bmad-advanced-elicitation

bmad-advanced-elicitation is a skill for Claude Code, Codex from pdurlej/jan-subagent. It costs 56 tokens per session (1,274 once invoked), scanned A, a copy of bmad-advanced-elicitation, MIT.

A guided critique method that makes an AI reconsider, refine, and improve its recent answer using approaches such as Socratic questioning, first-principles analysis, pre-mortems, and red-team review.

In plain words
What is it for?
Use it when you want a deeper critique or want to improve a specific piece of generated content through iterative review.
Why use it?
It helps expose weak reasoning, risks, and missed alternatives in an answer before it is accepted.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it when you want a deeper critique or want to improve a specific piece of generated content through iterative review.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/pdurlej/jan-subagent/bmad-advanced-elicitation
Install

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.

Any agent
npx skills add pdurlej/jan-subagent --skill bmad-advanced-elicitation
Clone the repo
git clone --depth 1 https://github.com/pdurlej/jan-subagent

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for bmad-advanced-elicitation

README.md
[![agentmods](https://agentmods.dev/badge/skills/pdurlej/jan-subagent/bmad-advanced-elicitation/github.svg)](https://agentmods.dev/skills/pdurlej/jan-subagent/bmad-advanced-elicitation)
Your own site
<a href="https://agentmods.dev/skills/pdurlej/jan-subagent/bmad-advanced-elicitation"><img src="https://agentmods.dev/badge/skills/pdurlej/jan-subagent/bmad-advanced-elicitation/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for bmad-advanced-elicitation

Your own site · 80×15
<a href="https://agentmods.dev/skills/pdurlej/jan-subagent/bmad-advanced-elicitation"><img src="https://agentmods.dev/badge/skills/pdurlej/jan-subagent/bmad-advanced-elicitation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,274 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 89% copy Near-identical to another mod in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00056 $0.01274
Opus 5 $0.00028 $0.00637
Sonnet 5 $0.00011 $0.00255
Haiku 4.5 $0.00006 $0.00127

Measured 10d ago against content hash 2d1011b1c93a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

bmad-advanced-elicitation 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 10d ago.

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.

Origin

This is a copy

89% identical to bmad-advanced-elicitation — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

_bmad/core/bmad-advanced-elicitation/SKILL.md · 138 lines

How it starts

The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Advanced Elicitation

Goal: Push the LLM to reconsider, refine, and improve its recent output.


CRITICAL LLM INSTRUCTIONS

  • MANDATORY: Execute ALL steps in the flow section IN EXACT ORDER
  • DO NOT skip steps or change the sequence
  • HALT immediately when halt-conditions are met
  • Each action within a step is a REQUIRED action to complete that step
  • Sections outside flow (validation, output, critical-context) provide essential context - review and apply throughout execution
  • YOU MUST ALWAYS SPEAK OUTPUT in your Agent communication style with the communication_language

INTEGRATION (When Invoked Indirectly)

When invoked from another prompt or process:

  1. Receive or review the current section content that was just generated
  2. Apply elicitation methods iteratively to enhance that specific content
  3. Return the enhanced version back when user selects 'x' to proceed and return back
  4. The enhanced content replaces the original section content in the output document

FLOW

Step 1: Method Registry Loading

Action: Load and read ./methods.csv and {agent_party}

CSV Structure
  • category: Method grouping (core, structural, risk, etc.)
  • method_name: Display name for the method
  • description: Rich explanation of what the method does, when to use it, and why it's valuable
  • output_pattern: Flexible flow guide using arrows (e.g., "analysis -> insights -> action")
Context Analysis
  • Use conversation history
  • Analyze: content type, complexity, stakeholder needs, risk level, and creative potential
Smart Selection
  1. Analyze context: Content type, complexity, stakeholder needs, risk level, creative potential
  2. Parse descriptions: Understand each method's purpose from the rich descriptions in CSV
  3. Select 5 methods: Choose methods that best match the context based on their descriptions
  4. Balance approach: Include mix of foundational and specialized techniques as appropriate

Step 2: Present Options and Handle Responses

Read the full file on GitHub · 138 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 10d ago First seen · 138 lines · 56 tokens per session scan A 2d1011b1c93a

Subscribe to this mod's changes

bmad-advanced-elicitation is a skill published in the GitHub repository pdurlej/jan-subagent (0 stars, last pushed 5mo ago), licensed MIT. It adds 56 tokens to every session and 1,274 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to bmad-advanced-elicitation, differing in 9 lines, and is treated as a copy.

Related

Other skills, from other repositories

nemo-curator

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or…

davila7/claude-code-templates · 83 tokens

training-llms-megatron

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA…

davila7/claude-code-templates · 82 tokens

nemo-evaluator-sdk

Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.

davila7/claude-code-templates · 76 tokens

cudaq-guide

CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.

NVIDIA/skills · 25 tokens

nemo-guardrails

NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.

davila7/claude-code-templates · 61 tokens

verify

Build, launch, drive, and screenshot the OpenNOW Electron settings UI on Windows.

OpenCloudGaming/OpenNOW · 17 tokens