apify-link-prospecting-outreach

name	apify-link-prospecting-outreach
description	Find sites ranking for target keywords, score every prospect with Ahrefs domain authority and page-level traffic, identify the strongest pitch angle per row ("links to competitor", "mentions brand without linking", "top-3 SERP", "resource page", "outdated content"), generate brand-voice-matched outreach emails using an outreach-type-aware template (unlinked-mention claim, competitor-link replacement, resource-page inclusion, outdated-content replacement, topical niche-edit), and propose a concrete in-article link placement as three artifacts — the verbatim source sentence, the same sentence rewritten with the link spliced in, or a fully-drafted new insertion if no natural fit exists. Use when user asks to find link building opportunities, prospect link partners, recover unlinked brand mentions, replace competitor links, build a tiered outreach list, or run cold email outreach for SEO link building.
author	Daniela Ryplová
author_url	https://github.com/danielarypl
metadata	{"keywords":"link-building, seo, outreach, prospecting, ahrefs, backlinks, cold-email, serp, unlinked-mentions, competitor-links, resource-pages"}

Link Prospecting Outreach

Turn a goal + a target keyword + a URL the user wants to promote into a tiered, ready-to-send outreach list: SERP-ranking prospects with Ahrefs-scored authority, the strongest pitch angle per prospect, an outreach-type-matched email draft, and a copy-paste-ready link placement.

Prerequisites

(No need to check it upfront)

• .env file with APIFY_TOKEN
• Ahrefs MCP available (the skill calls mcp__claude_ai_Ahrefs__* tools for prospect scoring)
• Node.js 20.6+ (for native --env-file support)
• One-time setup inside the skill's scripts/ folder: npm install

Helper scripts (one config, four steps)

After Step 1–2 inputs are collected, write them to a single campaign.json (schema in campaign.json.example). Every downstream script reads --config campaign.json, so the agent doesn't fork per-campaign copies. Sequence:

# 1. Run the Actor (writes {base}.json + sub-Actor sidecars when --fetch-sub-datasets)
node --env-file=.env scripts/run_actor.js --actor "apify/link-prospecting-tool" --input '<json>' --timeout 1800 --fetch-sub-datasets --output {base}.json --format json

# 2. Build unified prospect table from the sidecars
python3 scripts/build_prospects.py --config campaign.json

# 3. (After Step 5 Ahrefs MCP calls → save to {base}_ahrefs_domain.json + {base}_ahrefs_page.json)
python3 scripts/enrich_prospects.py --config campaign.json

# 4. (After Step 8 sub-agents write outputs to /tmp/placement_outputs/row_*.json)
python3 scripts/merge_subagent_outputs.py --config campaign.json --outputs-dir /tmp/placement_outputs

# 5. Write the final xlsx + metadata sidecar
python3 scripts/write_xlsx.py --config campaign.json

If the runner's client-side wait elapses with the Actor still running on Apify, use scripts/fetch_run_artifacts.js --run-id <id> --output {base}.json instead of restarting. If the parent run is missing SUB_ACTOR_RESULTS (post-2026-05-20 Actor schema), scripts/fetch_subactors_from_log.js resolves sub-Actor runIds from the parent log.

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Collect required anchor inputs incl. goal (block on these)
- [ ] Step 2: Collect brand voice, partnership type, output format
- [ ] Step 3: Run apify/link-prospecting-tool
- [ ] Step 4: Pull leads, mentions, authors, and sub-Actor datasets
- [ ] Step 5: Enrich every domain with Ahrefs metrics, assign Prospect Tier
- [ ] Step 6: Run skip pass — flag rows to drop before drafting
- [ ] Step 7: Compute "Why This Prospect" tag per surviving row
- [ ] Step 8: Compose per-row 3-artifact placement + outreach-type-aware email
- [ ] Step 9: Render output in chosen format

Step 1: Required Anchor Inputs (ask FIRST, before anything else)

Do NOT proceed to Step 2 until every required input is answered. Surface them as the very first interaction. The dedup input (#7) is optional but must still be explicitly asked.

1. Concrete goal for this campaign — pick one preset or supply custom text. The goal drives skip-pass filtering, outreach-type template selection, and Prospect Tier thresholds. Required.

