Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/docxology/templatenpx agentmods add skills/docxology/template/siaWrote 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/docxology/template/sia)<a href="https://agentmods.dev/skills/docxology/template/sia"><img src="https://agentmods.dev/badge/skills/docxology/template/sia.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00053 | $0.00414 |
| Opus 5 | $0.00026 | $0.00207 |
| Sonnet 5 | $0.00011 | $0.00083 |
| Haiku 4.5 | $0.00005 | $0.00041 |
Grade A, and why
infrastructure-sia 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 4d 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
SIA Harness
Deterministic harness for Meta → Target → Feedback generation loops with public/private task splits. Implements contracts inspired by hexo-ai/sia; does not vendor upstream code.
Commands
uv run python -m infrastructure.sia.cli validate projects/templates/template_sia/tasks/mini_classify
uv run python -m infrastructure.sia.cli inspect-run RUN_SUMMARY_JSON [--json]
uv run python scripts/pipeline/stage_02_analysis.py --project templates/template_sia
Public API
from infrastructure.sia import (
RunConfig,
TaskLayout,
load_agent_execution,
run_evaluation,
run_sia_loop,
validate_task_dir,
)
Task layout
Each task directory exposes:
data/public/— agent-visible inputsdata/public/task.md— required task description (validate_task_dirhard-fails if absent)data/public/evaluate.py— writesresults.jsonwithmetric_name,metric_value,n_samples
data/private/— evaluation-only labelsreference/— baseline target agent (reference/reference_target_agent.pyrequired)
Exemplar project
projects/templates/template_sia/ runs
fixture replay by default (live=False). Pass --live-sia on
scripts/run_sia_loop.py for opt-in Ollama-backed feedback (not CI) — the
feedback note is illustrative, not applied; live mode performs no code mutation
and uses no sandbox, so cross-generation improvement is shown only via fixtures.
What ships with it
12 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.
- __init__.py 1.8 KB runs code
- __main__.py 177 B runs code
- AGENTS.md 3.1 KB
- cli.py 3.7 KB runs code
- context_ledger.py 1.5 KB runs code
- evaluation_runner.py 3.0 KB runs code
- execution_logs.py 1.9 KB runs code
- live_llm.py 1.6 KB runs code
- loop_runner.py 11 KB runs code
- models.py 4.8 KB runs code
- README.md 498 B
- task_layout.py 1.9 KB runs code
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.
- 4d ago First seen · 50 lines · 53 tokens per session scan A 9ba99a487356
infrastructure-sia is a skill published in the GitHub repository docxology/template (19 stars, last pushed today), licensed Apache-2.0. It adds 53 tokens to every session and 414 once invoked, about $0.0003 per session on Opus 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 2026-09-03.
Other skills, from other repositories
launch-check
Run a pre-demo/pre-launch audit of the app - test data, broken screens, missing error states, brand consistency. Use when asked to 'demo check', 'launch check', 'pre-demo audit', 'check before user interview', 'audit before demo'.
cross-validation
The cross-validation skill enables the cross-validation agent to validate consistency between agent outputs and their sources. It provides the rules registry and validation runner that check pairwise consistency across the agent pipeline.
lang-go
Go toolchain: golangci-lint, go vet, go test, go mod. CI gate commands, file layout, interface patterns. Use when working on a Go project.
lang-python
Python toolchain: ruff, mypy, pytest, pytest-cov, uv/pip. CI gate commands, file layout, pyproject.toml config. Use when working on a Python project.
live-system-verification
Stands up a real running instance of the affected component(s) and runs testtype:live-system acceptance criteria against it — real HTTP calls, real process behavior, real evidence. Not a mock, not a simulated environment. Use when tasks.json contains any acceptance criterion tagged testtype: live-system.
performance-smoke-testing
Validate basic performance characteristics to detect regressions. Use when running lightweight load tests to measure latency, throughput, and error rates.