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competitor-research

Writing & research

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Updated 10-15 21:41

About this skill

Competitor Research Workflow

Single authoritative reference. Re-read before every run. Pure-SERP approach: zero RAG, zero AppMate semantic search. The script holds all the data-layer logic; Claude does keyword tokenization and a single batched LLM relevance pass.

Step 0 — Prerequisites

Run before anything else:

python3 scripts/appmate_config.py check

If exit code ≠ 0, STOP. Tell the user AppMate credentials are not configured, show the precheck output verbatim, and direct them to the appmate-setup skill. Do not invoke any other step of this skill.

One-line summary

Single app → script writes phase_a (raw metadata + primary_genre_id) → LLM tokenizes keywords → script fetches iTunes Search top-200 per token, collects rivals outranking self, aggregates, scores, hard-filters by genre+density, writes phase_b → LLM does batched relevance pass on name + description[:200] → script writes final JSON, Claude renders the markdown in the user's conversation language, pastes back into conversation.

Difference from existing skills

competitor-research (this skill) feature-ideation / growth-strategy
Role produces the competitive signal consumes it
Signal iTunes Search SERP overlap, strict outrank reuses this skill's data/competitors_<slug>.json (auto-chains this skill when the file is missing)
Output role the deliverable itself transient input evidence in their phase_a JSONs
Persistence data/competitors_<slug>.json (cached, reused) not cached

Input / Output / Trigger

Item Content
Trigger user says "find competitors for <app>" / "run <app> competitors" / invokes this skill for <app>
Input data/apps_full.json + data/sales_cache.json
Output data/phase_a_competitors_<slug>.json, data/phase_b_competitors_<slug>.json, data/competitors_<slug>.json, data/competitors_<slug>.md + Claude pastes the full markdown back into the conversation
User intervention 2 (trigger + LLM tokenize&filter, both in the same Claude turn)

Workflow (3 stages)

Stage 1 — Script: phase_a

python3 scripts/competitor_research.py analyze "<app>"

App argument: App Store ID / bundle ID / SKU / fuzzy name. Resolves via the same find_app used by other skills.

Writes data/phase_a_competitors_<slug>.json. If credentials are missing or app is not found, exits 2 with a clear message — do not proceed.

Stage 2 — Claude: tokenize keywords

Read data/phase_a_competitors_<slug>.json. Look at raw.title, raw.subtitle, raw.keywords for the main-market locale.

Tokenization rules (identical to aso-daily-report Step 2):

  • Cut real ASO words using semantic understanding of the source-locale text (Chinese, Japanese, Korean, etc.).
  • Recognize compound words (桌面便签, 云便签).
  • Reject CJK runs ≥ 6 characters (almost always invalid mashed runs).
  • Recognize brand variants, typos, and English-Chinese fusions.
  • De-duplicate case-insensitively.

Output a comma-separated token list and pass it to the script:

python3 scripts/competitor_research.py rank    "Sticky Note Pro" --tokens "便签,桌面便签,sticky note,memo"

This writes data/phase_b_competitors_<slug>.json with up to 25 candidates that survived the genre + density hard filters.

Stage 3 — Claude: batched relevance filter + render

Read data/phase_b_competitors_<slug>.json. For each candidate, look at name, description_short, and outranked_keywords[:3].

One batched judgement call. For ALL candidates in one pass, decide for each:

  • keep: true + a one-sentence reason (in the user's conversation language) explaining why the rival's target users overlap with the app's
  • keep: false + a one-sentence reason (in the user's conversation language) explaining why they do not

Example reasons (when the user is writing Chinese):

  • keep: 「XX便签」描述同样主打桌面快速记事,目标用户重叠
  • drop: 描述显示是情绪打卡 app,跟便签场景不重叠

Compose the final JSON data/competitors_<slug>.json:

{
  "app": "...",
  "app_id": "...",
  "bundle_id": "...",
  "market": "CN",
  "primary_genre_id": 6007,
  "generated_at": "...",
  "tokens": ["..."],
  "self_ranks": {"...": ...},
  "filtered": [
    {... full candidate fields from phase_b ...,
     "relevance_keep": true,
     "relevance_reason": "..."}
  ],
  "dropped_by_relevance": [
    {"itunes_id": "...", "name": "...", "threat_score": ...,
     "drop_reason": "..."}
  ]
}

filtered is sorted by threat_score desc, truncated to 10. dropped_by_relevance is diagnostic only — never rendered in markdown.

If fewer than 3 candidates pass relevance, the markdown shows an evidence-thin warning at the top.

Markdown report template (v1 — follow exactly)

Rendered in the same language the user has been using in this conversation. Default to English; if the user has been writing in Chinese / Japanese / Spanish / etc., translate the template headers, labels and prose accordingly. App Store metadata strings (title / subtitle / keywords / competitor app names) must remain in the App Store's source locale (e.g. zh-Hans names stay zh-Hans) — only the surrounding explanation follows the user's conversation language.

The template below is written with English placeholders. Substitute the equivalent words in the user's conversation language when rendering.

# 🎯 <App name> · Top competitors worth studying

> ⚠️ <evidence-thin warning — only when kept < 3>

**Main market**: <flag> <country>  ·  **30-day downloads**: <N>  ·  **Core keywords searched**: <X>

---

## 1. <Rival name> · ★<rating> (<review_count> reviews)

Outranks you on **<outrank_count> keywords**, on average **<round avg_rank_diff> places** higher

| Keyword | You | Them | Lead | Popularity |
|---|:-:|:-:|:-:|:-:|
| `<kw1>` | <#N or unranked> | **#<n>** | <diff> | <pop> <🔥 if ≥50> |
| `<kw2>` | ... | ... | ... | ... |
| `<kw3>` | ... | ... | ... | ... |

> **Why this one**: <relevance_reason — one sentence in the user's conversation language>

---

## 2. <Rival name> · ...
... (5–10 rivals total, top 3 keywords each) ...

---

**Top <N>**: #X / #Y / #Z — <one-sentence summary of each top rival's core threat>

Want a deeper look at one rival's keyword layout? Tell me the number and I can pull their metadata via the `aso-optimize` skill for side-by-side comparison.

