Pydantic AI is a typed Python SDK for building AI agents that can use different language models and interfaces, including voice, image generation, and embeddings. Developers use it for applications ranging from structured data extraction to long-running multi-agent work. The catalogue entries provide workflows and configuration for its coding-agent and harness features.
Borrowing it
Nothing to install: this file belongs to pydantic/pydantic-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pydantic/pydantic-ai/main/.agents/skills/refresh-issue-brief/SKILL.mdgit clone --depth 1 https://github.com/pydantic/pydantic-aiWrote 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/skills/pydantic/pydantic-ai/refresh-issue-brief)<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/refresh-issue-brief"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/refresh-issue-brief/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/skills/pydantic/pydantic-ai/refresh-issue-brief"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/refresh-issue-brief.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 | $0.00066 | $0.01294 |
| Opus 5.5 | $0.00026 | $0.00518 |
| Sonnet 5.5 | $0.00013 | $0.00259 |
| Haiku 4.5 | $0.00007 | $0.00129 |
Grade A, and why
refresh-issue-brief 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 yesterday.
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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Refresh Brief
Re-fetch the issue(s) referenced in .claude/skills/branch-context/issue-brief.md and update the brief only where genuinely new content warrants it.
When to run
Only when the user tells you to. Typical triggers:
- "check the latest comments on the issue"
- "the issue got updated, re-read it"
- "has anything changed upstream on the issue?"
Never run autonomously. The brief is meant to stay stable across many sessions — refreshing it every time would defeat the purpose.
Process
1. Parse current brief
Read .claude/skills/branch-context/issue-brief.md frontmatter:
issues:list (numbers, URLs, the last fetchedupdated_at, andcomments_fingerprint)last_fetched_atlast_fetched_comment_count
If issues: is empty (free-text problem, no linked issue), print: "No linked issue to refresh. Update the brief manually if scope changed." and exit.
2. Fetch current state
For each issue:
gh issue view <N> --json title,state,comments,labels,body,updatedAt
Collect:
- Current comment count
- Any comments with
createdAt > last_fetched_at - Current title / state / labels (flag changes)
- Whether
updatedAtdiffers from the issue's storedupdated_at. Treat a missing stored value as stale so briefs created before this field was added get one full comparison. - A fresh comment-state fingerprint from
.agents/skills/branch-context/issue-comment-fingerprint <issue-number>. The helper paginates commentidandupdatedAtthrough GraphQL becausegh issue viewomits commentupdatedAt. A mismatch detects added, edited, or deleted comments. Treat a missing stored fingerprint as stale.
3. Compare
For each issue, build a short summary of deltas:
- New comments (count + one-line summary of each)
- Edited or deleted comments? When the comment fingerprint changes, re-read every current comment and compare its decision-bearing content with the brief; do not inspect only newly created ones.
- Title changed? state changed (reopened/closed)? labels added/removed?
- Body edited? A changed
updatedAtrequires comparing the complete current body with the brief's scope, criteria, constraints, and references; never use a prefix comparison.
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.
- yesterday First seen · 127 lines · 66 tokens per session scan A a3412bf5ff76
refresh-issue-brief is a skill published in the GitHub repository pydantic/pydantic-ai (20,771 stars, last pushed today), licensed MIT. It adds 66 tokens to every session and 1,294 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-10-21.
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