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 skills add amalik/convoke-agents --skill bmad-advanced-elicitationgit clone --depth 1 https://github.com/amalik/convoke-agentsWrote 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.
[](https://agentmods.dev/skills/amalik/convoke-agents/bmad-advanced-elicitation)<a href="https://agentmods.dev/skills/amalik/convoke-agents/bmad-advanced-elicitation"><img src="https://agentmods.dev/badge/skills/amalik/convoke-agents/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.
<a href="https://agentmods.dev/skills/amalik/convoke-agents/bmad-advanced-elicitation"><img src="https://agentmods.dev/badge/skills/amalik/convoke-agents/bmad-advanced-elicitation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00023 | $0.00038 |
| Opus 5.5 | $0.00009 | $0.00015 |
| Sonnet 5.5 | $0.00005 | $0.00008 |
| Haiku 4.5 | $0.00002 | $0.00004 |
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 1mo 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.
This is a copy
100% identical to bmad-advanced-elicitation — 0 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.
What it actually says
Follow the instructions in ./workflow.md.
What ships with it
3 files 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.
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.
- 1mo ago First seen · 7 lines · 23 tokens per session scan A 9e1096c3b608
bmad-advanced-elicitation is a skill published in the GitHub repository amalik/convoke-agents (67 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 38 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to bmad-advanced-elicitation, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
kermt-pretrain-scratch
Pretrain a fresh KERMT model from scratch on a user-provided corpus. Builds a new vocabulary from the corpus, instantiates the model architecture from defaults, and launches pretrainddp.py inside the kermt container (detached for long runs). Unlike kermt-continue-pretrain, no starting checkpoint is loaded — the model…
cdf-auto-labeling
Use when a user needs to get started with CDF Auto-Labeling, plan a scenario, run or debug a shipped cookbook, author prompts or cookbooks, migrate a pipeline, configure a stage, or pull the prebuilt stage containers from NGC (nvcr.io). Confirm critical inputs (data path, output path, endpoints) and ask when any are…
llm-runtime
Integrate, stream, and orchestrate frontier LLM APIs with robust structured outputs, tool call schemas, prompt caching, and resilient backoff handling. Use when implementing AI agent runtimes, tool-calling loops, streaming SSE endpoints, or optimizing token usage and model latency.
prompt-engineering
Use when one prompt must give the same right answer across reruns, models, and pasted-in hostile input: forcing a fixed schema, picking the few-shot set, ordering the prompt blocks, or the inline cases you run while tuning. NOT the agent loop, tools, or retrieval (that is building-agents), NOT a standing CI eval…
chain-of-thought-prompts
Chain-of-thought and step-by-step reasoning prompts for complex problem solving.
few-shot-example-gen
Few-shot example generation and optimization for improved LLM performance.