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 | $0.00059 | $0.00628 |
| Opus 5.5 | $0.00024 | $0.00251 |
| Sonnet 5.5 | $0.00012 | $0.00126 |
| Haiku 4.5 | $0.00006 | $0.00063 |
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 24d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ingest user interview
Acquire the transcript, understand the research intent, analyze it with appropriate lenses, preview the result, then persist one approved typed package through Egregore Runtime.
Read references/analysis-contract.md before analysis or package construction.
When to invoke
Analyze a user interview from Granola, pasted text, or a file into an evidence-backed briefing, journey insights, product findings, patterns, and actions. Use for /ingest user-interview, onboarding interviews, research calls, or requests to process user feedback.
Use the meeting skill for a general team meeting.
Source
Accept one of:
- Granola connector: search/select, then fetch transcript and stable metadata;
- pasted transcript;
- a user-authorized
.txt,.md, or.jsonfile.
Infer participant, researcher, interview type, date, and source revision when reliable; otherwise ask compact questions. Do not put connector credentials or the raw transcript into canonical memory merely to analyze it.
Context and analysis
Reuse sufficient EGREGORE_ORG_CONTEXT_V1. Open selected canonical evidence
through Runtime when verification is necessary. If cross-interview comparison
is requested and context is insufficient, make at most one Egregore retrieval
request. Never query graph/QMD directly; canonical Markdown/Git wins.
Start with a compact scaffold. Analyze inline for short interviews. For long or complex material, parallel Journey, Sentiment, and Product lenses may run once, then synthesize their evidence and disagreements. Do not force multi-agent work when it adds latency without analytical value.
Show the emotional arc, evidence-backed product reading, proposed durable
insights, confidence, priority, tensions, and researcher actions. Ask Save,
Edit, or Skip. Do not write before Save.
Canonical writeback
Build one temporary egregore-research-ingest/v1 package under tmp/ containing the
approved interview briefing, participant journey document where needed, and
typed findings/patterns/decisions. Then make exactly one deterministic call, using
{package-json} for the package file just written:
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.
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.
- 24d ago Changed · -1,049 lines · +8 tokens per session 63c1c01b3a96
- 1mo 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 (293 stars, last pushed 9d ago), licensed MIT. It adds 59 tokens to every session and 628 once invoked, about $0.0002 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 2026-08-30.
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