audience-icp-filter

Audience ICP Filter

Takes an audience that already exists and splits it into ICP match, needs review and no match — with the user's own team and competitors stripped out, and a reason attached to every decision.

Authority — read this first

• Inlined below: the coverage gate, the ICP question set, the seniority and function tiers, the exclusion doctrine, the two-pass rule, the naming convention, the anti-patterns, and the fixed result UI. This covers the common ~90% of lists. Work from these tables.
• In references/title-taxonomy.json: the full regex patterns behind seniority/function detection. You do not need to read it — scripts/build.py loads it. Consult it only if a user disputes a classification or wants to extend the taxonomy.
• Never classify a list by hand. Run the script, then run your own review pass over its output. Both passes are mandatory.

Scope

This skill starts from a list that already exists: an audience in the user's sales tool, or a CSV. It does not import or scrape — importing is a separate job with its own timing and prerequisites, and folding it in here would make the skill slower and less reliable for no gain.

If the user hasn't imported yet, tell them to do that first, then come back with the audience.

What it does

Any audience is mostly noise: the user's own colleagues are in it, competitors are watching, and a third of the job titles are unreadable. This skill checks whether the data can support the ICP the user wants, asks what that ICP actually is, sorts the list, and writes the segments back as complementary audiences.

Execution style — fast and quiet

This skill does a lot of steps. Two rules keep it usable:

• Minimal narration. Do the reasoning and the tool calls, but do not narrate each step to the user ("page 1 loaded", "the param is skip not offset", "wrapping the payload"…). The user wants the result, not a play-by-play. Stay silent through the pipeline and speak only when you present the widgets — one or two sentences of framing, no more. Think as hard as you like; just don't type it out.
• Parallelize and batch. Fetch the lead pages concurrently (issue the get_audience_leads calls for all pages in one batch). Keep only the scored fields when you normalise (leadId, jobTitle, companyName, proEmail, shortBio, location, industry) — not all 40 columns — so the payloads stay small.
• Never re-read the whole audience in pass 2. This was the measured bottleneck: reviewing 250 leads one by one took 8 minutes. Pass 1 hands you a bounded pass2_queue — only the genuinely suspect leads (ambiguous, bio-inferred matches, agency/freelance matches, and leads dropped on a soft geo/industry miss). Pass 2 reviews only that queue, typically a few dozen. If the queue is still large (60+), fan it out: a couple of parallel sub-agents on Sonnet splitting the queue, reserving deeper reasoning only for the final ambiguous handful. A clean, on-target audience produces a queue of ~15–25; a full re-read is never needed.