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Skills/Code review/legioncodeinc/littlebird-skills

client-health-radar

legioncodeinc/littlebird-skills
SkillLoads cleanly

Per-client health rollup for agencies, consultancies and freelancers. Trigger on "which client is about to churn", "client health check", "am I losing this…

Scores
81
out of 100 · grade B+

2026-08-18

Works

40% of the score

100/100

  • Loads cleanly: valid frontmatter, required fields present, no dangling references.

Maintained

25% of the score

100/100

  • no commits in the last 12 weeks
  • repo is less than two weeks old

Adopted

20% of the score

10/100

  • 4 stars on the source repo.

Documented

15% of the score

90/100

  • 4,668 words with worked examples.
  • Ships 22 bundled files.
Loads cleanly. Every structural check Claude Code needs to register this passed. Last commit today.

Install

npx skills add legioncodeinc/littlebird-skills/client-health-radar

What it says it does

Per-client health rollup for agencies, consultancies and freelancers. Trigger on "which client is about to churn", "client health check", "am I losing this client", "weekly client review", "is this account at risk", "scope creep on this client", "which client is eating my margin", "client went quiet". Builds a ranked risk view per client from meetings, message threads and captured dashboards: unmet promises in both directions, silence gaps against that client's own cadence baseline, scope creep with the transcript quote where the ask happened, room and register changes, and payment or renewal signals. Bands with named evidence, never a score. Internal view only, nothing goes to the client.

Every number above came out of reading the file and its repository. Nothing is a judgement call, nothing is a model’s opinion, and nothing can be paid for. The full method is published. Source last committed 2026-08-17.

The record

Type
Skill
Category
Code review
Repository
legioncodeinc/littlebird-skills
Last commit
today · 2026-08-17
Repository stars
4
Installs
not in the skills.sh registry
Licence
AGPL-3.0
Instruction length
4,668 words
Bundled files
22
View the source on GitHub

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