Hi,
I've put a set of AI skills in the incubator repo under tools/skills. [1] They
wrap up jobs we do over and over, so the mechanical parts of report review,
release checking, and the rest are less manual. Everything there is source: the
installable packages are built from it, not committed.
The skills:
• monthly-report-review: pulls the cohort due this month, flags who hasn't
filed, checks narratives against health data, drafts shepherd comments per
podling.
• release-vote-reviewer: walks a general@ vote thread through artifacts,
signatures, checksums, DISCLAIMER, "incubating" in the filenames and the vote
maths, then drafts the +1 or the -1 with reasons.
• graduation-packet-builder: maturity evidence, name search status,
community and release history, plus a draft DISCUSS email and board resolution.
• retirement-shepherd: the reverse case, evidence that a podling has
stalled, the retirement emails and a shutdown checklist.
• board-report: a draft of the quarterly report, stats, graduations,
retirements, cohort summary, and items needing board attention.
• branding-sweep: trademark and branding check across all current podlings,
with a ranked fix list.
• podling-onboarding: proposal review against the checklist, then tracking
SGA, ICLAs, infra, and the first report date.
• podling-name-clearance: PODLINGNAMESEARCH end-to-end, with the write-up
in the required format.
Prerequisites: Python 3 to build, and local checkouts of the MCP server repos
the skills read from (reports, ipmc, health, podlings, trademark, mail, policy,
releases, cwiki). They run those repos’ code, so the paths either need to match
the defaults or be edited in the skill. Build with python3 package-skills.py
--all and install the resulting .skill files in your AI client.
Corrections and improvements welcome. There are eval fixtures under evals/ for
the four that involve real judgment, so it's easy to check that a change hasn't
made things worse.
Kind regards,
Justin
1. https://github.com/apache/incubator/tree/master/tools/skills
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