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 instructions/asisaga/linkedin.asisaga.com/pythongit clone --depth 1 https://github.com/ASISaga/linkedin.asisaga.comWhat 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 | $0.00737 | $0.00737 |
| Opus 5 | $0.00368 | $0.00368 |
| Sonnet 5 | $0.00147 | $0.00147 |
| Haiku 4.5 | $0.00074 | $0.00074 |
Grade A, and why
linkedin.asisaga.com python.instructions.md 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Coding Standards
Style & Formatting
- Follow PEP 8 conventions (4-space indentation, 88-char line limit)
- Use type hints on all function signatures (
-> dict,List[str], etc.) - Use
from __future__ import annotationsat the top of each module - Write Google-style docstrings for public functions and classes
- Use double-quoted strings consistently
Async Patterns
All workflows are async functions using await:
@app.workflow("workflow-name")
async def my_workflow(request: WorkflowRequest) -> Dict[str, Any]:
agents = await request.client.list_agents()
status = await request.client.start_orchestration(...)
return {"orchestration_id": status.orchestration_id, "status": status.status.value}
- Always
awaitSDK calls — they are all coroutines - Use
asyncio_mode = "auto"(configured inpyproject.toml) for pytest-asyncio - Avoid blocking I/O in async functions
Type Hints
from typing import Any, Dict, List, Callable
async def select_c_suite_agents(client: AOSClient) -> List[AgentDescriptor]: ...
async def my_workflow(request: WorkflowRequest) -> Dict[str, Any]: ...
- Use
Dict,List,Anyfromtypingfor Python 3.10 compatibility - Use
Callable[[ArgType], ReturnType]for function parameters
Imports
Order: stdlib → third-party → local, with blank lines between groups:
from __future__ import annotations
import logging
from typing import Any, Dict, List
from aos_client import AOSApp, WorkflowRequest
Logging
Use the module-level logger — never use print():
logger = logging.getLogger(__name__)
logger.info("Orchestration started: %s", orchestration_id)
Error Handling
Raise ValueError with descriptive messages when required agents are unavailable:
if not agent_ids:
raise ValueError("No matching agents available in the catalog")
Testing
pip install -e ".[dev]"
pytest tests/ -v # Run all tests
pytest tests/ -v -k "test_name" # Run specific test
pylint src/ # Lint
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 · 104 lines · 737 tokens per session scan A 77ec2a6e1b80
linkedin.asisaga.com python.instructions.md is an instructions file published in the GitHub repository ASISaga/linkedin.asisaga.com (0 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 737 tokens to every session, about $0.0037 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 2026-08-31.
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