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 mitodl/agent-kit --skill django-api-benchmarkgit clone --depth 1 https://github.com/mitodl/agent-kitWrote 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/mitodl/agent-kit/django-api-benchmark)<a href="https://agentmods.dev/skills/mitodl/agent-kit/django-api-benchmark"><img src="https://agentmods.dev/badge/skills/mitodl/agent-kit/django-api-benchmark/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/mitodl/agent-kit/django-api-benchmark"><img src="https://agentmods.dev/badge/skills/mitodl/agent-kit/django-api-benchmark.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 | $0.00169 | $0.05720 |
| Opus 5.5 | $0.00068 | $0.02288 |
| Sonnet 5.5 | $0.00034 | $0.01144 |
| Haiku 4.5 | $0.00017 | $0.00572 |
Grade A, and why
django-api-benchmark 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 6d 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 — 448 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmarking a Django API change
A performance PR that says "this should be faster" is a guess. This skill turns it into a measurement: seed a production-shaped throwaway database, run the same request on both git refs against identical rows, and attribute the difference query by query.
The mechanics are handled by
mitol-django-benchmark.
You do not write a harness. You write one TOML file describing the shape and
the target, run one command, and read the JSON it produces. Everything the
package enforces — refusing to measure under a profiler, seeding once across
both arms, verifying each arm ran the ref you think — is covered by its own
tests, so it is not your job to re-derive.
What is left is the part no package can do for you, and it is the part that decides whether the number means anything:
- Getting the shape right. Factory defaults are nothing like production. A seed that is off structurally produces a number with no bearing on the endpoint you care about.
- Not fitting the seed to the answer you want. Calibrating against the query you changed is circular, and it will manufacture a confident, large, wrong result.
- Knowing what to do when the benchmark says the code is fine. A clean local result against a production stall is a finding, not a dead end, and Step 7 is where it leads.
Related: drf-api-performance is about
writing fast endpoints. This is about proving one got faster.
Step 0 — Install it
uv add --dev "mitol-django-benchmark[drf,factories,django,postgres]"
Use the postgres2 extra instead of postgres on a project still using
psycopg2. Without a psycopg instrumentation extra the run still works, but the
trace pass reports that it captured no database spans and you lose per-query
attribution.
If the project has no benchmarks/ directory yet:
ol-benchmark init --project # benchmarks/benchmark.toml, committed
ol-benchmark init --local # benchmarks/benchmark.local.toml, gitignored
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 448 lines · 169 tokens per session scan A 1453840d43a6
django-api-benchmark is a skill published in the GitHub repository mitodl/agent-kit (5 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 169 tokens to every session and 5,720 once invoked, about $0.0007 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 2026-10-01.
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