Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/aeonfun/aeon/deploy-watchernpx skills add aeonfun/aeon --skill deploy-watchergit clone --depth 1 https://github.com/aeonfun/aeonWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00047 | $0.01274 |
| Opus 5 | $0.00023 | $0.00637 |
| Sonnet 5 | $0.00009 | $0.00255 |
| Haiku 4.5 | $0.00005 | $0.00127 |
Grade A, and why
[REPLACE: SKILL_NAME] scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sf -H "Authorization: Bearer $VERCEL_TOKEN" "$URL" > .vercel-deploys.json || \ Copies of this mod
1 near-identical copy found in the catalogue:
- [REPLACE: SKILL_NAME] — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
${var} — Optional. Override the Vercel project slug. If empty, watches
[REPLACE: VERCEL_PROJECT].
Today is ${today}. Watch Vercel deployments for [REPLACE: VERCEL_PROJECT] and alert on [REPLACE: ALERT_ON] within the last [REPLACE: LOOKBACK_HOURS] hours.
Required secrets
VERCEL_TOKEN— personal access token from https://vercel.com/account/tokens. Read scope is enough.- (optional)
VERCEL_TEAM_ID— if the project lives under a team, set this so the API queries the right scope.
If either secret is missing, log DEPLOY_WATCH_NO_TOKEN and exit cleanly — never abort the workflow.
Steps
-
Resolve scope:
PROJECT="${var:-[REPLACE: VERCEL_PROJECT]}" SCOPE_QS="" if [ -n "${VERCEL_TEAM_ID:-}" ]; then SCOPE_QS="&teamId=$VERCEL_TEAM_ID" fi -
Fetch recent deploys — Vercel API v6 lists deployments for a project:
SINCE_MS=$(( $(date -u +%s) * 1000 - [REPLACE: LOOKBACK_HOURS] * 3600 * 1000 )) URL="https://api.vercel.com/v6/deployments?projectId=$PROJECT&since=$SINCE_MS$SCOPE_QS&limit=20" curl -sf -H "Authorization: Bearer $VERCEL_TOKEN" "$URL" > .vercel-deploys.json || \ echo "DEPLOY_WATCH_FETCH_FAIL: $?"Make every Vercel call in-run with
./secretcurl(write the key as{VERCEL_TOKEN}— a bare$VERCEL_TOKENon the line is refused by the Bash permission layer). Read-only status checks and any irreversible action (e.g. triggering a deploy) both run in-run — the irreversible one as the skill's final, fail-closed action. Never defer a read. -
Parse and classify — for each deploy, capture:
uid,state(READY / ERROR / CANCELED / BUILDING / QUEUED),url,target(production / preview),creator,createdAt,meta.githubCommitMessage. -
Apply the alert filter —
[REPLACE: ALERT_ON]is one of:production-failures→ alert whentarget=productionANDstate in {ERROR, CANCELED}.any-failures→ alert on anystate in {ERROR, CANCELED}.slow-builds→ alert when build time > 10× the last-week median for this project.all→ alert on every state transition (noisy — only useful while debugging the skill).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 85 lines · 0 tokens per session scan A 77ba967c3239
[REPLACE: SKILL_NAME] is a skill published in the GitHub repository aeonfun/aeon (706 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 1,274 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
agent-framework-py-release
Use when cutting a Python release for the microsoft/agent-framework monorepo. Triggers on "bump py versions", "cut a python release", "prepare release PR for python", "release py packages", "bump python to X.Y.Z", or similar requests to bump Python package versions and prepare a release PR. Handles all four lifecycle…
python-package-management
Guide for managing packages in the Agent Framework Python monorepo, including creating new connector packages, versioning, and the lazy-loading pattern. Use this when adding, modifying, or releasing packages.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
verify-samples-tool
How to use the verify-samples tool to run, verify, and manage sample definitions in the Agent Framework repository. Use this when adding, updating, or running sample verification.
build-and-test
How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.