   Preset	Effect downstream
   Recover unlinked brand mentions	Skip pass drops every row where brand_mentioned_in_source is false. Default outreach type = unlinked-mention-claim.
   Replace competitor links	Skip pass drops every row not tagged Links to competitor. Default outreach type = competitor-link-replacement.
   Topical authority links to specific URL	No filter. Tier thresholds tighten (DR ≥ 50 for tier A). Default outreach type chosen per-row from Why This Prospect.
   Maximum link volume from any relevant site	No filter. Tier thresholds relax (DR ≥ 30 for tier A). Default outreach type chosen per-row.
   Custom	User-supplied paragraph; biases email tone and tier weights. No automatic skip filter.

2. Target keyword(s) — one or more keywords the user wants their link to appear next to. The skill prospects the SERP for each. At least one required.

3. Brand name — the user's brand or product name. The Actor will not run without this (it is the brand input field).

4. Product/category description — one or two sentences describing what the user sells, who they sell to, and what category their product fits in. Example: "Apify — web scraping platform that runs serverless scrapers as APIs. We sell to developers and data teams who need scraped data without managing infrastructure." Required. Used in Step 6 (topical-fit gate) and Step 7 (adversarial-mention detection) to recognise prospects who are in the same product category — those won't link no matter the pitch. Without this, the skill cannot distinguish a genuine editorial opportunity from a competitor's blog.

5. URL of content to link to — the destination URL that will be inserted into partner articles. Required.

6. Competitors — anyone in the user's product category who would publish a "ours vs theirs" comparison page on their own site. Frame the ask this way explicitly: Encourage 10+ entries; most users default to listing 3–5 obvious ones and miss the long tail. Mapped to on the Actor and reused in Steps 6 (adversarial-mention skip) and 7 ( Why-tag).

Once 1–6 and 8–10 are captured (7 is optional), move on.

Step 2: Secondary Inputs

Ask these next:

1. Brand info and voice — a short paragraph describing the product/brand and the tone for outreach (e.g., "casual and helpful", "formal B2B", "founder-led"). Used verbatim to shape every generated email.

2. Partnership type — the offer the user is willing to make. Determines the offer paragraph substituted into the per-row email. Outreach-type template selection happens separately, per-row, in Step 8.

   Option	What it offers
   ABC link exchange	Three-way link swap: partner links to user, user links to a third party, third party links to partner.
   Direct A B link exchange	Two-way link swap: partner links to user, user links to partner.
   Resource page / list inclusion	Ask to be added to an existing curated list or roundup. No reciprocal link offered.
   Unilateral ask (no reciprocal)	User asks for the link without offering anything in return — appropriate for unlinked-mention claims and broken-link replacements.
   Other	User types their own offer (paid placement, free product, co-authored content, etc.).

3. Output format:

   Format	Behavior
   xlsx	run_actor.js writes a styled spreadsheet to disk.
   markdown	Agent renders the table inline in chat with email drafts beneath each row.

Step 3: Run the Actor

The Actor ID is apify/link-prospecting-tool. Full input schema lives in reference/apify-actor-usage.md.

Recommended call payload for this skill (defaults chosen for outreach-first workflow):

{
  "queries": "<keyword 1>\n<keyword 2>",
  "brand": "<user's brand name>",
  "ownDomains": ["<user-domain.com>"],
  "competitorDomains": [],
  "ignoreDomains": [
    "wikipedia.org", "github.com", "stackoverflow.com", "stackexchange.com",
    "reddit.com", "quora.com", "youtube.com", "twitter.com", "x.com",
    "linkedin.com", "facebook.com", "medium.com", "archive.org",
    "chromewebstore.google.com", "addons.mozilla.org", "apps.apple.com",
    "play.google.com"  
  
   
   
   
   
   
   
   
   
   
   
   
   

The six enable* LLM-source flags map 1:1 to the user's Step 1 input #9 multi-select. Pass false for any engine the user deselected. enableEmailVerification maps to Step 1 input #10.

The ignoreDomains default includes two groups:

• Giants and UGC (wikipedia, github, stackoverflow, reddit, etc.) — too broad to pitch as editorial partners.
• App / extension marketplaces (Chrome Web Store, Firefox Add-ons, Apple/Google Play, VS Code Marketplace, etc.) — product directory listings, no editorial decision-makers.

