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Competitor Recon — DSH Plugin for DeepSeek Harness
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competitor-recon

Competitor Recon

Complaint-driven competitor research: enumerate competitors, extract features/pricing/positioning plus cited user complaints from reviews, forums, and issue trackers, cluster the complaints, and deliver a comparison table with copy-worthy and skip lists.

The plugin will be installed here. Keep web if you are unsure.

npx -y @deepseek-ai/dsh plugin --profile web add github:ChenneyZhuang/competitor-recon#26ff46f91298d8746dce84c610b9b2dd48bef270
READMECompatibilityVersions

Compatibility and provenance

Competitor Recon is published as competitor-recon and currently resolves to version 0.2.0. The Hub verifies its manifest and preserves the exact installation source for reproducible installs.

DSH compatibility
*
Runtime surfaces
any
Release source
github
Registry updated
9/14/2026

Versions

0.2.0stable
9/14/2026

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0.2.0
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License
MIT
Source
github
GitHub
★ 0
Weekly downloads
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Last push
9/14/2026
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README

competitor-recon 竞品调研

Run competitor reconnaissance before building any feature: the review sections of existing products are the cheapest requirements research that exists.

动手做功能之前先跑竞品侦察:现有产品的用户评论区,是世界上最便宜的需求调研。

Why / 为什么

Marketing pages state intentions; user reviews state outcomes. One grounded complaint is worth more than any amount of feature-page copy — and a complaint repeated by three independent users is an unmet need you can build against.

营销页写的是意图,用户评论写的是结果。一条有出处的真实抱怨,胜过整页功能文案;三个互不相识的用户抱怨同一件事,就是一个可以动手满足的未满足需求。

The method / 方法(4 步)

  1. Enumerate 3–6 competitors — search English and Chinese sources; one language samples half the market.
  2. Extract per competitor: features, pricing (numbers, not "contact us"), positioning, and user complaints with source links.
  3. Cluster complaints by the underlying need, ranked by frequency.
  4. Deliver: comparison table + copy-worthy list + skip list. A feature idea enters the build list only with a cited user complaint behind it.

Worked example — recon on an open-source ledger app (2026-09, real run)

Sources actually fetched during the run: V2EX threads (via curl), the target's GitHub issue tracker (via API — reaction counts double as frequency statistics), community forums.

Complaint clusterEvidenceVerdict
Ads / subscription inflation driving users outmultiple independent forum posts naming the incumbentsdifferentiate: stay clean, price predictably
"AI auto-capture might be wrong, I re-check manually"feature request with 👍×3 in the target's own trackerbuild: recognition → confirmation loop
Reimbursement / refund flows too complexcomplaints across two products, praise for a third'sbuild: expense-return workflow done simply
Multi-currency locked behind a paywalla long-time user defected over exactly thisskip: no demand evidence beyond the wall itself

The run produced the full comparison table plus a skip list with reasons — 3 build ideas, each cited; 3 skips, each justified.

Rules that make it work / 让它生效的规则

  • Reviews outrank marketing pages; conflicts are recorded as claims.
  • Complaint clusters are the finding — one is anecdote, three is a need.
  • Issue trackers are goldmines: a 👍 count is a frequency statistic for free.
  • Tool blocked? Switch clients, not sources (curl, public APIs). The evidence bar never drops.

Install / 安装

git clone https://github.com/ChenneyZhuang/competitor-recon ~/.claude/skills/competitor-recon

One SKILL.md. Needs only web search and any fetch client. MIT. v0.2.0 — live-tested end-to-end.

单个 SKILL.md,只需搜索与抓取能力。MIT 许可,v0.2.0,完整实测通过。