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.
git clone --depth 1 https://github.com/DougTrajano/pydantic-ai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/dougtrajano/pydantic-ai-skills/upstream-watch)<a href="https://agentmods.dev/commands/dougtrajano/pydantic-ai-skills/upstream-watch"><img src="https://agentmods.dev/badge/commands/dougtrajano/pydantic-ai-skills/upstream-watch/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/dougtrajano/pydantic-ai-skills/upstream-watch"><img src="https://agentmods.dev/badge/commands/dougtrajano/pydantic-ai-skills/upstream-watch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00031 | $0.02561 |
| Opus 5 | $0.00015 | $0.01281 |
| Sonnet 5 | $0.00006 | $0.00512 |
| Haiku 4.5 | $0.00003 | $0.00256 |
Grade A, and why
upstream-watch scanned grade A with 0 findings 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 5d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Upstream watch
Two upstream projects can break this package without any change here:
- pydantic-ai — it wraps the framework and imports several of its private symbols, which no deprecation policy protects.
- pydantic-ai-harness — a required runtime dependency since v2.
SkillsCapabilityis built directly on its publicSkillsclass, and harness is on0.xreleases where its own README says the API may change between minor releases.
This routine is the standing review that catches both. It produces one entry in
.github/upstream-watch.md and, when action is needed, the issue or PR that fixes it.
Run it weekly, or on demand before a release.
1. Establish the lower bound
Read the top entry of .github/upstream-watch.md. Its two "checked through" tags are
the lower bound for this run — you are reviewing everything published after them. Note
today's date for the new entry heading.
2. Collect the releases
gh release list --repo pydantic/pydantic-ai --limit 40
gh release list --repo pydantic/pydantic-ai-harness --limit 20
Everything newer than the lower bound is in scope for both repositories. The
pydantic-ai v1.x backport line is not: pyproject.toml declares
pydantic-ai-slim>=2.38, so v1 is no longer a supported configuration. (Entries before
2026-09 reviewed it because the floor was then >=1.105 — that is why the log mentions it.)
Read each release body with gh release view <tag> --repo <repo>. When a release note is
ambiguous about what a PR actually touched, open the PR's file list rather than guessing —
past entries in the log did exactly this, and it is what turned a scary-sounding release
note into a confident "no action".
3. Assess impact against what this package actually uses
Two narrow surfaces. Check each release against them specifically.
3a. pydantic-ai private imports
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.
- 5d ago Changed · +95 lines 07891af566b0
- 11d ago First seen · 97 lines · 31 tokens per session scan A 75aa627cd676
upstream-watch is a command published in the GitHub repository DougTrajano/pydantic-ai-skills (369 stars, last pushed 4d ago), licensed MIT. It adds 31 tokens to every session and 2,561 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
release-manager
Automated release coordination and deployment with ruv-swarm orchestration for seamless version management, testing, and deployment across multiple packages.
release-swarm
Orchestrate complex software releases using AI swarms that handle everything from changelog generation to multi-platform deployment.
sync-coordinator
Multi-package synchronization and version alignment with ruv-swarm coordination for seamless integration between claude-code-flow and ruv-swarm packages.
release-update
(dev-only) release-update maintainer harness — Claude Code upstream change tracker (release-notes version-delta sweep + docs sync). NOT distributed to user projects.
dashboard-git-init
Initialise a git repo in a directory. Usage /dashboard:git-init [cwd].
dashboard-session-diff
Show file changes (git diff) for a session by id-prefix. Usage /dashboard:session-diff . Runs locally, no LLM.