Telegram lead-qualification skill with amoCRM
Universal AI Sales Agent
Three skills and a reference Telegram lead-qualification agent: free-form dialog, 0-100 scoring, RAG over a client base, amoCRM write-back
Medium risk
We rate an entry medium when the tool runs code, makes network calls or reads project files. Check what exactly it does before installing.
Why this level
- Writes lead cards to amoCRM and assigns a pipeline stage, affecting a real sales pipeline
- Stores and processes lead contact data through Telegram and the client's knowledge base
Install
In your terminal, with SkillFoxx CLI
npx skillfoxx add skills/universal-ai-sales-agentDetects the agents on your machine, checks the risk and pins the version.
Other ways to install
Assembled automatically, review before installing.
Run in a terminal in the project folder
npx skills add foggearthquake/universal-ai-agent-sells --skill lead-qualification-agent -a claude-code -yThe skills tool installs the current version from the repository. Add the -g flag to use the skill in every project.
Without third-party tools, from commit 8500918
tmp=$(mktemp -d)
git clone --filter=blob:none --no-checkout https://github.com/foggearthquake/universal-ai-agent-sells.git "$tmp"
git -C "$tmp" sparse-checkout set --no-cone /skills/lead-qualification-agent/
git -C "$tmp" checkout 85009187190248f4a3b5fe4ce0ff0f1cdbfa8943
mkdir -p .claude/skills
cp -R "$tmp/skills/lead-qualification-agent" .claude/skills/lead-qualification-agentCommands for macOS and Linux, on Windows run them in Git Bash.
Run in a terminal in the project folder
npx skills add foggearthquake/universal-ai-agent-sells --skill lead-qualification-agent -a cursor -yThe skills tool installs the current version from the repository. Add the -g flag to use the skill in every project.
Without third-party tools, from commit 8500918
tmp=$(mktemp -d)
git clone --filter=blob:none --no-checkout https://github.com/foggearthquake/universal-ai-agent-sells.git "$tmp"
git -C "$tmp" sparse-checkout set --no-cone /skills/lead-qualification-agent/
git -C "$tmp" checkout 85009187190248f4a3b5fe4ce0ff0f1cdbfa8943
mkdir -p .agents/skills
cp -R "$tmp/skills/lead-qualification-agent" .agents/skills/lead-qualification-agentCommands for macOS and Linux, on Windows run them in Git Bash.
Run in a terminal in the project folder
npx skills add foggearthquake/universal-ai-agent-sells --skill lead-qualification-agent -a github-copilot -yThe skills tool installs the current version from the repository. Add the -g flag to use the skill in every project.
Without third-party tools, from commit 8500918
tmp=$(mktemp -d)
git clone --filter=blob:none --no-checkout https://github.com/foggearthquake/universal-ai-agent-sells.git "$tmp"
git -C "$tmp" sparse-checkout set --no-cone /skills/lead-qualification-agent/
git -C "$tmp" checkout 85009187190248f4a3b5fe4ce0ff0f1cdbfa8943
mkdir -p .github/skills
cp -R "$tmp/skills/lead-qualification-agent" .github/skills/lead-qualification-agentCommands for macOS and Linux, on Windows run them in Git Bash.
Run in a terminal in the project folder
npx skills add foggearthquake/universal-ai-agent-sells --skill lead-qualification-agent -a codex -yThe skills tool installs the current version from the repository. Add the -g flag to use the skill in every project.
Without third-party tools, from commit 8500918
tmp=$(mktemp -d)
git clone --filter=blob:none --no-checkout https://github.com/foggearthquake/universal-ai-agent-sells.git "$tmp"
git -C "$tmp" sparse-checkout set --no-cone /skills/lead-qualification-agent/
git -C "$tmp" checkout 85009187190248f4a3b5fe4ce0ff0f1cdbfa8943
mkdir -p .agents/skills
cp -R "$tmp/skills/lead-qualification-agent" .agents/skills/lead-qualification-agentCommands for macOS and Linux, on Windows run them in Git Bash.
Run in a terminal in the project folder
npx skills add foggearthquake/universal-ai-agent-sells --skill lead-qualification-agent -a gemini-cli -yThe skills tool installs the current version from the repository. Add the -g flag to use the skill in every project.
