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/daypunk/lockedin/lockedin-render-interviewnpx skills add daypunk/LockedIn --skill lockedin-render-interviewgit clone --depth 1 https://github.com/daypunk/LockedInWrote 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/daypunk/lockedin/lockedin-render-interview)<a href="https://agentmods.dev/skills/daypunk/lockedin/lockedin-render-interview"><img src="https://agentmods.dev/badge/skills/daypunk/lockedin/lockedin-render-interview.svg" alt="Measured on agentmods" 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 | $0.00088 | $0.00724 |
| Opus 5 | $0.00044 | $0.00362 |
| Sonnet 5 | $0.00018 | $0.00145 |
| Haiku 4.5 | $0.00009 | $0.00072 |
Grade A, and why
lockedin-render-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 6d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-interview
Status: v1.2 (calibrated). Research-based calibration complete.
RUBRIC.md ships with five cross-source-validated dimensions. A
banned_phrases.json (25 entries, each backed by 2+ sources) and
research-notes.md (7 cited sources) ship alongside the prompts.
Pass/fail fixture corpus under tests/fixtures/interview/.
Use this when
- The user names an interview question and asks for an answer.
- The user pastes a job description and asks for talking points.
- The user says "STAR" or "behavioural" or "면접" or "기술 면접".
Do NOT use when
- The user wants a full resume →
lockedin-render-resume-en. - The user wants a Korean cover letter →
lockedin-render-jaso. - The vault has no relevant role / project / achievement nodes.
Seed first via
/lockedin initor by ingesting a resume.
Two-turn pattern
Writer turn produces the draft. Reviewer turn re-loads RUBRIC.md
fresh in a separate Claude turn and emits a JSON score. Same as the
other renderers; the split is load-bearing.
Output shape
A single markdown answer, no headers. STAR (Situation / Task /
Action / Result) by default; PAR (Problem / Action / Result) when
the question is incident-shaped. One experience per paragraph with
explicit transitions, mirroring the policy in
lockedin-render-resume-en and lockedin-render-jaso.
The answer pulls evidence from the vault using slug citation
([[type/slug]]); the slugs are resolved to natural language by
lockedin/render/resolve_slugs.py before the artifact is shown to
the user.
Files in this directory
SKILL.md (this file)
research-notes.md 7 cited sources, cross-source analysis summary
banned_phrases.json 25 entries, severity-tagged, each backed by 2+ URLs
prompt-writer.md writer-turn instruction
prompt-reviewer.md reviewer-turn instruction (re-loads RUBRIC.md fresh)
RUBRIC.md 5-dimension scoring contract + score bands
Calibration status
v1.2 calibrated. The rubric dimensions (clarity, evidence_density,
persona_fit, conciseness, tone) are grounded in cross-source public
research from MIT CAPD, The Muse, Indeed, Harvard Business Review,
Yale OCS, The Interview Guys, and Big Interview. The banned_phrases.json
contains 25 entries across four categories (weak_ownership, trait_claim,
rehearsed_non_answer, vague_filler), each backed by 2+ independent
sources. Pass and fail fixture corpus is at
tests/fixtures/interview/{pass,fail}/ (3 pass, 3 fail).
What ships with it
5 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.
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.
- 6d ago First seen · 74 lines · 88 tokens per session scan A 3a643cd7d50b
lockedin-render-interview is a skill published in the GitHub repository daypunk/LockedIn (127 stars, last pushed 3mo ago), licensed MIT. It adds 88 tokens to every session and 724 once invoked, about $0.0004 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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