# The Return-to-Region Playbook: Metro Ties to a Ranked Onsite Slate

*Turn one hard onsite req in a non-hub metro into 30 to 60 ranked out-of-region candidates, each with a verified school or employer tie, a mobility score, and a first message.*

- Canonical URL: https://www.refolk.ai/guides/return-to-region-sourcing-playbook
- Pillar: Recruiting and sourcing
- Format: Playbook
- Published: 2026-11-02
- Last reviewed: 2026-11-02
- Reading time: 22 min
- Keywords: how to recruit people back to their hometown, sourcing candidates with ties to a city, recruiting for an onsite role in a small market, find alumni who moved away from a region, returning resident recruitment strategy

## Key takeaways

- A return-to-region slate starts from 3 to 8 feeder schools and 10 to 25 anchor, acquired, or defunct employers with sites inside the metro, and narrows 150 to 400 raw leavers to 30 to 60 ranked names.
- Regional campuses keep far more graduates in state than flagships do. Sam Houston State keeps 93.1% at Year 1 against 75% at LSU and Ohio State, so a flagship seed yields more leavers per graduate.
- No government series counts people returning to a metro directly: IRS inflows include newcomers and suppress flows under 20 people, so apply the ACS state-level returner share before quoting a pool size.
- Naming a shared former employer is the only outreach lever with a published effect size, a 27% lift in response according to LinkedIn, so the first message should name the documented tie and nothing inferred about the person.
- Graduation year works as an age proxy under the ADEA, which protects people 40 and over, so no field in a mobility rubric may be a date, even though many returners come home in their 40s and 50s.
- In Refolk's index, 34,336 US profiles carry the title Manufacturing Engineer, yet the same title filtered to Senior returned zero, so check the count for the title alone before you trust any filter.

This playbook is for recruiters, sourcers, and founders filling an onsite or hybrid role in a secondary metro where the local pool has run dry. It takes one hard req and turns it into a deduplicated, ranked slate of 30 to 60 named people who live elsewhere but have a documented tie to the metro through a school or a past employer. Each one comes with a mobility score and a first message that names the tie. It also marks the line you must not cross: no graduation years, no birthplace, and no guesses about family.

## What is a return-to-region slate?

A return-to-region slate is a ranked list of qualified people who studied or started their careers in your metro, now live somewhere else, and have a tie you can prove and name in your first message. Build one when an onsite role in a non-hub metro has exhausted the local pool and a cold national search is converting badly.

It fills a gap between two things that already exist. Relocation-scoring guides ask whether any given candidate will move. Regional return-home campaigns, such as Boomerang Greensboro or the Love, Kansas campaign, market a place to everyone who ever left it. Neither tells a recruiter how to start from one metro's schools and employers, find the people who left, check that the tie is real, and rank them against one req.

Why bother with a tie? Because people rarely move. Chmura measured the share of Americans who relocated across county or state lines in one year at 3.2%. Strada's Talent in Motion research finds that 55 percent of college-educated workers studied and work in their home state, and 12% left for college and came back. Most people stay put, and the ones who come back are a small, specific group. A documented tie is the cheapest filter you have for finding them, and it gives you something true to say when you reach out.

What you hand the hiring manager at the end:

- 30 to 60 named out-of-region candidates, deduplicated across GitHub, LinkedIn, and the open web.
- One tie per person, either a school or a past employer, with a link or note proving the campus or site was in the metro.
- A mobility score from a written rubric that contains no dates.
- A first message under 400 characters that names the tie.
- An open search for the same req, run on the same written criteria, so the tie slate adds to your pool instead of replacing it.

> Name the place they worked, not the life you imagine they want back.

## Which schools and employers seed a metro?

Your seeds are 3 to 8 feeder schools and 10 to 25 anchor, acquired, or defunct employers whose campuses and sites are physically inside the metro. Get the schools from Census PSEO employment flows, and the employers from WARN layoff filings plus your own knowledge of the metro's anchors.

