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 nullhack/temple8 --skill derive-source-stubsgit clone --depth 1 https://github.com/nullhack/temple8Wrote 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/nullhack/temple8/derive-source-stubs)<a href="https://agentmods.dev/skills/nullhack/temple8/derive-source-stubs"><img src="https://agentmods.dev/badge/skills/nullhack/temple8/derive-source-stubs/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/nullhack/temple8/derive-source-stubs"><img src="https://agentmods.dev/badge/skills/nullhack/temple8/derive-source-stubs.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 | $0.00029 | $0.00609 |
| Opus 5.5 | $0.00012 | $0.00244 |
| Sonnet 5.5 | $0.00006 | $0.00122 |
| Haiku 4.5 | $0.00003 | $0.00061 |
Grade A, and why
derive-source-stubs 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 1mo 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
Derive Source Stubs
- Load [[software-craft/source-stubs]], [[software-craft/solid]], [[software-craft/object-calisthenics]], [[software-craft/design-patterns]], [[architecture/context-mapping]] — source stub conventions, the quality principles, the pattern catalog, and the inter-context relationship patterns.
- Read
.cache/<session_id>/journal.md(always present — bootstrapped at plan entry bymodel-data-schema). IF it carries escalation findings from build THEN adjust the source stubs of the contracts it names; otherwise skip. - Read the test .py imports and bodies — every type constructed, method called, and relationship asserted in a body is the specification of the source surface. Emit a matching .pyi signature (with an ellipsis body) for every type the tests reference, wherever the source lives.
- Define types, relationships, and compositions to satisfy SOLID and Object Calisthenics per [[software-craft/solid]], [[software-craft/object-calisthenics]]. IF a relationship emits a smell at the type surface (a kind-field that will switch, parallel hierarchies, construction scattered across callers, refused bequest) THEN reach for the matching pattern per [[software-craft/design-patterns]] — but KISS/YAGNI first: prefer the simplest structure that removes the smell, and prefer Python idioms (a Protocol, a dataclass, a first-class callable, a generator, a decorator) over GoF ceremony. Do not apply a pattern speculatively; Speculative Generality is the smell that rejects it.
- IF a relationship bridges two bounded contexts (an external service, an upstream module, a shared data shape) THEN pick the inter-context pattern per [[architecture/context-mapping]] — default to an Anti-Corruption Layer (an adapter that translates the foreign model into ours) for any external dependency; reach for Shared Kernel only for a small value-object set both contexts truly co-own.
- Do not prescribe layout — no hardcoded module structure. External connector and adapter stubs are not a separate category; they live under the package alongside everything else.
- Handle config per [[software-craft/secrets-and-config]]: non-secret config in the workspace
.env; secrets in~/.secrets/<project>.env(out-of-workspace), loaded withdotenv_values()into a frozen typed Settings, never intoos.environ. A Settings model is optional and, if warranted, is a normal source stub. - IF a value's representation is ambiguous (e.g. a database URL passed as bare string) THEN pin one canonical form once (the
sqlite:///pathURL form) and state it in the stub so every consumer agrees per [[software-craft/source-stubs]].
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.
- 1mo ago First seen · 16 lines · 29 tokens per session scan A 92a821fec3e4
derive-source-stubs is a skill published in the GitHub repository nullhack/temple8 (11 stars, last pushed 29d ago), licensed MIT. It adds 29 tokens to every session and 609 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
agentic-test-audit
Audit a pytest suite: surviving mutants and untested code, flaky, order-dependent and slow tests, and removal candidates checked on held-out mutants. Report-only.
agentic-verify
Check that a Python change is caught by tests: run every mutant of the changed lines and write tests until they catch them. Reports evidence instead of a diff to read.
building-pydantic-ai-agents
Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), workspaces, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydanticai, or asks to build an AI agent, add tools/capabilities, attach a workspace, defer capability…
test-generator
Generate pytest test cases for Python functions and classes.
python
Use when the task is Python itself, in any framework or none: PEP 695 generics, mypy --strict typing, dataclass/Protocol/TypedDict/Enum choices, asyncio.TaskGroup, stdlib idioms, src/ layout + pyproject.toml with uv, ruff+mypy+pytest gate. NOT a FastAPI/ASGI service (that is fastapi), NOT a deep pytest suite (that is…
tdd
Drive a red → green → refactor cycle for the active task.