Do NOT auto-add to ignoreDomains (let the user decide):

• UGC/community sites like kaggle.com, dev.to, substack.com, producthunt.com, g2.com, capterra.com, trustpilot.com — some users get real value pitching these.
• API directories like rapidapi.com, programmableweb.com, publicapis.dev — relevant for some products (especially developer-tool brands), irrelevant for others. Surface these as candidates only if the user wants to add them.

The URL-pattern skip rules in Step 6 catch the per-row noise (subdomain prefixes, path patterns) that ignoreDomains can't express.

department defaults to ["marketing"] only. The skill prioritises editorial-leaning contacts within the returned marketing department during row composition (see Step 8). Only add sales if the user explicitly wants BD-style partnership pitches. Only add c_suite if the prospect domains are very small (1–5 person shops) where the founder may also be the editor.

Call the runner script:

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/scripts/run_actor.js \
  --actor "apify/link-prospecting-tool" \
  --input 'JSON_INPUT' \
  --timeout 1800 \
  --fetch-sub-datasets \
  --output YYYY-MM-DD_outreach.json \
  --format json

Notes:

• --timeout 1800 is the recommended client-side wait. The Actor itself runs 15-50+ min depending on keyword count, LLM-engine fan-out, and enableEmailVerification. Past calibration runs land in the 20–55 min range. Bumping the default avoids the partial-result situation where the runner gives up but the Actor keeps going.
• If the client-side wait still elapses with the Actor still running on Apify (status RUNNING or READY when the runner exits), do not restart the Actor. Use scripts/fetch_run_artifacts.js --run-id <id> --output <file> to poll the existing run and download all artifacts — same output shape as run_actor.js --fetch-sub-datasets.
• --fetch-sub-datasets downloads sibling files alongside the main output: *_mentions.json, *_authors.json, *_serp.json, *_wcc.json. You need all of them to populate every output column.

Step 4: Access All Datasets

The Actor's output schema changed on or before 2026-05-20. The build_prospects script must handle the new shape; older skill versions that joined a separate MENTIONS dataset are broken.

Current schema (verified 2026-05-20):

File written by runner / fetcher	Source	Populates
*_output.json (main)	"All leads" dataset	Contact Full Name, Contact Job Title, Department, Seniority, Contact Email, Email Verification (when enableEmailVerification: true), Contact LinkedIn, Company, Domain. Each lead's source_url[] array contains the article URLs that produced this contact, each with a brand_mentioned_in_source boolean — this is the new home of the per-(URL, contact) mention data.
*_serp.json	Google Search Results Scraper sub-Actor (one item per (query × engine) combination)	SERP Position, Article Title, Publish Date (via organicResults[]), and engine attribution per URL (Google Organic, ChatGPT, Gemini, Copilot, Perplexity, Google AI Mode) by joining aiModeResult.sources[], perplexitySearchResult.sources[], chatGptSearchResult.sources[], geminiSearchResult.sources[], copilotSearchResult.sources[]. URLs from ChatGPT carry a ?utm_source=chatgpt.com query suffix — normalise URLs (strip tracking params) before joining.
*_wcc.json	Website Content Crawler sub-Actor	Placement Source Sentence, Placement With Link, Placement New Insertion, Article Author cross-check, outbound-link inspection for Links to competitor and Resource / roundup page tags. Canonical URL list for building rows — every URL that got body-crawled appears here, including ones that didn't yield a lead.
*_authors.json	AI Web Scraper sub-Actor (when searchAuthorName: true)	Article Author, Author Source (set to searchAuthorName). Note: this sub-Actor frequently TIMES-OUT at its 300s default — partial results are still saved.

What changed (vs. pre-2026-05-20 runs):

1. No separate MENTIONS / AUTHORS / DOMAINS_WITH_LEADS named datasets — mention info is folded into main_leads[i].source_url[].
2. No SUB_ACTOR_RESULTS record in the parent run's key-value store. Sub-Actor runIds are now only discoverable from the parent run log via regex \[apify\.<slug> runId:([A-Za-z0-9]+)\]. The runner script's --fetch-sub-datasets flag now falls back to log-parsing when the KV index is missing; the standalone scripts/fetch_subactors_from_log.js does the same for runs whose runner already exited.
3. The mentions schema reduced: source_url[i] carries only {domain, brand_mentioned_in_source, url} — no per-engine flags like the old ChatGPT_mention / Perplexity_mention. Engine attribution must be reconstructed from the SERP sub-dataset's LLM-result sub-fields (see SERP row above).