### Feeder schools from PSEO

PSEO (Post-Secondary Employment Outcomes) is a Census Bureau dataset that links graduates of participating institutions to the state and industry of their main employer. Its Employment Flows table counts graduates by destination at one, five, and ten years after graduation. The API gives you counts of employed graduates and of in-state graduates for each horizon (fields such as Y5_GRADS_EMP and its _INSTATE counterpart), and you compute the out-of-state share yourself. No national figure is published. You work school by school, in the PSEO Explorer or through the API.

| Institution or group | Measure | Value |
| --- | --- | --- |
| Sam Houston State (regional) | In state, Yr 1 / 5 / 10 | 93.1 / 91.9 / 90.2% |
| LSU, Ohio State (flagships) | In state, Yr 1 | 75% |
| Broad-access publics (Strada) | Work in home state | About two-thirds |
| Most selective publics (Strada) | Work in home state | About half |

How to read it: regional campuses keep more of their graduates in state because they draw students from inside the state. That means a regional seed gives you fewer leavers per graduate. At Year 1, Sam Houston State sits 18.1 points above the two flagships. Foote and Stange found that graduates of flagship institutions, and graduates in certain majors, are more likely to move out of state. Seed both kinds. Flagships give you more leavers. Regionals give you a smaller set of leavers who were drawn mostly from inside the state.

The state matters as much as the school. Strada reports that 72 percent of college-educated workers in Michigan stayed in their home state for college and work, against 36 percent in Colorado. Your state's baseline changes how many leavers you should expect to find.

Two limits to check before you trust a PSEO number:

- **Partial coverage.** At its published coverage count, PSEO included 336 institutions covering about 16% of US graduates. If your feeder school is missing, fall back on its alumni footprint in profile search.
- **An earnings floor.** PSEO only counts graduates who earn at least the annual equivalent of full-time work at the federal minimum wage and work three or more quarters in a year. Graduates below that line drop out of the counts.

### Employers from WARN filings and local knowledge

WARN filings are the best-documented public record of closures and mass layoffs. Under the federal rule, employers with 100 or more employees must give 60 days' notice before a plant closure or a mass layoff affecting 50 or more workers. Aggregator sites collect the state filings into searchable lists. Each filing names a city, which gives you a specific site to seed, not just a company name.

Build the employer list in three bands:

- **Anchors.** The metro's large current employers in the req's field.
- **Acquired.** Companies that were bought and absorbed, whose old names still appear on profiles.
- **Defunct.** Closures and plant shutdowns found in WARN filings.

State business registries and chamber directories may add names, but I have not verified them as sources. Treat what they give you as leads to confirm.

Acquired and defunct employers matter more than they look. When a plant closes, its alumni scatter at the same time, and their profiles keep the old employer name. A single regional employer can dominate a talent sample.

**20% - Share of a 25-profile sample of Manufacturing Engineers in Refolk's index who worked at one employer, Daniel Defense**

The sample is small and not representative, but it shows how one employer can produce a cluster of alumni with the same tie.

> **Tip:** Seed by site, not by brand
>
> Write every employer seed as "company in city", for example a named plant in the metro. A bare company name pulls in every state where that company operates.

## How big is the pool of people who left?

No published series counts how many people return to a mid-size metro each year, so you estimate the pool from three sources and quote it as a range. IRS county inflows give you annual volume, ACS microdata gives you the share of movers returning to their birth state, and PSEO tells you how many graduates of each seed school work out of state.

The two federal migration series answer different questions. IRS Statistics of Income (SOI) migration data counts address changes on tax returns. It is large, but it has no birthplace field, so it cannot tell a returner from a newcomer. The American Community Survey (ACS) records birth state, so researchers working with its microdata can flag movers whose current state matches their birth state as returners. That only works at state level.

| Attribute | IRS SOI | ACS |
| --- | --- | --- |
| Small-flow suppression | Under 20 people | Under 3 |
| Identifies returners | No | Yes, via birth state |
| Relative sample | Baseline | About 2% of IRS |
| Demographics | None | Yes |

My sizing method:

1. **Denominator.** Take IRS inflows into the target county or counties as the annual volume. Flows under 20 people are suppressed, so small origin counties disappear from the data.
2. **Returner share.** Apply the state-level ACS returner share, taken from the microdata, to estimate how much of that inflow is people coming home. Never quote raw IRS inflows as a returner pool, because they include newcomers.
3. **Leavers per seed school.** For each feeder school, subtract in-state graduates from employed graduates in the target field at Year 5 and Year 10. A school keeping 90.2% in state at Year 10 sends about 1 in 10 of its tracked graduates out of state. A flagship at 75% in Year 1 sends 1 in 4.
4. **Brief.** Write the result into the brief as a range with its sources, so the hiring manager understands why the slate holds 30 to 60 names and not 300.

