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 ChipAlexandru/strategy-consultant --skill expert-interviewgit clone --depth 1 https://github.com/ChipAlexandru/strategy-consultantWrote 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/chipalexandru/strategy-consultant/expert-interview)<a href="https://agentmods.dev/skills/chipalexandru/strategy-consultant/expert-interview"><img src="https://agentmods.dev/badge/skills/chipalexandru/strategy-consultant/expert-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/chipalexandru/strategy-consultant/expert-interview"><img src="https://agentmods.dev/badge/skills/chipalexandru/strategy-consultant/expert-interview.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 | $0.00106 | $0.03041 |
| Opus 5 | $0.00053 | $0.01520 |
| Sonnet 5 | $0.00021 | $0.00608 |
| Haiku 4.5 | $0.00011 | $0.00304 |
Grade A, and why
expert-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 9d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Expert Interview — Confirm, Enhance, and Challenge Research Findings
Expert interviews come AFTER the public research phase. Their purpose is to confirm findings, fill information gaps, add precision to estimates, and surface insights that public data cannot provide. Expert interview data carries high weight (CS-2) because it comes from practitioners with direct domain knowledge.
Preflight Gate (run BEFORE any other step)
This phase requires upstream state and artifacts. Before doing anything else, verify ALL of the following:
engagement-state.jsonexists in the active workspace."research"is incompleted_phases.- The following artifacts exist on disk and are non-empty:
research-validated.mdprecision-anchor.md
If ANY required item is missing or empty, STOP. Do not identify gaps, do not draft expert profiles, do not write interview guides. Report the specific missing state field or artifact path and route control back to engagement-manager. Do not reconstruct research findings locally — without research-validated.md, gap identification has no anchor and the guides will be generic.
When the gate passes:
- Read
engagement-state.jsonand treat itsworkspace_pathas the active workspace. - Read
research-validated.mdfor gap identification and CS-3 claims to upgrade. - Read
precision-anchor.mdfor the question that frames every interview guide.
At the end of this phase, append "expert-interview" to completed_phases, update artifact_paths.expert_interview_notes (and any updated research_validated if the validator was re-run), set current_phase to sense-check, refresh next_required_action and last_updated, and write engagement-state.json.
This skill covers three phases: planning who to interview, creating interview guides informed by research findings, and processing interview outputs back into the research flow.
When This Skill Activates
This skill activates in two scenarios:
- Pre-interview (after research, before interviews happen): Plan who to interview and create interview guides
- Post-interview (after interviews are conducted): Process uploaded notes/transcripts and integrate findings
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.
- 9d ago First seen · 237 lines · 106 tokens per session scan A 19c07da90c1c
expert-interview is a skill published in the GitHub repository ChipAlexandru/strategy-consultant (4 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 106 tokens to every session and 3,041 once invoked, about $0.0005 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 2026-08-31.
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