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 NOMARJ/sigil --skill empathy-enginegit clone --depth 1 https://github.com/NOMARJ/sigilWrote 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/nomarj/sigil/empathy-engine)<a href="https://agentmods.dev/skills/nomarj/sigil/empathy-engine"><img src="https://agentmods.dev/badge/skills/nomarj/sigil/empathy-engine/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/nomarj/sigil/empathy-engine"><img src="https://agentmods.dev/badge/skills/nomarj/sigil/empathy-engine.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 | $0.00114 | $0.01587 |
| Opus 5.5 | $0.00046 | $0.00635 |
| Sonnet 5.5 | $0.00023 | $0.00317 |
| Haiku 4.5 | $0.00011 | $0.00159 |
Grade A, and why
empathy-engine 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.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Empathy Engine — Evidence-Grounded Persona Panels
This is preparation for research, not research itself. Synthetic evidence has a confidence ceiling of MED. HIGH confidence requires at least one real-world data point.
Purpose
Construct a panel of synthetic personas grounded in real human experiences shared online, then conduct structured interviews to extract insight statements. Produces the evidence root for the NOMARK traceability chain: Person → Insight → Epic → Feature → Story.
Based on IDEO Field Guide to Human-Centered Design (57 methods). Extended with dual-consumer lens (human + AI agent) and structural anti-bias mechanisms (Hostile Critic, Silence Audit).
The Protocol
Step 1: Define the Research Frame
Read SOLUTION.md. Extract or construct:
RESEARCH FRAME
Challenge: [HMW statement or "What This Is" from SOLUTION.md]
Domain: [industry/sector]
Geography: [target markets]
Consumer type: [B2C end user / B2B buyer / operator / etc.]
Extremes to include: [power users, non-users, workaround inventors, reluctant adopters]
If SOLUTION.md "Who It Serves" already has entries, use them as starting hypotheses to investigate — not as conclusions.
Step 2: Harvest Evidence
Search for real human voices. Every piece retains its source URL.
| Source Type | What It Reveals | Search Approach |
|---|---|---|
| Product reviews | Pain points, delight, unmet needs | Search [product/category] + "review" on G2, Capterra, Trustpilot, Reddit |
| Forum discussions | Workarounds, tribal knowledge, emotion | Search [problem space] on Reddit, Stack Exchange, niche communities |
| Support threads | Failure modes, confusion, feature gaps | Search [product] + "issue" or "problem" or "help" |
| Social media | Sentiment, complaints, aspirational use | Search [product/category] on Twitter/X, LinkedIn |
| Published interviews | Expert perspectives, named experiences | Search [domain] + "interview" or "podcast" |
| Job postings | Workflow context, tool expectations | Search roles that use this type of product |
| Academic/industry research | Statistics, demographics, validated findings | Search [domain] + "research" or "report" or "study" |
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 · 144 lines · 114 tokens per session scan A 21bf95176c11
empathy-engine is a skill published in the GitHub repository NOMARJ/sigil (6 stars, last pushed 11d ago), licensed Apache-2.0. It adds 114 tokens to every session and 1,587 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-09-03.
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