Some figures for scale. In one IRS tax-year pair, 8.9 million migrant returns covered 16.3 million people. Campaign results give a sense of what is possible but are not a conversion rate: Boomerang Greensboro invested about $250,000 and reports 162 returners through the programme, and a Lightcast analysis of it cites 25 to 40% eventual return to home regions. I have not seen the method behind that range, so I would not put it in a forecast.

## The return-to-region procedure, step by step

The build runs in eight steps, from seeding the metro to writing one message per candidate, and narrows 150 to 400 raw names to a ranked slate of 30 to 60. Expect about two working days per req, plus roughly two minutes per profile for tie verification. These times are my desk estimates, not measurements.

Sources disagree on order. Regional campaign guides put marketing before targeting. Chmura advises using labour-market data to pick likely returners first. For a single req, targeting first is the only order that works. Marketing will not produce a slate.

#### Return-to-region build, one req

1. **Seed the metro** - List 3 to 8 feeder schools from PSEO flows and 10 to 25 anchor, acquired, or defunct employer sites from WARN filings and local knowledge. About 2 hours.
2. **Size the pool** - Pull IRS county inflows, apply the ACS returner share, and compute PSEO out-of-state shares for the target field. Write an expected-pool range into the brief. About 1 hour.
3. **Find the leavers** - Search for people in the role whose school or past employer is a seed and whose current location is outside the region. 2 to 4 hours, 150 to 400 raw names.
4. **Verify the tie** - Confirm that the campus or employer site was in the metro, not a remote role, another plant, or another campus. About 2 minutes per profile, with evidence logged.
5. **Qualify for the req** - Apply the written, job-related criteria shared with the open search. No graduation year, no birthplace. 60 to 120 names remain.
6. **Score mobility** - Score current-job tenure, earlier relocations, and tie type with the rubric below. Every name gets a score.
7. **Dedupe and rank** - Merge the same person across GitHub, LinkedIn, and the web on name, employer, and dates, then sort. 30 to 60 ranked names.
8. **Write the first message** - Name the documented tie, the role, and the pay, and nothing inferred about the person. Under 400 characters each.

#### Raw leavers to ranked slate

| Stage | Figure | Note |
| --- | --- | --- |
| Raw leavers | 150 to 400 | Seed school or employer, located outside the region |
| Tie verified and qualified | 60 to 120 | Site confirmed, written criteria met |
| Ranked slate | 30 to 60 | Scored, deduplicated, message drafted |

*A typical build keeps roughly one in five to one in seven raw leavers, and most of the cut happens at tie verification and qualification.*

### Step 3 is where the time goes

The leaver search combines three conditions: the role, a seed (a school or an employer at a site in the metro), and a current location outside the region. Most search tools make you build those as three separate filters and then reconcile the overlaps by hand. In [Refolk](/) you write the whole thing as one sentence and get people back from GitHub, LinkedIn, and the open web, which also gives step 7 the cross-platform matches it needs.

Before you add any filter, check how many results the title gets on its own. Here is what that looks like for one title in Refolk's index:

| Filter | Total | Exact-title share of sample | Top-employer share of sample |
| --- | --- | --- | --- |
| US, all seniorities | 34,336 | 80% (20 of 25) | 20% Daniel Defense (5 of 25) |
| US, Senior | 0 | n/a | n/a |
| Canada, Senior | 0 | n/a | n/a |

**34,336 - US profiles titled Manufacturing Engineer in Refolk's index**

The same title filtered to Senior returned zero in both the US and Canada, which is a filter failure, not an empty market.

What this tells you:

- **The national pool is big and scattered.** The 10 regions listed in the 25-profile sample were all different, Longmont CO, Greater Hartford, and Providence among them. The tie is what narrows that pool to people with a reason to consider your metro.
- **Filters can drop everyone without warning.** A seniority filter returned nothing while the title alone returned 34,336. Check the count at each filter before you treat an empty result as real.
- **The sample shows a mechanism, not a distribution.** Twenty-five profiles is not representative. I would not draw cross-country comparisons from this table.