Without third-party tools, from commit 8500918
tmp=$(mktemp -d)
git clone --filter=blob:none --no-checkout https://github.com/foggearthquake/universal-ai-agent-sells.git "$tmp"
git -C "$tmp" sparse-checkout set --no-cone /skills/lead-qualification-agent/
git -C "$tmp" checkout 85009187190248f4a3b5fe4ce0ff0f1cdbfa8943
mkdir -p .agents/skills
cp -R "$tmp/skills/lead-qualification-agent" .agents/skills/lead-qualification-agentCommands for macOS and Linux, on Windows run them in Git Bash.
Run in a terminal in the project folder
tmp=$(mktemp -d)
git clone --filter=blob:none --no-checkout https://github.com/foggearthquake/universal-ai-agent-sells.git "$tmp"
git -C "$tmp" sparse-checkout set --no-cone /skills/lead-qualification-agent/
git -C "$tmp" checkout 85009187190248f4a3b5fe4ce0ff0f1cdbfa8943
mkdir -p .devin/skills
cp -R "$tmp/skills/lead-qualification-agent" .devin/skills/lead-qualification-agentCommands for macOS and Linux, on Windows run them in Git Bash.
Formerly Windsurf.
Run in a terminal in the project folder
npx skills add foggearthquake/universal-ai-agent-sells --skill lead-qualification-agent -a cline -yThe skills tool installs the current version from the repository. Add the -g flag to use the skill in every project.
Without third-party tools, from commit 8500918
tmp=$(mktemp -d)
git clone --filter=blob:none --no-checkout https://github.com/foggearthquake/universal-ai-agent-sells.git "$tmp"
git -C "$tmp" sparse-checkout set --no-cone /skills/lead-qualification-agent/
git -C "$tmp" checkout 85009187190248f4a3b5fe4ce0ff0f1cdbfa8943
mkdir -p .cline/skills
cp -R "$tmp/skills/lead-qualification-agent" .cline/skills/lead-qualification-agentCommands for macOS and Linux, on Windows run them in Git Bash.
Run in a terminal in the project folder
npx skills add foggearthquake/universal-ai-agent-sells --skill lead-qualification-agent -a roo -yThe skills tool installs the current version from the repository. Add the -g flag to use the skill in every project.
Without third-party tools, from commit 8500918
tmp=$(mktemp -d)
git clone --filter=blob:none --no-checkout https://github.com/foggearthquake/universal-ai-agent-sells.git "$tmp"
git -C "$tmp" sparse-checkout set --no-cone /skills/lead-qualification-agent/
git -C "$tmp" checkout 85009187190248f4a3b5fe4ce0ff0f1cdbfa8943
mkdir -p .roo/skills
cp -R "$tmp/skills/lead-qualification-agent" .roo/skills/lead-qualification-agentCommands for macOS and Linux, on Windows run them in Git Bash.
A fork of Roo Code, same .roo folders.
Run in a terminal in the project folder
npx skills add foggearthquake/universal-ai-agent-sells --skill lead-qualification-agent -a opencode -yThe skills tool installs the current version from the repository. Add the -g flag to use the skill in every project.
Without third-party tools, from commit 8500918
tmp=$(mktemp -d)
git clone --filter=blob:none --no-checkout https://github.com/foggearthquake/universal-ai-agent-sells.git "$tmp"
git -C "$tmp" sparse-checkout set --no-cone /skills/lead-qualification-agent/
git -C "$tmp" checkout 85009187190248f4a3b5fe4ce0ff0f1cdbfa8943
mkdir -p .agents/skills
cp -R "$tmp/skills/lead-qualification-agent" .agents/skills/lead-qualification-agentCommands for macOS and Linux, on Windows run them in Git Bash.
Run in a terminal in the project folder
tmp=$(mktemp -d)
git clone --filter=blob:none --no-checkout https://github.com/foggearthquake/universal-ai-agent-sells.git "$tmp"
git -C "$tmp" sparse-checkout set --no-cone /skills/lead-qualification-agent/
git -C "$tmp" checkout 85009187190248f4a3b5fe4ce0ff0f1cdbfa8943
mkdir -p .agents/skills
cp -R "$tmp/skills/lead-qualification-agent" .agents/skills/lead-qualification-agentCommands for macOS and Linux, on Windows run them in Git Bash.
