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 skills/randomm/oo/oo-learn-patternsnpx skills add randomm/oo --skill oo-learn-patternsgit clone --depth 1 https://github.com/randomm/ooWhat 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 | $0.00074 | $0.01978 |
| Opus 5 | $0.00037 | $0.00989 |
| Sonnet 5 | $0.00015 | $0.00396 |
| Haiku 4.5 | $0.00007 | $0.00198 |
Grade A, and why
oo-learn-patterns scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| **Unknown** | Anything else (curl, docker, etc.) | Pass through (safe default) | How it starts
The opening of the file, as written. The whole thing — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
oo-learn-patterns
Create project-specific output patterns so oo can compress verbose command
output into terse summaries for AI coding agents.
Workflow
1. Detect project root and toolchain
Find the git root (git rev-parse --show-toplevel) and scan for toolchain
markers to determine which commands the project uses:
| Marker file | Commands to pattern |
|---|---|
Cargo.toml |
cargo test, cargo build, cargo clippy, cargo fmt --check |
package.json |
npm test, npm run build, npx jest, npx eslint, npx tsc |
pyproject.toml / setup.py / requirements.txt |
pytest, ruff check, mypy, pip install |
go.mod |
go test ./..., go build ./..., go vet ./... |
Makefile / CMakeLists.txt |
make, cmake --build |
Dockerfile / docker-compose.yml |
docker build, docker compose up |
terraform/ / *.tf |
terraform plan, terraform apply |
.github/workflows/ |
inspect YAML for additional commands |
Also check for CI config files (.github/workflows/*.yml, .gitlab-ci.yml,
Jenkinsfile) to discover commands actually used in the project.
2. Create the patterns directory
mkdir -p <git-root>/.oo/patterns
3. Author one .toml file per command
For each discovered command, create a pattern file in .oo/patterns/.
Name files descriptively: cargo-test.toml, npm-build.toml, etc.
Use the TOML format reference below. Key principles:
command_matchis a regex tested against the full command string[success]extracts a terse summary from passing output via named captures[failure]filters noisy failure output to show only actionable lines- An empty
summary = ""suppresses output entirely on success (quiet pass) - Omit
[failure]to show all output on failure (sensible default)
4. Validate patterns
After creating patterns, verify them:
oo patterns # lists all loaded patterns (project + user + builtins)
oo <command> # run a real command to test the pattern
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 · 257 lines · 74 tokens per session scan A 9ac9985b0170
oo-learn-patterns is a skill published in the GitHub repository randomm/oo (21 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 74 tokens to every session and 1,978 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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