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Post-deploy monitoring: watch production after a deploy and alert on regressions
Passed 21 of 22 test cases with the skill, and 11 of 22 without it.
and 9 more in the full report
10 fixed11 passed either way1 still failing0 broke2 errored
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
Our deployment to https://app.org is fully verified, and the post-deploy report is clean. We want to update our stored baseline `.canary/baselines/baseline.json` so future deployments compare against this verified build's snapshot (`.canary/reports/latest-snapshot.json`). Write the exact shell command to overwrite the baseline file and report the updated git commit.
| Checked | Without skill | With skill |
|---|---|---|
| executes cp .canary/reports/latest-snapshot.json .canary/baselines/baseline.json | Passed | Passed |
| prints a confirmation message containing the updated git commit SHA | Missed | Passed |
The instructions your model reads when the skill loads.
Watch a live application after deployment. Alert on errors and regressions. Compare against a pre-deploy baseline.
Two modes:
--baseline: capture the current state BEFORE deployingParse the user's arguments and detect the deployment context.
# Detect current branch and recent deploy commit
git branch --show-current
git log --oneline -5
# Auto-detect platform from config files
[ -f fly.toml ] && echo "PLATFORM: fly"
[ -f render.yaml ] && echo "PLATFORM: render"
[ -f vercel.json ] && echo "PLATFORM: vercel"
[ -f netlify.toml ] && echo "PLATFORM: netlify"
[ -f Procfile ] && echo "PLATFORM: heroku"
[ -f railway.toml ] && echo "PLATFORM: railway"
# Check for health endpoint
curl -sf "${URL}/health" -w "\n%{http_code}" 2>/dev/null | tail -1
curl -sf "${URL}/api/health" -w "\n%{http_code}" 2>/dev/null | tail -1
Create the working directory:
mkdir -p .canary/baselines .canary/reports .canary/screenshots
test cases passed, vs 11 / 22 without
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