Run in a terminal in the project folder
tmp=$(mktemp -d)
git clone --filter=blob:none --no-checkout https://github.com/foggearthquake/universal-ai-agent-sells.git "$tmp"
git -C "$tmp" sparse-checkout set --no-cone /skills/lead-qualification-agent/
git -C "$tmp" checkout 85009187190248f4a3b5fe4ce0ff0f1cdbfa8943
mkdir -p .agents/skills
cp -R "$tmp/skills/lead-qualification-agent" .agents/skills/lead-qualification-agentCommands for macOS and Linux, on Windows run them in Git Bash.
Clone foggearthquake/universal-ai-agent-sells, set BOT_TOKEN, OPENAI_API_KEY, database settings and amoCRM tokens in .env, start PostgreSQL with docker compose up -d postgres, apply schema.sql, fill execution/knowledge with your own files, and run python -m bot.
Other ways from the author
cp .env.example .env && docker compose up -d postgres && psql -U user -d db -f schema.sql && python -m botFull local run of the reference implementation after filling in .env.
This is third-party code. Review the repository files before installing.
What it does
The repository provides three reusable skills for building lead-qualification agents: business-faq-rag-agent (answers drawn only from the client's knowledge base, never invented), handoff-crm-telegram-agent (contracts for handing a conversation to a human and to a CRM), and lead-qualification-agent (extracting fields from free-form dialog, 0-100 scoring with client-specific weights, warmth classification). Alongside them sits a full Python reference implementation: a Telegram bot on Aiogram 3 runs a button-free dialog, PostgreSQL with pgvector stores the client's knowledge base, hard escalation triggers hand off to a human on a price objection or complaint, and the qualification result is written to an amoCRM lead card with auto-filled fields and pipeline stage assignment. Per-client customization happens only through config files: qualification_rules.py, risk_triggers.py, tone.py and a knowledge-base folder, with no code changes.
Who it is for. For agencies and developers building Telegram sales agents with amoCRM who want ready qualification skills instead of building from scratch.
Good fit when
- You need a Telegram bot that qualifies inbound leads in free-form dialog with no buttons or menus
- You need 0-100 lead scoring with configurable weights and thresholds, no code changes required
- You need the qualification result written to amoCRM with a pipeline stage assigned
Not a fit when
- You need a platform other than Telegram: the dialog layer is built specifically on Aiogram 3
- You need a CRM other than amoCRM: the integration is built for its REST API specifically
- The author describes the project as a paid client solution: there is no free hosted service, you deploy it yourself
Example request
I want to know the cost and timeline for implementing your system for a 2000-item warehouseLimitations
The README explicitly calls the project a production solution for a paid client deployment, not a hosted open service. It needs your own PostgreSQL with pgvector, a Telegram bot token, an OpenAI-compatible API key, and amoCRM tokens. The skills are built for use inside this specific DOE architecture (Directives, Orchestration, Execution); porting to another stack would need adaptation.
How to disable. Stop the python -m bot process and remove the Telegram bot's webhook or token, and revoke the amoCRM tokens.
Security check
- Writes lead cards to amoCRM and assigns a pipeline stage, affecting a real sales pipeline
- Stores and processes lead contact data through Telegram and the client's knowledge base
README in short
The English README describes the architecture from Telegram through an FSM dialog manager to a RAG retriever, a qualification scorer, and a decision router to amoCRM and an escalation queue, a scoring table across three lead-warmth tiers, five engineering decisions like avoiding button-based flows and hard escalation triggers, the Python 3.13 and Aiogram 3 tech stack, a quick start via docker compose, and a table of config files for per-client customization with no code changes.
SKILL.md
--- name: lead-qualification-agent description: Qualify inbound leads in chat by collecting intent, timeline, budget, and contact; compute 0-100 score with client-specific weights; classify warmth and qualification; and produce concise qualification reasoning. Use when building or operating conversational lead qualification flows. --- Read references/qualification-flow.md before implementation. Load client profile from a JSON config with fields, weights, thresholds, and risk triggers. Execute this sequence: 1. Extract structured fields from free-form user messages. 2. Ask one missing high-impact question per turn. 3. Compute score and warmth. 4. Set qualified or not_qualified. 5. Output compact reasoning tied to observed data. When uncertainty is high, ask one clarification instead of guessing.
FAQ
Can the bot answer outside the knowledge base?
No, by design the agent answers only via RAG over the loaded knowledge base, and if nothing is found it says so honestly and offers a human handoff.
How do I configure the agent for a new client without code changes?
Through config/qualification_rules.py, config/risk_triggers.py, config/tone.py and your own knowledge base in execution/knowledge, no code changes needed.
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