I ran this search: `Manufacturing engineers now working outside Kansas who studied at Wichita State or previously worked at Spirit AeroSystems in Wichita` - [see the full result list](https://www.refolk.ai/s/exkv3rey18).

*Returns named manufacturing engineers outside Kansas with a Wichita State or Spirit AeroSystems Wichita line on their record, ready for tie verification.*

## How do you verify a tie and score mobility?

A tie is verified when evidence shows the campus or employer site was physically in the metro while the person was there. Mobility is scored only from observable career signals: tenure in the current job, earlier relocations, and the type of tie.

#### Tie evidence, strongest first

1. **Employer site in metro** - The job-location line or the WARN filing city names the metro
2. **Campus in metro** - The campus city on the school page or the profile is in the metro
3. **Early-career role in metro** - The first job's location is in the metro, with the site confirmed
4. **Volunteer or board role** - A local civic role, corroborated by a second signal
5. **Inferred, never scored** - Hometown guesses, surnames, family, anything not on the record

*Only the top four layers can carry a score, and an inferred tie never enters the rubric or the message.*

What each tie proves, and how it can mislead:

- **Employer site.** Proves the person worked locally. It misleads when the profile lists a company headquartered in the metro but the job was remote or at a plant in another state.
- **Campus.** Proves the person lived there as a student. It misleads with multi-campus systems and online degrees that carry the main campus name.
- **Early-career role.** Proves they worked there at the start of their career. It carries the same site risk as any employer tie.
- **Volunteer or board role.** Shows local involvement but is weak on its own. Only score it when a second signal backs it up.

The profile fields that record ties are education, first-employer location, earlier job locations, and volunteer or board roles. Nobody has published how often each of those fields is filled in, and I have not measured it. So a missing location does not mean there is no tie. More often, the field is just empty. Budget about two minutes per profile and log the evidence as a link or a one-line note.

> **Rule:** No dates in the rubric
>
> No field in the mobility score may be a date: not graduation year, not years since graduation, not total career length. Score tenure in the current job only, and write down the bands before you score anyone.

**Mobility scoring rubric (0 to 10)**

```
TIE TYPE (0 to 4)
4 = Employer site in metro, verified on job-location line or WARN city
3 = Campus in metro, verified on school page or profile
2 = Early-career role in metro, site verified
1 = Volunteer or board role in metro, corroborated by a second signal
0 = Unverified or inferred -> remove from tie slate

CURRENT-JOB TENURE (0 to 3)
Current role only. Never total career length.
3 = ______
2 = ______
1 = ______
0 = ______

EARLIER RELOCATIONS (0 to 3)
Moves between metros visible in job-location history.
3 = Two or more prior moves
1 = One prior move
0 = None visible

NEVER SCORED
Graduation year, years since graduation, birthplace, family status, surname, photo, inferred hometown.

Evidence for tie: ______________________
Scored by: ________  Score: ____ / 10
```

*Fill in the tenure bands for the role family before scoring, then apply the same bands to every name, including those from the open search.*

#### Tie strength against mobility signals

Horizontal axis runs from Weak or unverified tie to Strong verified tie. Vertical axis runs from Low mobility signals to High mobility signals.

| Quadrant | What it means |
| --- | --- |
| Mobile, weak tie | Move to the open search and do not lead with a tie |
| Mobile, strong tie | Top of the slate, message first |
| Settled, weak tie | Drop from this slate |
| Settled, strong tie | Keep on the slate, message later with a lighter cadence |

*The verified tie sets your message, and the mobility score sets the order and cadence.*

## Where is the legal line?

Score and message on documented ties and job-related criteria only. Graduation years act as an age proxy under the ADEA, which protects people 40 and over. A local-tie requirement used on its own can mirror the local-only recruiting pattern the EEOC has treated as disparate impact on national origin.

### Age

People tend to move back at mid-career. An Iowa State study covering four decades of graduating cohorts found that some natives who left appear to return later in life, around their 40s and 50s. In the IRS data, filers aged 26 to 44 made up 57.2% of movers. Both facts tempt recruiters to target "people who graduated 15 to 25 years ago", which is age screening by proxy.

The EEOC does not treat a request for graduation dates as a violation in itself, but it scrutinises such requests closely. Employment counsel at Ward and Smith advise that "open and obvious attempts to establish age, date of birth, year of graduation from high school, and the like are to be avoided in the selection process." Age screening has cost employers real money: EEOC v. iTutorGroup ended in a $365,000 settlement paid to over 200 applicants who were screened out by age.

