mancitrus/system-prompts-and-models-of-ai-tools

mirror of https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git synced 2026-06-17 23:09:35 +00:00

Sami Assiri f79c69ff25 ci(dealix): root GitHub workflows, ai-company track, full Dealix API tree

Made-with: Cursor

2026-05-01 14:03:52 +03:00

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Agents Reference

Every agent extends core.agents.base.BaseAgent and exposes async def run(**kwargs).

Phase 8 — Auto Client Acquisition

IntakeAgent

File: auto_client_acquisition/agents/intake.py

Captures leads from any source, normalizes them, detects duplicates.

Inputs:

payload: dict[str, Any] — raw lead data
source: LeadSource | str — where it came from

Output: Lead dataclass with:

Normalized contact_phone (E.164 via phonenumbers)
Normalized contact_email (lowercase, validated)
Detected locale (ar or en) from message text
dedup_hash — SHA-256 of (email or phone) + company
metadata.is_duplicate flag

Sources supported: website, whatsapp, email, referral, linkedin, cold_outreach, manual, api.

ICPMatcherAgent

File: auto_client_acquisition/agents/icp_matcher.py

Scores leads against an Ideal Customer Profile across 5 weighted dimensions.

Weights (sum to 1.0):

Industry: 0.25
Size: 0.15
Region: 0.20
Budget: 0.20
Pain: 0.20

Output: FitScore with per-dimension scores + overall tier (A/B/C/D) + recommendations.

Default ICP: target industries (tech, real estate, healthcare, education, logistics), GCC regions, SAR 10k–200k budget.

PainExtractorAgent

File: auto_client_acquisition/agents/pain_extractor.py

Hybrid extraction:

Fast keyword pass (local, zero-cost) — scans AR+EN keyword dictionary
LLM enrichment — routed to GLM for Arabic, Claude otherwise

Output: ExtractionResult with pain points, urgency 0–1, likely offer, recommended next step.

QualificationAgent

File: auto_client_acquisition/agents/qualification.py

Generates 5 BANT questions (Budget / Authority / Need / Timeline) and updates lead status.

LLM-first with bilingual fallback question sets if LLM fails.

Status advancement:

BANT ≥ 3 → QUALIFIED
BANT ≥ 2 → DISCOVERY

BookingAgent

File: auto_client_acquisition/agents/booking.py

Priority order:

Calendly — returns scheduling link (self-service)
Google Calendar — creates event directly (if service account configured)
Manual fallback — returns "team will contact you" message

Saudi weekend aware — skips Friday/Saturday when picking default slots.

CRMAgent

File: auto_client_acquisition/agents/crm.py

HubSpot integration:

Upserts contact by email (handles 409 conflicts via search-then-update)
Creates deal with stage mapped from lead status
Associates deal ↔ contact

Uses tenacity retry with exponential backoff.

ProposalAgent

File: auto_client_acquisition/agents/proposal.py

Generates proposals via Claude (Task.PROPOSAL).

Region-aware pricing:

Saudi Arabia → PRICING_SA_* from settings
Other GCC → PRICING_GCC_*
Global → PRICING_GLOBAL_*_USD

Output: Proposal with markdown body + 30-day validity.

OutreachAgent

File: auto_client_acquisition/agents/outreach.py

Bilingual cold opener generation per channel (email, WhatsApp, LinkedIn, SMS).

Email variant includes a bilingual subject line builder.

FollowUpAgent

File: auto_client_acquisition/agents/followup.py

Cadence: [0d, 3d, 7d, 14d] by default.

Attempts 1–2 use canned bilingual scripts.
Attempts 3+ use LLM with prior-history context.
Auto-pauses on terminal statuses (won/lost/disqualified).

AcquisitionPipeline

File: auto_client_acquisition/pipeline.py

Orchestrates the full funnel with per-step error isolation. Failures in any step appear in result.warnings but never abort the pipeline.

Phase 9 — Autonomous Growth

SectorIntelAgent

File: autonomous_growth/agents/sector_intel.py

Curated data for 12 Saudi sectors. Can optionally enrich with LLM research.

Methods:

run(sector, enrich_with_llm=False) — single sector
best_opportunity() — highest (growth × AI readiness)
target_sectors() — top 5

ContentCreatorAgent

File: autonomous_growth/agents/content.py

Generates articles, LinkedIn posts, case studies, newsletters, tweet threads.

Arabic content → GLM (for nuance); English content → Claude.

DistributionAgent

File: autonomous_growth/agents/distribution.py

Plans when to publish across channels using Riyadh-timezone optimal slots.

Optimal times (examples):

LinkedIn: 8am, 12pm, 5pm
Twitter: 9am, 1pm, 7pm
Email: 9am
WhatsApp broadcast: 7pm

EnrichmentAgent

File: autonomous_growth/agents/enrichment.py

Three-layer enrichment:

Email domain → sector hints (e.g., aramco.com → oil_gas)
Phone country code → region
LLM inference from company name (best-effort)

CompetitorMonitorAgent

File: autonomous_growth/agents/competitor.py

Summarizes competitor intel into: positioning, pricing hints, strengths, weaknesses, counter-moves.

MarketResearchAgent

File: autonomous_growth/agents/market_research.py

Uses Gemini (Task.RESEARCH) for source-dense research with three depth levels (quick/standard/deep).

Output includes summary + bullet points + explicit caveats section.

Prompt library

All prompts live in core/prompts/karpathy_prompts.py. Every prompt is Karpathy-style: explicit role, explicit task, explicit output format.

Registered prompts: pain_extraction, icp_reasoning, proposal_generation, content_writer, sector_analysis, qualification_questions, competitor_summary, outreach_opener, followup.

Sales scripts (canned bilingual) in core/prompts/sales_scripts.py: opener, follow_up_1, follow_up_2, demo_confirm, proposal_cover.

Extending with a new agent

# auto_client_acquisition/agents/my_agent.py
from core.agents.base import BaseAgent

class MyAgent(BaseAgent):
    name = "my_agent"

    async def run(self, *, foo: str, **_) -> dict:
        self.log.info("running", foo=foo)
        response = await self.router.run(
            task=Task.REASONING,
            messages=f"Process this: {foo}",
        )
        return {"result": response.content}

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