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/vectorize-io/hindsight/code-reviewnpx skills add vectorize-io/hindsight --skill code-reviewgit clone --depth 1 https://github.com/vectorize-io/hindsightWhat 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 | $0.00035 | $0.07430 |
| Opus 5 | $0.00017 | $0.03715 |
| Sonnet 5 | $0.00007 | $0.01486 |
| Haiku 4.5 | $0.00003 | $0.00743 |
Grade A, and why
code-review 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 yesterday.
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 — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Review all changed code against the project's quality standards and coding conventions.
Code Standards
Read and internalize these standards before writing code. The review steps below verify compliance.
Python Style
- Python 3.11+, type hints required
- Async throughout (asyncpg, async FastAPI)
- Pydantic models for request/response
- Ruff for linting (line-length 120)
- No Python files at project root - maintain clean directory structure
- Never use multi-item tuple return values — not even for internal/private functions. Always use a dataclass or Pydantic model. No exceptions, no "it's just two values" shortcuts. If a function returns more than one value, define a named type for it.
Type Safety with Pydantic Models
NEVER use raw dict types for structured data — this applies to all code, including internal helpers and private functions. If the dict has known keys, it must be a dataclass or Pydantic model:
- Use Pydantic
BaseModelfor all data structures passed between functions - Use
@dataclassfor lightweight internal data containers when Pydantic validation isn't needed - Add
@field_validatorfor type coercion (e.g., ensuring datetimes are timezone-aware) - Avoid
dict.get()patterns - use typed model attributes instead - Parse external data (JSON, API responses) into Pydantic models at the boundary
- This catches type errors at parse time, not deep in business logic
- The only acceptable
dictusage is for truly dynamic/unknown keys (e.g., arbitrary metadata, JSON blobs with no fixed schema)
# BAD - error-prone dict access
def process(data: dict) -> str:
return data.get("name", "") # No validation, silent failures
# GOOD - typed and validated
class UserData(BaseModel):
name: str
created_at: datetime
@field_validator("created_at", mode="before")
@classmethod
def ensure_tz_aware(cls, v):
if isinstance(v, str):
v = datetime.fromisoformat(v.replace("Z", "+00:00"))
if v.tzinfo is None:
return v.replace(tzinfo=timezone.utc)
return v
def process(data: UserData) -> str:
return data.name # Type-safe, validated at construction
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.
- yesterday First seen · 372 lines · 35 tokens per session scan A c24978d06b8f
code-review is a skill published in the GitHub repository vectorize-io/hindsight (21,822 stars, last pushed 2d ago), licensed MIT. It adds 35 tokens to every session and 7,430 once invoked, about $0.0002 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 2026-08-30.
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