Empty-state template (when len(filtered) == 0)

When the LLM relevance pass keeps zero candidates, do NOT render the per-rival ## blocks. Instead, render exactly this (translate headers/labels into the user's conversation language):

# 🎯 <App name> · Top competitors worth studying

> ⚠️ No qualifying rivals · this app has no same-category rivals on its own keyword SERPs that meet the `MIN_OUTRANK_COUNT = 3` threshold.

**Main market**: <flag> <country>  ·  **30-day downloads**: <N>  ·  **Core keywords searched**: <X>

Likely causes (ordered by likelihood):

1. **Too few keywords**: at least 3 valid tokens are required for any rival to pass the density threshold. Check whether `raw.keywords` in `phase_a_competitors_<slug>.json` is empty or has only 1-2 tokens.
2. **The app is the leader of this niche**: no rival outranks you ≥ 3 times across your own SERP.
3. **Category mismatch**: your `primary_genre_id` does not match any candidate rival — common for niche keywords with cross-category mixing.

To dig deeper, run `python3 scripts/competitor_research.py show-b "<app>"` to inspect the phase_b candidate pool (the pre-filter detail).

10 inviolable rules

  1. Each rival is its own H2 (##) block — low-density layout.
  2. Keywords wrapped in backticks `桌面便签`.
  3. Column headers are full words in the user's conversation language. Never use single-letter abbreviations T/S/K/X.
  4. The "Why this one" line uses a > blockquote (translate the label to the user's conversation language).
  5. The keywords table shows exactly top 3 outranked keywords per rival — full list is in JSON.
  6. dropped_by_relevance never appears in markdown. JSON only.
  7. Sort rivals by threat_score descending.
  8. Closing "Top N" + "deeper look at one rival" guidance is required (translated to the user's conversation language).
  9. Paste the full markdown back into the conversation. "Saved to data/competitors_.md" alone is not allowed.
  10. Empty-state: when len(filtered) == 0, render the Empty-state template above (no ## rival blocks); never invent placeholder rivals.

Data source conventions

Dimension Source
Pick app data/apps_full.json via aso_optimize_v2.find_app
Main market the country with the largest 30-day downloads in sales_cache.json
primary_genre_id iTunes Lookup, cached in data/itunes_lookup_cache.json (no TTL)
SERP top-200 per token iTunes Search API (https://itunes.apple.com/search), cached in data/serp_details_cache.json
Keyword popularity keyword_local.lookup_popularity (static keyword_reference_<region>.json)
Tokenization LLM semantic split (not regex / jieba)
Relevance filter LLM batched call over name + description[:200]

Key parameters

Parameter Value Note
SERP_LIMIT 200 top-N per iTunes Search call
MIN_OUTRANK_COUNT 3 candidate must outrank on ≥ this many tokens
MAX_CANDIDATES_BEFORE_LLM 25 phase_b truncates to this
DESCRIPTION_TRUNCATE 200 chars shown to LLM per candidate
TOP_N_RIVALS 10 upper bound on filtered
MIN_RIVALS_FOR_REPORT 3 below this, ⚠️ evidence-thin warning
TOP_K_KEYWORDS_PER_CARD 3 per-card keyword table size in markdown

CLI

python3 scripts/competitor_research.py analyze "Sticky Note Pro"
python3 scripts/competitor_research.py rank    "Sticky Note Pro" --tokens "便签,桌面便签,sticky note,memo"
python3 scripts/competitor_research.py show-a  "Sticky Note Pro"
python3 scripts/competitor_research.py show-b  "Sticky Note Pro"

Connection to existing workflows

  • An aso-daily-report run that finds an app dropping out of top 20 on its own keyword → trigger this skill to see who took the slot.
  • Use the resulting "Top N" rivals to seed a follow-up aso-optimize skill run for <app> for keyword reshuffling.
  • No downstream skill consumes competitors_<slug>.json yet — wiring feature-ideation / growth-strategy is a separate task (see spec §15).

Known limits

  • LLM tokenization cannot run unattended (cron-incompatible).
  • SERP changes hourly; the 7-day rank cache may be stale on borderline rivals.
  • An app with ≤ 2 distinct tokens cannot reach MIN_OUTRANK_COUNT = 3 → empty result.
  • Drop reasons in dropped_by_relevance vary across runs on borderline cases (audit via the JSON).

Checklist (must pass before pasting back)

Content

  • Main list of 5–10 rivals (≥3 to skip warning, else show ⚠️)
  • Each card shows exactly top 3 keywords
  • Sorted by threat_score descending

Language

  • Single-language throughout the rendered output (matches the user's conversation language)
  • No single-letter abbreviations T/S/K/X
  • No technical jargon (no "SERP" / "RAG" / "outrank" in user-visible text)
  • Keywords wrapped in backticks

Structure

  • Each rival uses ## N. <name>
  • The "Why this one" label uses > blockquote
  • dropped_by_relevance NOT rendered
  • Closing "Top N" + "deeper look at one rival" line present

Delivery

  • data/competitors_<slug>.json written
  • data/competitors_<slug>.md written
  • Full markdown pasted back into the conversation (not just "saved")