Borrowing it
Nothing to install: this file belongs to egregore-labs/egregore. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/egregore-labs/egregore/main/.claude/skills/ingest-user-interview/SKILL.mdgit clone --depth 1 https://github.com/egregore-labs/egregoreWrote 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/egregore-labs/egregore/ingest-user-interview)<a href="https://agentmods.dev/skills/egregore-labs/egregore/ingest-user-interview"><img src="https://agentmods.dev/badge/skills/egregore-labs/egregore/ingest-user-interview/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/egregore-labs/egregore/ingest-user-interview"><img src="https://agentmods.dev/badge/skills/egregore-labs/egregore/ingest-user-interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high System Prompt Leakage · line 636 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
- high Anti-Refusal · line 983 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00051 | $0.11161 |
| Opus 5 | $0.00026 | $0.05581 |
| Sonnet 5 | $0.00010 | $0.02232 |
| Haiku 4.5 | $0.00005 | $0.01116 |
Grade A, and why
ingest-user-interview 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 11d 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 — 1,118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze user interview transcripts. Uses a multi-dimensional analysis pipeline with 3 analyst agents + Opus synthesis to extract rich, structured insights from user research sessions.
Arguments: $ARGUMENTS (Optional: search term, file path, or "synthesis" for cross-interview patterns)
Usage
/ingest user-interview— Interactive: choose source (Granola, paste, file)/ingest user-interview <path>— Process a specific transcript file/ingest user-interview <search>— Find interview in Granola by title
When to invoke
Trigger phrases:
- "process the interview", "analyze the user interview", "ingest the interview"
- "onboarding interview", "user research session", "research call"
- "user feedback session", "interview with [name]"
Architecture
Multi-dimensional analysis pipeline with 3 Sonnet analyst agents + Opus synthesis:
Input (transcript) → Pass 0 (Opus, inline): scaffold
Cross-interview context → 4 Neo4j queries → graph context
┌─────────────────────────────────────────┐
JOURNEY (Sonnet) SENTIMENT (Sonnet) PRODUCT (Sonnet)
transcript+scaffold transcript (fresh) transcript+scaffold+quests
friction, aha, emotions, confusion, feature discovery,
task flow, stuck delight, frustration, mental models,
points, drop-off engagement arc unmet needs
└──────────────┬──────────────────────┘
SYNTHESIS (Opus, inline)
→ Interview Analysis Briefing
→ Enriched insight list
Cost & Resource Budget
Target per interview:
- Bash calls: ~6-10 (source fetch, graph batches, file writes, git)
- Task agents: 3 Sonnet (parallel, inline — NOT background)
- AskUserQuestion: 0-2 (source selection, participant info)
- Graph batches: 2-3 (1 context read, 1-2 artifact write batches at <=20 queries each)
- Token-heavy: Journey + Sentiment agents receive full transcript (~6K words each)
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.
- 11d ago First seen · 1,118 lines · 51 tokens per session scan A 7bedab2fb042
ingest-user-interview is a skill published in the GitHub repository egregore-labs/egregore (288 stars, last pushed 7d ago), licensed MIT. It adds 51 tokens to every session and 11,161 once invoked, about $0.0003 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.
Other skills, from other repositories
work
Execute an approved wish plan — orchestrate subagents per task group with fix loops, validation, and review handoff.
sw-do
Implement a SpecWeave increment task by task through the ledger, with evidence per task and a verified close. Use for "implement this", "start working", "continue the increment", "keep going".
done
Close an increment: ledger check, specweave verify, optional review, then specweave complete. Use when all tasks are done and saying "close increment", "we are done", or "finish up".
xiaohongshu-image-creator
An image-making assistant for Xiaohongshu, a Chinese social platform for lifestyle, product, and educational posts. It creates vertical covers and supporting images matched to the post’s topic, audience, and visual style.
atomic-tdd
Test-first discipline. Auto-triggers on "let's implement X", "add feature Y", "fix bug Z", "write a test for", "implement", "build out", and similar pre-code-change phrases. Iron rule: failing test exists before production code. Skip only for pure docs/config changes with an explicit "skipped because:" note. Explicit…
agent-creator
Author a new project-specific Pythinker subagent (a specialist like "migration-reviewer" or "api-contract-checker") with a correct spec, a persona-rich system prompt, and a structured output contract. Use when the user wants to create, scaffold, or design a custom agent / subagent, or asks how Pythinker agent YAML /…