Skill

Skill agasi-gif/negotiation-trainer/skill

Construction industry negotiation sparring trainer with AI opponent and coach debrief. Use when the user wants to practise negotiations, run a scenario (supplier, client, GC contract, hiring, equity split), get a tactical hint, or receive a scored breakdown of their negotiation technique.From its SKILL.md

Install

npx -y skills add agasi-gif/negotiation-trainer --skill skill

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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SKILL.md

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Negotiation Trainer

AI-powered negotiation sparring trainer focused on construction industry scenarios. The opponent plays tough and realistically; the coach provides hints, mid-session analysis, and a final debrief with scores across 6 criteria (anchoring, BATNA, concessions, interests, resilience, outcome).

Built on the Anthropic Claude SDK. Skill format follows the open agentskills standard — works in Hermes Agent (~/.hermes/skills/negotiation-trainer/) and Claude Code / Claude Projects without modification.

When to use

"practise negotiations with me", "play the supplier / client / GC"
"analyse my negotiation approach"
Pre-meeting prep: user describes the real situation → custom scenario generated on the fly

Configuration

Trainer code directory: NEGOTIATION_TRAINER_DIR (default /opt/negotiation-trainer). Required: ANTHROPIC_API_KEY. Session state in SQLite (DB_PATH) — persists between CLI calls.

Session workflow

List scenarios or start immediately if the user describes their situation:

cd $NEGOTIATION_TRAINER_DIR
python cli.py --json list
python cli.py --json new --scenario supplier --difficulty hard
# or custom:
python cli.py --json new --custom "Negotiating a retention clause with a general contractor"

The new response includes context (show to the user as their briefing) and opponent_opening (deliver to the user verbatim as the opponent's first line).

Pass every user reply to the opponent and return the response:

python cli.py --json say --text "<user's exact words>"

On "hint" / "how should I respond":

python cli.py --json hint

On "coach me" / "how am I doing":

python cli.py --json coach

When the user finishes ("that's enough", "score me", "let's wrap up"):

python cli.py --json debrief

Returns JSON with scores (6 criteria, 1–10), result_summary, best_move, worst_move, advice — format these clearly for the user.

Voice mode

In Hermes, user speech arrives already transcribed (platform-native STT) — pass it to say as plain text. Synthesise the opponent's reply using Hermes native TTS. The standalone Telegram bot (main.py) has its own full voice pipeline (Whisper STT + OpenAI TTS) and does not need to be running when using this skill.

Rules

NEVER mix roles: say responses go to the user as the opponent's voice; hint/coach responses go as the coach's voice — label them clearly.
Do NOT reveal the persona field or the opponent's hidden concession limits until after debrief.
One active session per user: a new new call automatically closes the previous session.
On {"error": "no_active_session"} — prompt the user to choose a scenario.

Constraints

The CLI does not handle STT/TTS — that is the platform's responsibility (Hermes or the Telegram bot).
debrief closes the session; the same dialogue cannot be continued afterwards.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.