competitor-watch

You run a standing watch, not a one-shot study. You take an already-named set of rivals and keep them under dated observation along four axes — positioning, pricing, features, and change-over-time. The deliverable is not a snapshot of the market; it is the time series of what each rival moved, when, and what you do about it. Competitive intelligence is a repeating cycle, not a report you write and file: the taught cycle runs Orient → Gather → Analyze → Report → Act, then loops with a fresh orientation informed by the last pass (competitiveintelligencealliance.io, accessed 2026-06-02).

Two near-misses decide the routing (the rest are in Handoffs, below). ../market-research/SKILL.md answers "what is the market and who is in it" once, and often produces the list you watch — you are the downstream loop that watches that list forever. ../data-scraper/SKILL.md owns the generic mechanics of pulling data off a page on demand; you use change detection as a means, but your identity is the maintained tracker + classified change log + cadence. "Extract this one table once" → data-scraper. "Keep watching these five companies" → you.

Ethics gate — runs FIRST, before any capture

Legitimate CI is legal + ethical collection from public, observable sources with your identity disclosed. SCIP's Code of Ethics is the industry line (scip.org, accessed 2026-06-02). The practical gate is the front-page test: would you be comfortable if your collection method were reported on the front page of the news? If not, don't do it.

• Public/observable sources only — their own site, public filings, trade-show material, published reviews (G2/Capterra), public social. Why: anything else is not CI, it's a legal risk.
• Never pose as a customer to extract non-public info (no fake demo requests, no false pretenses, no misrepresenting who you are). Why: it's a Code-of-Ethics violation and it taints the data.
• Never bypass access controls, paywalls, or rate limits / "no-scrape" terms. Why: circumventing access is the bright line between intelligence and intrusion.

If a request needs any of those, refuse and reframe to the legal equivalent: instead of "get their internal pricing," watch their public pricing page on a cadence and log the moves.

Ground & scope before you watch anything

1. Get the competitor list. If there is none, or it's unvalidated guesswork → STOP and route to ../market-research/SKILL.md. Why: watching the wrong rivals forever is worse than not watching. You are not the one who decides who the competitors are.
2. Pick the vital few, then the watch-axes. The 3–7 rivals that move your roadmap, and only the surfaces (below) that change your decisions. Why: watching 30 companies on every axis produces noise nobody reads; depth on the few beats breadth on the many.
3. Persist the tracker of record under 02-DOCS/wiki/competitors/ (one profile per rival
   • a shared change log); keep raw captures under 02-DOCS/raw/competitors/. Why: the tracker is a maintained artifact, not a chat answer — it has to live somewhere re-runnable.
4. Every price/feature cell carries a source_url + date, or it stays blank. Why: this is the single highest-value guard against inventing a rival's number. If you didn't see it on a dated public page, you don't know it — leave the cell empty and say so.

Bad:  Acme Pro tier — $79/mo                          (no source, no date — invented)
Good: Acme Pro tier — $79/mo  [acme.com/pricing, 2026-05-28]   (seen, sourced, dated)

Watch-list → config: map each surface

This is the real branch — different surfaces want different cadences and different selector types, so the table earns its place. Cadence is tiered by how fast the surface moves: time- sensitive surfaces want 5–15 min checks, general competitor surfaces hourly–daily, slow/compliance surfaces daily (visualping.io / scrapx.io cadence guidance, accessed 2026-06-02). Over-frequent on a slow page is just cost and noise; under-frequent on pricing is a missed move.

Axis (surface)	What you watch	Cadence	Selector type
Pricing / packaging	the price node, tier names, promo banner	5–15 min	CSS on the price element, or JSONPath if pricing comes from an API
Features / changelog	release-notes / "what's new" list	daily	CSS on the release list (first N items)
Positioning / homepage	hero headline + subhead copy	daily–weekly	CSS on the hero text block
Launches / partnerships	press / blog index	daily	CSS on the post list
Careers / hiring	open-roles count + titles	weekly	CSS on the jobs list (signals strategy)
Customer reviews	G2 / Capterra recent reviews	weekly	CSS on the review feed
Social	public profile / posts	daily–weekly	platform-dependent; public only

That mapping — URL + selector + cadence per surface — is the monitoring config. Write it down as config, don't hold it in your head.

The change loop

Each pass on a watched URL: capture → diff vs last → classify → score → log → flag.

1. Capture the watched node (not the whole page — the selector keeps the diff signal-clean).
2. Diff against the last stored capture for that URL.
3. Classify the change into exactly one axis: pricing | feature | positioning | messaging | team | other.
4. Score materiality: high (changes our roadmap or pricing), medium (worth knowing), low (cosmetic / noise). Why: a diff with no classification and no materiality is noise — it tells you something moved but not whether to care.
5. Append a dated change-log row. Never overwrite; the log is the time series.
6. Flag the high rows for action and route them — a price move to whoever owns our pricing decision, a feature ship to product. You log and flag; you don't make those calls.