### National origin

The EEOC's national origin guidance describes a case where "only advertising vacant positions within a predominantly White local area and maintaining a pre-application residency requirement, resulted in a disparate impact." There is no formal safe harbor. The documented best practice is "using a variety of recruitment methods, as opposed to word-of-mouth recruiting... establishing written objective criteria... and applying those criteria consistently." In practice:

- Run the tie slate alongside an open search for the same req.
- Hold both to the same written criteria.
- Never make a tie a requirement to apply or to advance. Use it as a sourcing lens and a message hook, nothing more.

### Family status and state law

I could not verify how family status is treated for this use, so do not test it. Keep it out of both scoring and copy. State fair-employment laws vary, and I have not researched them here. Check the req's state with counsel before launch.

> **Watch out:** Mid-career is a pattern, not a filter
>
> Returners often come home in their 40s and 50s, which is exactly why you must never target by cohort. Rank on tie evidence and current tenure, and let age fall wherever it falls.

## How does a return-to-region slate go wrong?

It usually goes wrong in two ways: false ties (the wrong campus or the wrong plant) and protected traits slipping into the scoring, the targeting, or the copy. These are the eight failure modes I check for on every build, each with what it looks like and the check that catches it.

1. **School tie, wrong campus.** A multi-campus system or an online degree reads like a local tie because it carries the flagship's name. The person may never have set foot in the metro. *Check:* confirm the campus city on the school page or the profile.
2. **Employer tie, wrong site.** Big anchors run sites in many states. The classic false positive is a remote worker, or someone at another plant, employed by a company headquartered in the metro. *Check:* read the job-location line on the profile, or match it to the WARN filing city.
3. **Graduation year used as a mobility signal.** Ranking by "years since graduation" looks like a career-stage measure but works as age screening. *Check:* confirm that no field in the rubric is a date, and that tenure means current job only.
4. **Recreating a residency requirement.** If the tie slate becomes your only pool, you have rebuilt the local-only pattern the EEOC described. *Check:* run the open search in parallel and apply the same written criteria to both.
5. **Inferred family reasons in copy.** A line like "closer to your parents" asserts something about family status that you do not know and should not use. *Check:* every message names only the documented school or employer.
6. **Overcounting the pool with IRS inflows.** Raw inflows include newcomers, and flows under 20 people are suppressed, so the number is inflated in some places and missing in others. *Check:* apply the ACS returner share before you quote a pool size, and present it as a range.
7. **Trusting a seniority filter.** In Refolk's index, a Senior filter on Manufacturing Engineer returned zero against 34,336 for the title alone. An empty result can be a broken filter. *Check:* query the title on its own first and record the count at every filter.
8. **Duplicates across GitHub and LinkedIn.** One person appears twice, takes two slots in the ranking, and may get two messages. *Check:* match on name, employer, and dates before you rank.

Failure modes 1 and 2 cost you credibility. Messaging someone about "your years in Wichita" when they worked remotely for a Wichita company tells them you did not read their profile. Failure modes 3 to 5 cost more than that, which is why they get their own checklist item below.

## What should the first message say?

Name the documented school or employer, the role, the place, and the pay, in under 400 characters. Leave out anything you inferred about the person's life. The tie is the reason the message is credible. Sentiment about home is not.

The evidence on outreach is thin, so here is what exists and what does not:

- **Common former employer.** LinkedIn reports that mentioning a common former employer in a first message raises the chance of a response by 27%. It gives no sample size. This is the only published effect size close to a location tie.
- **Personalisation.** LinkedIn reports 40% higher acceptance for personalised InMails.
- **Length.** LinkedIn reports a 22% lift for messages under 400 characters.
- **Baseline.** LinkedIn expects InMail senders to see a response rate of roughly 13%.
- **Location tie on its own.** No public study measures the effect of naming a shared city. Do not promise the hiring manager a lift from it.

Because the published lever is a specific employer, name the specific employer or school. "Your time at the Wichita plant" beats "your Kansas roots". With [Refolk](/) the tie evidence you logged in step 4 travels with the record, so the message can quote it rather than paraphrase from memory.