If a column's source is missing, write "Not found" and add a manual-lookup hint in Notes. Never fabricate.

Step 5: Ahrefs Enrichment and Prospect Tier

For every unique domain that survived the Actor's filtering, fetch authority and traffic metrics via Ahrefs MCP. Call all three tools in parallel per domain (and across domains — batch parallelise to keep this step under a minute for typical 20–50 prospect lists):

Ahrefs tool	Used for	Column it populates
mcp__claude_ai_Ahrefs__site-explorer-domain-rating (target = domain)	Domain Rating	Domain DR
mcp__claude_ai_Ahrefs__site-explorer-metrics (target = article URL, mode = exact)	Page-level organic traffic (last 30 days)	Page Traffic
mcp__claude_ai_Ahrefs__site-explorer-backlinks-stats (target = domain)	Referring domains count	Referring Domains

If Ahrefs returns no data (domain not indexed, page too new), set the column to "-" and add a Notes hint "Ahrefs has no data — verify manually before pitching". Do not fabricate values.

Assign Prospect Tier using the thresholds matching the user's goal:

Goal	Tier A	Tier B	Tier C
Topical authority links to specific URL	DR ≥ 50 AND Page Traffic ≥ 300/mo	DR 30–49 OR Page Traffic 50–299	everything below
Maximum link volume from any relevant site	DR ≥ 30 AND Page Traffic ≥ 100/mo	DR 15–29 OR Page Traffic 20–99	everything below
Recover unlinked brand mentions	irrelevant — every mention is worth claiming; tier by DR alone (≥ 40 = A, 20–39 = B, < 20 = C)
Replace competitor links	tier by DR (≥ 50 = A, 30–49 = B, < 30 = C)
Custom	use the Topical authority thresholds

Surface tier breakdown to the user before Step 8 — let them confirm whether to draft emails for all tiers or only A/B.

Step 6: Skip Pass

Before drafting any email, walk every row and apply skip rules. Skipped rows get Outreach Status = "Skip", a one-line reason in Notes, and no email or placement is generated (saves tokens and user review time).

Skip rules (in order):

1. Goal mismatch. If the goal is Recover unlinked brand mentions and the row's Mentions data shows brand_mentioned_in_source: false, skip. If the goal is Replace competitor links and the row's WCC body has no outbound link to any competitorDomains entry, skip.

2. Already pitched. If the row's domain matches an entry in the optional already-pitched list from Step 1 input #7, skip.

3. Own / competitor domain leak. The Actor should already filter these, but double-check — if the row's domain matches ownDomains or competitorDomains, skip.

4. Stale content. If Publish Date is older than 5 years, skip (low chance the editor will update the post).

5. URL-pattern skip. Skip rows whose URL matches any of these patterns:

   • Subdomain prefixes: developers.*, docs.*, support.*, helpcenter.*, legacy.*, dsarequests.*, connectivity.*, community.*, dev.* (when used as a doc subdomain — e.g. dev.example.com/api/), api.* only when followed by a path that's clearly documentation (/reference/, /docs/, /spec/). Do NOT skip rapidapi.com or other API-directory domains by this rule alone — api.* is a subdomain check, not a substring check.
   • Path patterns: /api-docs/, /reference/, /marketplace/, /extensions/, /profile/, /users/, /free-tools/, /spec/, /content/privacy, /content/terms, /content/dma, /content/how_we_work, /legal/, /_redirects, /sitemap.
   • Vendor product page patterns: URL ends in , , contains , , , , (extension detail pages).

Never suggest external lookup services or workaround tools in Notes — no hunter.io, no LinkedIn search, no third-party verification services. The skill's job is to surface what we found, factually. When information is missing (no email, no author, etc.), state the gap and stop. The user knows where to look; suggesting their tools back at them is condescending and clutters the output.

A row failing any rule above is skipped before Step 7. Skipped rows still appear in the final output (so the user can see what was filtered) but with empty placement and email cells and Outreach Status = "Skip" plus the reason in Notes.

