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 commands/swoopeagle/standardgraph/testgit clone --depth 1 https://github.com/swoopeagle/standardgraphWhat 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 | $0.00000 | $0.00279 |
| Opus 5 | $0.00000 | $0.00139 |
| Sonnet 5 | $0.00000 | $0.00056 |
| Haiku 4.5 | $0.00000 | $0.00028 |
Grade A, and why
test 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 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.
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.
What it actually says
Run the StandardGraph MCP test suite and report results.
Execute:
DB_PATH=~/.standardgraph/common_core.db uv run python scripts/mcp_test.py 2>&1
The suite has 272 tests across 14 sections:
- list_systems
- search_standards — subject coverage
- search_standards — edge cases
- get_progression
- lookup_standard
- map_standard
- Data integrity (embeddings, relationships, nulls, duplicates)
- Prerequisites & successors
- Grade range filters
- Precomputed crosswalk paths
- US Math — all 51 states, searches, and crosswalks (rigorous)
- AP Math — all 4 systems, searches, and crosswalks (rigorous)
- IB Math — MYP/DP counts, searches, progression, crosswalk (rigorous)
- Cross-system comparisons (AP↔IB↔Cambridge)
After running:
- Report the summary line (N passed | N failed | N warnings | N total)
- If there are failures, show each failed check with its detail message
- If all pass, confirm "272/272 — all clear"
- If performance checks are borderline (list_systems > 4s), note it as a watch item
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 · 29 lines · 0 tokens per session scan A 67587eaf8955
test is a command published in the GitHub repository swoopeagle/standardgraph (5 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 279 tokens. 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 2026-08-31.
Other commands, from other repositories
setup-elevenlabs.es
Command "setup-elevenlabs.es" from minicoohei/ai-agent-camp, covering configuración de la api de elevenlabs, step 0: verificar el progreso de configuración, lo que hará en esta sesión, verificación de preparación and step 1: abrir elevenlabs en el navegador.
setup-elevenlabs
Command "setup-elevenlabs" from minicoohei/ai-agent-camp, covering elevenlabs api セットアップ, step 0: セットアップ進捗の確認, このセッションでやること, 準備チェック and step 1: ブラウザでelevenlabsを開く.
setup-fal
Command "setup-fal" from minicoohei/ai-agent-camp, covering fal.ai api セットアップ, step 0: セットアップ進捗の確認, このセッションでやること, 準備チェック and step 1: ブラウザで fal.ai を開いてアカウントを作成する.
setup-github
Command "setup-github" from minicoohei/ai-agent-camp, covering github アカウント設定とリポジトリ作成, step 0: セットアップ進捗の確認, このセッションでやること, セッション開始前の確認 and step 1: github アカウントの確認.
setup-bigquery.en
Command "setup-bigquery.en" from minicoohei/ai-agent-camp, covering bigquery / gcp authentication setup, step 0: check setup progress, what you'll do in this session, readiness check and step 1: install gcloud cli.
setup-bigquery.es
Command "setup-bigquery.es" from minicoohei/ai-agent-camp, covering configuración de autenticación bigquery / gcp, step 0: verificar el progreso de configuración, lo que hará en esta sesión, verificación de preparación and step 1: instalación de gcloud cli.