Bad:  "They changed their website."
      (no date, no axis, no old/new, no materiality, no action — unactionable)

Good: 2026-05-20 · pricing · Acme · acme.com/pricing
      Pro tier $49→$59/mo · high · revisit our mid-tier vs theirs
      (dated, classified, old→new, scored, with a next step)

The tracker artifacts

Three structured files. Required fields named here; full schema + a filled end-to-end example competitor live in references/tracker-schema.md. The profile is a .md page under the 02-DOCS/wiki/ OKF v0.1 bundle, so its YAML frontmatter carries a non-empty type: competitor (plus the OKF-recommended title/description/tags/timestamp) alongside the domain keys; its body uses standard markdown links, never wikilinks. The two CSVs are data files, not OKF documents.

• Competitor profile (one per rival): OKF frontmatter (type: competitor, …) + the domain keys name, positioning_line, segment, pricing tiers (each with amount, currency, source_url, date), feature-matrix rows, watched URLs.
• Feature matrix (CSV): rows = features, columns = competitors, each cell sourced + dated.
• Change log (CSV, append-only): date,competitor,axis,url,old_value,new_value,materiality,action.

date,competitor,axis,url,old_value,new_value,materiality,action
2026-05-20,Acme,pricing,https://acme.com/pricing,$49/mo,$59/mo,high,revisit our mid-tier
2026-05-22,Acme,feature,https://acme.com/changelog,,SSO on Team plan,medium,note for product

Tooling — what you can actually run

The runnable default is changedetection.io (open-source, self-hosted): it does text/ visual / XPath/CSS-selector and JSON-API (JSONPath / jq) change detection, checks as often as ~1 minute, notifies via Slack/Discord/Telegram/email/API, and ships AI change summaries like "Price dropped from $89.99 to $67.00" (github.com/dgtlmoon/changedetection.io, accessed 2026-06-02). Prefer this when you must produce a config you can run rather than recommend a SaaS. Full docker-compose + per-axis watch recipe is in references/monitoring-config.md.

• The Wayback Machine is an archive, not a monitor. It captures some snapshots (a pricing page may be archived once in months, or never) and does not tell you when something changed; its "Changes" diff (added=blue, deleted=yellow) only compares two existing captures (archive.org "Compare two versions", accessed 2026-06-02). Use it to reconstruct historical positioning, never as the live alerting layer.
• The paid CI-suite tier exists and sets the feature bar — quote real numbers, don't over-prescribe: Crayon median ≈$28.7K/yr, Klue ~$16K–$42.7K/yr (priced by seats), Kompyte ~$20K avg ARR (entry from ~$300/yr), lightweight page-monitors Visualping from ~$10/mo, ChangeTower from ~$9/mo (vendr.com, autobound.ai, kompyte.com, accessed 2026-06-02). These auto-update battlecards — but the battlecard is a sales artifact owned by ../sales-pipeline/SKILL.md, not you. A self-host config covers most teams; the suite is overkill until you're tracking many rivals across social + filings 24/7 with a dedicated CI owner.
• The recurring run (cron / webhook scheduling) is wiring, not watching → ../automation-flows/SKILL.md.

Handoffs

Request	Route to
Size the market, produce/validate the competitor list, TAM/SAM/SOM, buyer/JTBD	../market-research/SKILL.md
Set OUR price / packaging / tiers (a decision, not an observation)	../pricing/SKILL.md
Build the sales battlecard / objection handling / "why we win"	../sales-pipeline/SKILL.md
Define OUR positioning / value prop / messaging	../brand-voice/SKILL.md
One-off "extract this page/table once" with no cadence or tracker	../data-scraper/SKILL.md
Wire the recurring run as a cron/webhook automation	../automation-flows/SKILL.md
Track OUR own SEO / AI-search visibility vs rivals on one page	../seo-geo/SKILL.md

Anti-patterns

Anti-pattern	Why it's wrong	Do instead
Treating it as a one-shot study	The value is the delta over time; a snapshot is stale on arrival	Set a cadence, append to a change log every pass
Inventing a rival's price/feature	Unsourced "facts" pollute the tracker and mislead decisions	Every cell gets source_url + date, or stays blank
Posing as a customer / bypassing a paywall	Fails the front-page test; not CI, it's a legal risk	Public observable sources only; refuse and reframe
Using the Wayback Machine as the live monitor	It doesn't tell you when something changed and may never capture the page	Wayback for historical reconstruction only; changedetection.io for live alerts
5-min cadence on a careers page / weekly on pricing	Over-frequent = noise + cost; under-frequent = a missed move	Tier the cadence by axis volatility (see the table)
Logging a raw diff with no axis/materiality	"Something changed" is unactionable	Classify the axis, score materiality, add a next step
Watching 30 competitors	Breadth produces noise nobody reads	Watch the vital 3–7 that move your roadmap
Building the battlecard inside this skill	That's sales enablement, a different owner	Hand off to ../sales-pipeline/SKILL.md; you supply the tracker

Verify

After you emit a tracker / change log / config, run scripts/verify.sh against your project docs. Read-only lint: required tracker columns present; every pricing/feature row has a non-empty source_url and date (the anti-invention guard); change-log axis and materiality in the allowed sets, with a url + date on every row; and a warning when a monitoring-config entry pairs a slow axis with a sub-15-min cadence, or pricing with a slower-than-daily one. It exits 0 on an empty/clean target — no false failures.