementation (use feature-dev), architecture advice (use codex-architect). Output: quantitative comparison + recommendation.

SKILL.md

feasibility-study.SKILL.md
name: feasibility-study
description: "Feasibility analysis from first principles. Use when: evaluating solutions before tech-spec, comparing approaches, risk assessment. Not for: implementation (use feature-dev), architecture advice (use codex-architect). Output: quantitative comparison + recommendation."
allowed-tools: Read, Grep, Glob, Bash(git:*), Bash(bash:*), Write, Agent, Bash(node:*)

Feasibility Study Skill

Supplementary Agent

For each solution option, dispatch background exploration:

Agent({ description: "Explore feasibility of solution option", subagent_type: "feasibility-analyst", prompt: `Research the feasibility of: <solution description> Evaluate technical feasibility, effort, risk, extensibility, and maintenance cost.` })

Trigger

  • Keywords: feasibility, is this possible, can we, should we, explore options, before tech spec

When NOT to Use

  • Already have a tech spec (use `/deep-analyze`)
  • Need implementation, not analysis (use `/codex-implement`)
  • Quick question (use `/codex-explain` or `/codex-architect`)

Workflow

Resolve → Decompose → Constraints → Code research → Solutions → Codex discussion → Decision → Report

Phase 0: Resolve the feature context

The sets Phase 1 reads have to come from somewhere, and this skill is invoked directly (`/feasibility-study <topic>`) as often as it is invoked from another skill — so it resolves them itself rather than assuming a caller supplied them:

# The node entrypoint. The shell wrapper's own header tells skills to prefer this one once they hold
# `Bash(node:*)` — and this skill now does, for its transport dispatches. The wrapper was named here
# only while that grant was absent; keeping it afterwards would have instructed a second-choice
# entrypoint for a reason that had stopped being true.
node scripts/resolve-feature.js [--feature <key>]

The reply is one JSON document. What this skill reads out of it: `scan_error` **first** — the gate below decides whether anything else in the payload means what it says — then `current_authority` (what the system does today) and the `design_records` entries — design records carry the *rationale*; it is the `type: requirements` subset of them that states what was **asked for**, which is why Phase 1 filters before it selects. Each entry is `{ file, type, namespace, confidence, is_canonical, role }`, `file` relative to `docs_path`. An empty or non-JSON reply is a failure too — `node` may be unavailable, which the shim cannot report as a payload. Treat it exactly as `scan_error !== false` below.

Phase 1: Requirement Decomposition

**Input source priority**: 1. If a requirements doc resolves from `design_records` → consume as the authoritative statement of what was **asked for**, validate via 5-Why. It is a design record, not a description of current behaviour: for "what does the system do today", read code, `rules/` and `current_authority` 2. Otherwise → extract requirements from user input via 5-Why analysis

**`design_records` is an array**, so "a requirements doc" needs a rule rather than an assumption — a split or variant-backed phase contributes more than one. Filter to `type: requirements` first, then:

| # | Candidates (`design_records` where `type: requirements`) | Result | |---|--------------------------------------------------------|--------| | 1 | none | Path 2 — extract from user input. A feature with no requirements doc is a normal state, not an exit | | 2 | exactly one | that one | | 3 | two or more, exactly one with `is_canonical: true` | that one | | 4 | two or more, and none or several canonical | **Gate: Need Human**, naming the candidates |

Rows 1 and 4 are different answers: "there is none" is acted on, "there are two" must not be resolved by picking. The same order `/architecture` applies to its tech-spec candidates.

> **`scan_error` gate.** Gate on **`scan_error !== false`**, not on `scan_error === true`. When it > is not exactly `false` the four source sets are **unknown, not empty** — the corpus could not be > enumerated (unreadable directory, broken taxonomy, no repository), *or* the resolver never ran > and a shell fallback supplied a payload with no such field at all. `{}` is the shape that made > the stricter test useless: it has no `scan_error`, so `=== true` is false and the gate passes a > payload that contains nothing. Do not proceed as though the feature has no authority documents — > report and take the ⚠️ Need Human exit. A `key` may still be present, so a non-null `key` is not > evidence the sets are complete.

Use "5 Why" to uncover essence: 1. Surface requirement (what user asks for) 2. Underlying problem (why they need it) 3. Success criteria (quantifiable acceptance)

Phase 2: Constraint Analysis

Inventory constraints by type (Technical, Business, Resource, Compatibility) with flexibility rating.

Phase 3: Code Research

Research existing codebase:

  • Related modules and reusable logic
  • Existing design patterns
  • Tech debt to work around

Phase 4: Solution Exploration

Brainstorm 2-3+ solutions, each with: 1. Core idea (one sentence) 2. Implementation path 3. Quantified feasibility (see `references/analysis-phases.md`) 4. Cost and trade-offs

Phase 5: In-Depth Codex Discussion

**⚠️ Core step — not optional (unless `--no-codex`) ⚠️**

See `references/codex-discussion-guide.md` for full rules and examples.

| Tool | Purpose | When | |------|---------|------| | `/codex-brainstorm` | Enumerate all options | At start | | `/codex-architect` | Evaluate design | After proposal forms | | `@skills/codex-code-review/references/codex-transport.md` § Resume | Ask details | Anytime |

Phase 6: Comparative Decision

Side-by-side comparison → recommendation + backup + open questions.

Evaluation Dimensions

| Dimension | Green | Yellow | Red | | --------------------- | ----- | ------ | --- | | Technical Feasibility | Has existing patterns | Needs adaptation | Major

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