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 skills add steph-dove/klaussy-agents --skill httpx-self-reviewgit clone --depth 1 https://github.com/steph-dove/klaussy-agentsWrote 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/steph-dove/klaussy-agents/httpx-self-review)<a href="https://agentmods.dev/skills/steph-dove/klaussy-agents/httpx-self-review"><img src="https://agentmods.dev/badge/skills/steph-dove/klaussy-agents/httpx-self-review/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/steph-dove/klaussy-agents/httpx-self-review"><img src="https://agentmods.dev/badge/skills/steph-dove/klaussy-agents/httpx-self-review.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 | $0.00074 | $0.00919 |
| Opus 5 | $0.00037 | $0.00460 |
| Sonnet 5 | $0.00015 | $0.00184 |
| Haiku 4.5 | $0.00007 | $0.00092 |
Grade A, and why
httpx-self-review 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 11d 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.
This is a copy
97% identical to fastapi-self-review — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review the change you just made before you call it complete. This is the gate between "I wrote code" and "it's done" — run it on your own diff and fix what it surfaces, don't just report.
Step 1: Get the diff
Look at exactly what changed — git diff (unstaged), git diff --cached (staged), and untracked files. Read the full changed files, not only the hunks; a problem often lives in the context around an edit.
Step 2: Walk the checklist
Go through every item against the diff. For each, either confirm it holds or fix it now.
Reuse before reinvention
- Does this add a function, helper, type, or constant that already exists somewhere in the repo? Search first, then reuse it instead.
- Is any logic duplicated from another module? Call the existing code, don't copy it.
Built-ins and existing dependencies
- Did you hand-roll something the standard library or an already-installed dependency provides (deep-clone, debounce, grouping, UUID, HTTP, parsing, date math)? Replace it with the built-in.
- Did you add a new third-party dependency? That's a decision to raise with the user, not to slip in — flag it.
Comments
- Deleting is the default; keeping one needs a reason you could defend in review. Go comment by comment and cut every one that restates the code, narrates steps, or reads as changelog ("Now we handle…", "Added to fix…").
- What survives gets one sentence, and only where it earns its place: a why, a gotcha, an invariant, a link. A second sentence usually means the first one restated the code.
- Prefer a clearer name over a comment.
Imports
- Did you import inside a function or method? Hoist it to the top of the file. It reads as an agent tell — the import got written where the need surfaced, not where it belongs — and it hides a module's dependencies from anyone scanning the file.
- Keep it local only when it earns it: breaking an import cycle, or deferring an optional/expensive dependency. Say which, in a
# noqacomment on the line.
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.
- 11d ago First seen · 61 lines · 74 tokens per session scan A 377a32114ae8
httpx-self-review is a skill published in the GitHub repository steph-dove/klaussy-agents (16 stars, last pushed 14d ago), licensed MIT. It adds 74 tokens to every session and 919 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to fastapi-self-review, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
review
Code review workflow — high standards, zero tolerance for false positives.
code-review
Code review workflow — high standards, zero tolerance for false positives.
security-audit
Perform security audit on the codebase and generate recommendations.
review-pr
Review a GitHub PR with security, performance, and quality checks.
find-simplifications
Use for a periodic repo-wide sweep of qwen-code for accumulated excess surface — dead components and files, orphaned locale keys, exports nothing consumes, added-then-removed scaffolding — filing candidates on a tracking issue and landing only what a maintainer has said yes to. Repo-wide and evidence-first; every…
swarm-pr-review
Run a graph-guided, tool-augmented PR review using context packing, parallel exploration, mandatory repository-agnostic risk-family coverage with dispatch scaled to diff size and risk, independent reviewer validation, critic challenge, and metrics writeback. Use for deep pull request review with low false-positive…