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 agentmods add skills/fusengine/agents/elicitationnpx skills add fusengine/agents --skill elicitationgit clone --depth 1 https://github.com/fusengine/agentsWhat 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 | $0.00034 | $0.01499 |
| Opus 5 | $0.00017 | $0.00749 |
| Sonnet 5 | $0.00007 | $0.00300 |
| Haiku 4.5 | $0.00003 | $0.00150 |
Grade A, and why
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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
It sits between Execute and eXamine in the APEX flow, scores itself against an exit threshold (>=90% proceed, 70-89% document gaps, <70% iterate), and persists its findings to .claude/apex/docs/elicit-{task-slug}.json so a later pass can diff against prior verdicts instead of restarting.
Elicitation Skill
Purpose
Enable expert agents to self-review and self-correct their code before external validation (sniper). Based on BMAD-METHOD's 75 elicitation techniques.
3 Execution Modes
Mode 1: MANUAL (default)
Expert presents 5 relevant techniques → User chooses → Expert applies
Mode 2: AUTO (--auto)
Expert auto-detects code type → Auto-selects techniques → Applies silently
Mode 3: SKIP (--skip)
Skip elicitation → Go directly to sniper validation
Quick Start
After Execute phase, expert runs:
# Manual mode (default)
> Apply elicitation skill
# Auto mode (no prompts)
> Apply elicitation skill --auto
# Skip self-review
> Apply elicitation skill --skip
Workflow Overview
┌─────────────────────────────────────────────────────────┐
│ ELICITATION WORKFLOW │
│ │
│ Step 0: Init → Load context │
│ Step 1: Analyze Code → Detect code type │
│ Step 2: Select → Choose techniques (or auto) │
│ Step 3: Apply Review → Execute techniques │
│ Step 4: Self-Correct → Fix own issues │
│ Step 5: Report → Summary before sniper │
└─────────────────────────────────────────────────────────┘
What ships with it
9 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.
- references/artifact-contract.md 1.1 KB
- references/elicit-profile.md 1.5 KB
- references/techniques-catalog.md 8.4 KB
- steps/step-00-init.md 2.2 KB
- steps/step-01-analyze-code.md 2.8 KB
- steps/step-02-select-techniques.md 3.8 KB
- steps/step-03-apply-review.md 4.1 KB
- steps/step-04-self-correct.md 3.5 KB
- steps/step-05-report.md 5.3 KB
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.
- yesterday First seen · 170 lines · 34 tokens per session scan A c815d9059277
elicitation is a skill published in the GitHub repository fusengine/agents (25 stars, last pushed 28d ago), licensed MIT. It adds 34 tokens to every session and 1,499 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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