Web・iOS・Androidの画面を、読み上げやキーボード操作に対応させ、ラベル、配色、操作対象の大きさなどをWCAG 2.2に沿って設計・点検するスキル。
- アイコンボタンの説明を付けたいとき
- キーボード操作とモーダルの点検
- コントラストや操作対象の大きさの確認
自社の強みや顧客像を基に、比較すべき競合と調査先を選びます。直接競合・周辺競合・目標となる企業に分類し、根拠付きの候補一覧を作って詳しい比較の準備を整えます。
Use when scoping a competitive landscape — identifying, categorising, and score-filtering a competitor set before any benchmarking begins. Decides who counts as a competitor, which tier they belong to, and which sources to mine. First step in the three-skill competitive pipeline; precedes benchmark-methodology.
競合比較を始める前に、比較対象となる企業と調査先を選びます。自社の提供内容、顧客像、差別化の根拠を整理し、規模・専門領域・市場・ブランドの特徴などで候補を分類します。直接競合、部分的に重なる周辺競合、目標となる企業の3層に分け、独自性や商業面の信頼性を基に候補を絞ります。成果物は、分類と出典リンクを付けた競合一覧です。
競合が多く、誰を詳しく調べるべきか決めにくいときに向いています。デザイン、映像、コピーライティングなどのサービス市場で、自社の立ち位置に合う調査範囲を定めたい場合にも使えます。候補は通常10〜18件から、詳しく比較する8〜12件へ絞ります。
自社の立ち位置をまとめた資料が前提です。資料がない場合は聞き取りから始めます。競合の属性は少なくとも2つの情報源で確認します。このスキルが扱うのは候補選定で、詳しい比較評価は次のbenchmark-methodologyに引き継ぐ構成です。
この紹介文は、公開されている SKILL.md をもとに AI(Claude Haiku)が作成しました。正確な仕様は下の原文を確認してください。
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this skill to decide who to benchmark and where to find them before any scoring begins. A competitive analysis is only as good as its frame: the wrong set makes the client look either unbeatable or doomed. The goal is a defensible, decision-relevant set — not an exhaustive census.
Before scoping the set, establish the client's positioning brief. If you don't already have it, run a short brand-discovery interview to elicit it — do not invent one and do not scope the set blind. The brief supplies:
Do not proceed without the positioning brief. A competitor list scoped without the client's lens is noise, not intelligence. The scoping consequence in particular determines which competitors are strong rivals (those that contest the client's moat) vs. merely overlapping on service menu.
For each candidate, capture these axes — they decide both inclusion and tier:
Don't sort competitors into niche-specific buckets; sort them along a few generic axes so the landscape isn't skewed toward one archetype. These axes apply to any creative-service market (design, motion, copywriting, branding, content, film, etc.). Aim for breadth across each axis first, then prune to the most instructive.
Plot each candidate on the relevant axes; a competitor is direct when it sits near the client on positioning, specialization, size, and market at once.
Group the final set into three tiers — this structure carries through to the report:
Match the source to the dimension you need. The platform types below are generic; substitute the ones native to the client's niche (e.g. Dribbble/Behance for design, showreel/Vimeo for motion, writing samples/published work for copy):
Always verify claims across at least two sources before treating a competitor attribute as fact (self-reported site copy ≠ verified outcome). Carry an adversarial-verification discipline into every profile.
A lightweight pre-filter to decide who graduates into full benchmarking. Score 1–5; keep candidates that score high on either distinctiveness or credibility — the client's strategic tension means both poles are instructive.
| Candidate | Positioning stance | Specialization | Size band | Tier | Offer overlap (1–5) | Distinctiveness (1–5) | Commercial credibility (1–5) | Craft proximity (1–5) | Include? |
|---|
Rules of thumb (apply per the client's scoping consequence in the positioning brief):
A scoped, tiered competitor set (typically 10–18 candidates → 8–12 profiled),
each tagged with its axis positions, tier, and source links, ready to hand to
benchmark-methodology.
Scenario: A boutique brand-identity studio (2-person, EU-remote, productized sprints, contrarian/manifesto-driven aesthetic) wants to scope its competitive set before benchmarking. The strategic tension from the positioning brief is memorability × hireability.
Step 1 — eight-axis population (sample candidates):
| Candidate | Positioning stance | Specialization | Size band | Engagement | Distinctiveness | Evidence model | Brand strength | Market |
|---|---|---|---|---|---|---|---|---|
| Studio A | brand-led / editorial | identity only | micro | productized | contrarian | aesthetic-led | cult | global-remote |
| Studio B | capability-led | broad DS+motion | boutique | bespoke | conventional | outcome-led | interchangeable | US |
| Agency C | capability-led | brand+digital | mid-size | retainer | conventional | outcome-led | interchangeable | EU |
| Freelancer D | brand-led | brand voice only | solo | day-rate | editorial | aesthetic-led | ownable | global |
| Studio E | brand-led | brand strategy | micro | productized | manifesto-driven | outcome-led | cult | EU-remote |
Step 2 — pre-filter scoring (client scoping consequence: weight distinctiveness because the client's moat is POV-first, not capability breadth):
| Candidate | Offer overlap (1–5) | Distinctiveness (1–5) | Commercial credibility (1–5) | Craft proximity (1–5) | Tier | Include? |
|---|---|---|---|---|---|---|
| Studio A | 5 | 5 | 3 | 5 | Direct | ✓ must-profile |
| Studio B | 3 | 2 | 5 | 3 | Adjacent | ✓ credibility anchor |
| Agency C | 2 | 1 | 5 | 2 | Aspirational | ✓ scale reference |
| Freelancer D | 4 | 4 | 2 | 4 | Direct | ✓ cautionary case |
| Studio E | 5 | 5 | 4 | 4 | Direct | ✓ must-profile |
Step 3 — output handed to benchmark-methodology:
Five candidates (3 Direct, 1 Adjacent, 1 Aspirational), each tagged with
axis positions, tier, and source links. Studio A and Studio E are the
sharpest head-to-head rivals; Freelancer D is the "memorable but
un-hireable" cautionary case to learn from.
brand-discovery — use first to establish the positioning brief and strategic tension that scopes the competitor set.benchmark-methodology — the next step; takes the tiered set and scores each competitor across nine dimensions.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Web・iOS・Androidの画面を、読み上げやキーボード操作に対応させ、ラベル、配色、操作対象の大きさなどをWCAG 2.2に沿って設計・点検するスキル。
AIエージェントの不調を、指示・記憶・ツール実行・画面表示など12の層から調べるスキル。コードやログを根拠に原因を整理し、重要度順の指摘と修正案をまとめます。
実際の開発課題で複数のコーディングエージェントを比較するスキル。成功率、取得可能なAPI費用、所要時間、繰り返し実行の安定性を測り、選定や更新後の評価に使えます。
AIエージェントが使うツールの種類や入出力、エラーからの復帰手順を設計・見直します。文脈の情報量も整理し、作業完了率や再試行回数で改善を評価します。
AIエージェントの失敗や同じ操作の繰り返しを記録し、原因の切り分け、小さな復旧操作、結果の報告まで進める手順を示して、根拠のある再試行につなげるスキル。
AIエージェントが失敗や同じ操作を繰り返す原因を、エラーと実行状況から整理します。小さな復旧操作を試し、結果と根拠を引き継げる報告にまとめるスキルです。