Step 7: "Why This Prospect" Tags

For every surviving row, compute one or two Why This Prospect tags, prioritised by which makes the strongest pitch. These tags drive the outreach-type template selection in Step 8.

Tag	Trigger	Source of truth
Mentions brand, no backlink	brand_mentioned_in_source: true AND backlink_in_source: false in Mentions dataset	Mentions dataset
Links to competitor [domain]	WCC page body contains an outbound link whose host matches any competitorDomains entry	WCC dataset
Top-3 SERP for [keyword]	SERP Position is 1, 2, or 3 for any keyword	Google Search Scraper sub-dataset
Resource / roundup page	WCC page body has 10+ outbound links AND the page title or H1 matches /(best|top|list|roundup|tools|resources|guide to)/i	WCC dataset
Outdated content	Publish Date is older than 24 months AND newer than 5 years (5+ years was already skipped)	Google Search Scraper / WCC

A row may carry up to two tags. Order them by pitch strength using this priority: Mentions brand, no backlink > Links to competitor > Resource / roundup page > Top-3 SERP > Outdated content. If no tag fits, leave the column as "-" — the row still gets pitched, just without a special angle.

Step 8: Compose Per-Row Placement and Email

Each surviving row gets three placement artifacts plus one email draft. Apply these quality rules without exception:

1. No fabrication. If the article author or contact email is unknown, set the field to "Not found" and leave a one-line factual note (e.g., "No email found for this contact" or "No author detected"). Do not suggest external lookup tools or workarounds in Notes — see Step 6 rule 11 for the rationale. Just state the fact and stop.

2. Prioritise editorial-leaning contacts. When the All leads dataset returned multiple contacts for the same domain, prefer the one whose jobTitle matches /editor|content|writer|managing|editorial|blog|copy/i, demote anyone whose jobTitle matches /ceo|cfo|cto|founder|chief|vp\b|president/i unless the company is a 1–5 person shop. Surface the chosen contact in the row; keep alternates in Notes as "Alternate contacts: <name1> (<title>), <name2> (<title>)".

3. Three placement artifacts — try strategies in this priority order. Use the WCC sub-dataset's page text. Try strategies 1 → 2 → 3 in order; stop at the first one that produces a clean fit. Record which strategy was used by prepending the Notes field with Placement: drop-in / Placement: additive / Placement: new insertion.

   Strategy 1 — drop-in (preferred). Find a sentence in the article where the user's URL can be added to existing words without changing any of the surrounding prose. The link goes on an existing word or short phrase the author already wrote. Output:

   • Placement Source Sentence = the verbatim sentence as it appears in the article.
   • Placement With Link = the same sentence with the link inserted on an existing word/phrase. No new prose, no rewording, no deletions. Example: source = "Tools like Octoparse and BeautifulSoup work well for hotel data." → with-link = "Tools like Octoparse and **[BeautifulSoup](URL)** work well for hotel data." (link added to existing word). The editor doesn't have to approve any new wording — just a hyperlink.
   • Placement New Insertion = "-".

   Drop-in works when the article already names a brand, tool, or technique that maps cleanly to the user's URL. It's the lowest-friction ask of any outreach pattern: "could you add a hyperlink to a word you already wrote?"

   When no drop-in target exists but the article has a sentence the user's URL would naturally , keep the original sentence intact and add new sentence after it. The new sentence introduces an adjacent reader-need that the article doesn't already cover and that the user's URL addresses. Output:

5a. The email MUST include the exact placement wording — verbatim. The recipient should never have to ask "what's the wording you're suggesting?" or click through to a separate cell to see the proposed text. Embed the proposal directly:

• For drop-in: quote both the source sentence and the linked version inline. Example: "In your line 'Tools like X and Y work well for Z', would you turn 'Y' into a hyperlink to <URL>?"
• For additive: quote the anchor sentence verbatim AND the exact follow-on sentence you're proposing. Example: "Right after your sentence 'X happens because Y.', would you add: 'For the Z case specifically, see <URL>.'?"
• For new insertion: quote the anchor sentence the new paragraph should follow, then the full proposed paragraph inline. Example: "In the 'Honorable mentions' section, after 'each platform has its own trade-offs.', would you add this paragraph: '<full paragraph with link>'?"

