Extract the repeatable reason you win.
Reads your won deals, finds the signals, personas, and motions that repeat, and ranks active lookalikes to work.
Inside: The pattern extraction, the win-signal ranking, and the lookalike list.
Install it in one line, or paste it in.
~/.claude/skills/ and runs automatically when it is relevant.Connect your context. Set it to your motion.
it reads all of the above automatically, across every won deal in the window you pick.
pulls the actual words buyers used, so the pattern is "why they said yes," not just the fields.
adds which features the winners adopted first, so the profile includes real behavior.
This was built for a B2B SaaS org running a staged pipeline. Set these to your stack:
Run any segmentation you like. The skill reports the patterns that actually repeat in your wins, so point it at your fields, not anyone else's.
| Set this | What it is | Default / Example |
|---|---|---|
| WON filter | how you mark a win | Stage = Closed Won |
| WINDOW | the win-date range to analyze | trailing 4 quarters |
| SEGMENT fields | how you group accounts | industryemployee countregion |
| PERSONA field | the title that signs | primary contact role or title |
| MOTION fields | inbound vs outbound, source | lead sourcechannel |
| COMPETITOR field | who you displaced or beat | competitor on the deal |
| USE_CASE field | the problem you solved | primary use case or product |
| MIN_SAMPLE | fewest wins before a pattern counts | 5 (raise if you close a lot) |
Everything the skill does, in full.
Reads your closed-won deals and finds what they have in common: the segments you win, the personas who sign, the motions that work, and the competitive spots where you come out ahead. It turns that into one target profile you can hand a rep, then ranks your active pipeline by how closely each open deal matches your winners. The point is not a nice chart. It is a shorter list of accounts that look like the ones you already closed.
- 1Pattern extraction (what repeats)
Group every won deal across your segment, persona, motion, competitive, and use-case fields. Report only the patterns that clear MIN_SAMPLE, so one lucky whale never becomes a "profile." For each pattern, show the share of wins, the average deal size, and the average cycle length, so a common-but-small pattern is not confused with a rare-but-huge one.
- 2Win-signal ranking
Rank the patterns by lift, not raw count. A signal earns its place when won deals show it far more often than the base rate of all your deals. "Half our wins are mid-market" means nothing if half your pipeline is mid-market. The signals that matter are the ones over-represented in wins.
- 3Target profile (the ICP you actually close)
Fold the top signals into one plain-language profile: the segment, the persona who signs, the motion that lands them, the use case that resonates, and the competitive setup where you win. This is not the ICP on the website. It is the ICP your win data votes for.
- 4Lookalike ranking (active pipeline)
Score every open deal by how many profile signals it hits. Return a ranked list, best matches first, with the signals each one shares with your winners and the ones it is missing. This is the payoff: a shorter list of open accounts that look like deals you already won.
- No pattern reported below MIN_SAMPLE. Small samples lie, and one big logo is not a trend.
- Signals ranked by lift over base rate, never by raw count alone.
- Every lookalike shows the specific signals it matches, never just a score.
- Competitive and use-case claims come from a field or a transcript, never invented.
CLOSED-WON PATTERNS · 34 wins, trailing 4 quarters Top win signals (by lift over base rate) Signal Share of wins Avg size Avg cycle Mid-market, 200-800 staff 62% $41K 38 days Champion = Head of Ops 55% $44K 35 days Inbound demo request 48% $39K 29 days Displaced a manual/DIY setup 44% $47K 33 days Target profile (what you actually close): Mid-market ops leader, inbound, replacing a manual process. Fast cycle, above-average deal size. Active lookalikes (open pipeline, best match first) Account Signals matched Missing Haledon 3 of 4 (seg, persona, mo) no competitor read Brightsea 3 of 4 (seg, persona, uc) outbound, not inbound Corverin 2 of 4 (seg, use case) enterprise size Next move: 1. Work Haledon and Brightsea first. They match your winners on 3 of 4. 2. Add a competitor field to your next 10 wins. It is your thinnest signal.
MIN_SAMPLE (5) and the trailing-4-quarter window are defaults, not laws. They suited a mid-market SaaS cycle with steady volume. If you close a handful of large deals a year, widen the window and lower the floor with your eyes open. If you close hundreds, raise the floor so only strong patterns survive. Lift is always measured against your own base rate, so the profile is yours, not a benchmark.
Where an operator takes this next.
The read is step one. Here's where an operator takes it once the manual version proves out.
You built the read once; now it runs itself.
Point a scheduled Claude task at Salesforce nightly and write the lookalike score back to a custom field on the opportunity.
Feed the top win signals into Clay to re-rank the outbound target list so reps prospect the accounts that already look like winners.
DM the account owner in Slack the moment an open deal crosses the match threshold, instead of waiting for the next QBR.
One skill is the on-ramp.
A single skill does one job. Chained into a playbook, or run as a full build, it becomes a system. Here is where this one plugs in.