**First message, employer or school tie**

```
Hi {first name} - your time at {employer} in {metro} stood out to me.
[School version: you studied at {school} in {metro}.]

I'm hiring a {role} for an onsite team in {metro}: {one line on the actual work}. Pay is {range}.

If a move to {metro} is worth a conversation, I'd value 15 minutes. If not, no reply needed.
```

*Use the employer line or the school line, never both, and keep the filled-in message under 400 characters.*

## How do you keep the slate current?

Rerun the leaver search while the req is open, add new WARN filings to your seeds as they appear, and recheck each candidate's current location before you send. Then log responses by tie type, so that over time you build the evidence no public study yet provides.

Practical cadence:

- **Rerun the search** monthly while the req is open and every time it reopens. People change jobs and locations, and a new search picks up leavers who moved since the last pass.
- **Refresh the seeds.** Every new WARN filing in the metro is a fresh group of alumni with the same tie.
- **Recheck location before sending.** A candidate who has already moved back is now a local candidate. Move them to the main pipeline.
- **Log outcomes by tie type.** Track response and conversion separately for employer ties and school ties. After a few reqs you will have your own lift figure.

Saving the plain-English query from step 3 makes the rerun a single action instead of rebuilding filters from scratch.

#### Before the slate goes to the hiring manager

- [ ] Every name has one tie, with a link or note proving the campus or site was in the metro.
- [ ] No field in the scoring rubric is a date, a birthplace, or a family inference.
- [ ] The pool estimate in the brief applies the ACS returner share, not raw IRS inflows.
- [ ] The count for the title alone was recorded before any seniority or other filter was added.
- [ ] Duplicates across GitHub, LinkedIn, and the web were merged on name, employer, and dates.
- [ ] An open search for the same req is running on the same written criteria.
- [ ] Every first message is under 400 characters and names only the documented school or employer.
- [ ] Current location was rechecked for every candidate on the day of sending.
- [ ] Responses are logged by tie type for the next build.

## Frequently asked questions

### How do you recruit people back to their hometown for an onsite role?

Start from evidence, not sentiment. List the metro's feeder schools and its anchor, acquired, and defunct employers. Then search for people in your target role who studied or worked at those places and now live elsewhere. Check that each tie was physically in the metro, score mobility from career signals only, and send a short first message that names the school or employer. Hometown appeals and guesses about family belong in neither your targeting nor your copy.

### Can I filter candidates by graduation year to find likely returners?

No. Graduation year works as a stand-in for age, and the ADEA protects people 40 and over. The EEOC does not treat a request for graduation dates as a violation in itself, but it scrutinises such requests closely, and employment counsel advise against obvious attempts to establish age in selection. Many returners do come home in their 40s and 50s, and that pattern is exactly why the rubric must rank on tie evidence and current tenure, never on dates.

### How many candidates should a return-to-region slate have?

Aim for 30 to 60 ranked, deduplicated names for one req. A typical build starts with 150 to 400 raw leavers from the school and employer seeds. Tie verification and the req's written criteria cut that to 60 to 120, and scoring and dedupe bring it to the final slate. If you end up well below 30, widen the employer seeds before you loosen the criteria.

### Does mentioning a shared city in outreach improve response rates?

No public study measures the effect of naming a location tie. The closest published figure comes from LinkedIn: mentioning a common former employer in a first message raises the chance of a response by 27%, though LinkedIn gives no sample size. So name the specific employer or school, which is the version with evidence behind it, and log your own response rates by tie type to build local evidence.

### Is sourcing only people with local ties a residency requirement?

It can turn into one if it is your only channel. EEOC national origin guidance describes a case where advertising only within a predominantly White local area, together with a pre-application residency requirement, produced a disparate impact. Use the tie as a sourcing lens and a message hook, never as a requirement to apply or advance. Run an open search for the same req alongside it, using the same written criteria.

### What data shows how many people move back to a metro?

No published series shows it directly. IRS Statistics of Income migration data gives annual county inflows but has no birthplace field, so it mixes returners with newcomers and suppresses flows under 20 people. ACS microdata can flag people returning to their birth state, but only at state level. Use IRS for volume, apply the ACS returner share, and add PSEO out-of-state counts for each seed school.

---

*From the Refolk guide library. I revise these guides rather than replacing them, so the current version is always at https://www.refolk.ai/guides/return-to-region-sourcing-playbook*