The email is the ask. If the wording isn't in the email, the ask is incomplete. Vague phrasing like "happy to draft it for you" / "happy to send exact wording" / "a follow-on sentence linking to..." is a content-skill bug — always rewrite to include the verbatim proposal.

6. Word cap: emails are 150 words or less (subject + body combined).

7. Personalisation is mandatory. Every email must open with a concrete reference to the specific article (title + a one-line takeaway from its content). No generic "I loved your article" openers.

Step 9: Render Output

Markdown format — agent renders inline in chat:

1. A header line with the Apify run ID and tier breakdown (A: 8, B: 15, C: 7, Skipped: 12).
2. One Markdown table row per prospect with the most actionable columns (tier, why, contact, placement summary). Skipped rows render in a separate collapsed section at the bottom.
3. Below the table, one fenced code block per non-skipped row containing the email draft (subject + body), labeled with the row index, tier, and outreach type.

xlsx format — scripts/write_xlsx.py --config campaign.json writes a 2-sheet workbook after Steps 5–8 finish.

xlsx is written as two sheets:

• Outreach — active rows only, full 30 columns. This is the send-ready deliverable. Sorted by Prospect Tier ascending (A first), then by Domain DR descending, then by SERP Position ascending.
• Skipped — skipped rows with reduced columns: Domain, Article URL, Article Title, Skip Reason (extracted from Notes), Source Engines, Why This Prospect. This sheet exists for auditing what was filtered without cluttering the main view. Missing Ahrefs columns aren't visible here, so the empty-data confusion goes away.

The user opens the file and lands on Outreach by default — only actionable prospects. They can switch to Skipped to audit. This pattern replaces the older single-sheet-with-red-rows approach.

Both formats also produce a sidecar run_metadata.json: { runId, actorId, startedAt, finishedAt, inputs, datasetIds, tierCounts, skipCounts }. Drop it next to the main output file.

Output Row Schema (30 columns)

SERP Position, Source Engines, Keyword, Article Title, Article URL, Domain, Domain DR, Page Traffic, Referring Domains, Prospect Tier, Why This Prospect, Article Author, Author Source, Publish Date, Contact Full Name, Contact Job Title, Department, Seniority, Contact Email, Email Verification (one of verified / catch-all / risky / invalid / unknown / -), Contact LinkedIn, Company, Outreach Type, Partnership Offer, Placement Source Sentence, Placement With Link, Placement New Insertion, Suggested Email Copy, Outreach Status (default "Not started", "Skip" for skipped rows), Notes (auto-flags + skip reason + manual hints + alternate contacts).

Full schema with types and source datasets per column is in reference/output-formats.md.

Error Handling

Error / symptom	What to do
APIFY_TOKEN not found	Ask user to create .env with APIFY_TOKEN=your_token. Get one at console.apify.com/account/integrations.
Ahrefs MCP unavailable	Skip Step 5. Set Domain DR, Page Traffic, Referring Domains, Prospect Tier to "-" and add a one-line note in the output header explaining tiers were not computed. Continue with the rest of the workflow.
Cannot find module 'xlsx'	Run npm install inside the skill's scripts/ folder.
Error: 'brand' is required	The user skipped Step 1 anchor #3. Re-ask brand name.
Actor run TIMED-OUT (client-side, Actor still running on Apify)	Do not restart. Use node --env-file=.env scripts/fetch_run_artifacts.js --run-id <runId> --output YYYY-MM-DD_outreach.json --timeout 1800 to poll the existing run and download all datasets when it terminates. Same output shape as the runner.
Actor run TIMED-OUT (Actor itself ran past its timeoutSecs)	See reference/troubleshooting.md. Lower organicResult, cut keywords, or disable some LLM engines. Almost never the cause — usually it's the client-side wait.
Author = Not found	Expected for ~30% of pages without bylines. Skill writes "Not found" and adds a manual-lookup hint to Notes. Do not fabricate. The AI Web Scraper sub-Actor frequently TIMES-OUT at its 300s default mid-crawl (past campaigns have lost author data for ~7 high-DR sites at a time this way); when this happens the _authors.json dataset still contains the partial results that finished before the timeout. WCC metadata.author / openGraph article:author / JSON-LD Person.name are the fallbacks the agent should try before writing "Not found".
Sub-Actor datasets missing (no _mentions.json / _authors.json / _serp.json / after )