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 niharya/skills-drawer --skill interview-notesgit clone --depth 1 https://github.com/niharya/skills-drawerWrote 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/niharya/skills-drawer/interview-notes)<a href="https://agentmods.dev/skills/niharya/skills-drawer/interview-notes"><img src="https://agentmods.dev/badge/skills/niharya/skills-drawer/interview-notes/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/niharya/skills-drawer/interview-notes"><img src="https://agentmods.dev/badge/skills/niharya/skills-drawer/interview-notes.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 | $0.00139 | $0.02612 |
| Opus 5.5 | $0.00056 | $0.01045 |
| Sonnet 5.5 | $0.00028 | $0.00522 |
| Haiku 4.5 | $0.00014 | $0.00261 |
Grade A, and why
interview-notes scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview Notes
Read one interview the way a careful analyst would, before the interviewer listens to it themselves. Do the parsing, organising and pattern-finding that AI does well; never put words in anyone's mouth. First the transcript is cleaned into a copy people can read, with every change logged. Then the user gets two layers: the read in chat (one screen: what matters most, what to check) and the notes in a doc (the full depth, every quote checked). Then anything they ask afterwards.
Find this skill's folder from the "Base directory for this skill" line shown when it loaded; call it SKILL_DIR. Every check runs through python3 -I "$SKILL_DIR/scripts/ia.py" <command> …, from a fresh working folder (mkdir -p ~/interview-notes && cd ~/interview-notes). Never run the scripts from inside the upload folder. reference/commands.md lists every command.
Creed
Stories beat statements. One correction beats ten agreements. Their words beat ours. One true finding beats five safe ones. Look before you confirm. Silence only counts if you asked. Say what you can't see.
Rules that never bend
- The checks govern claims, never noticing. Anything striking may be reported, labelled as noticed. Every claim, quote, count and number passes the scripts before the user sees it.
- The interviewer's words are never the person's evidence. Quote marks hold only the interviewee's words; the interviewer's words go in italics. Every quote carries its turn,
(T042). A "yes" to a cut-off question is a backchannel, not agreement. - Code does the checking, never memory: parsing, quotes, turn references, counts, numbers, chapter coverage. Never count by hand. Never add up counts in your head; ask the script.
- Their words stay theirs. Quote exactly as the transcript has it, hesitations and all (
uh,-, repeats), or exactly as the clean copy has it (its only differences are logged corrections and removed fillers). Keep the script they were transcribed in: Hindi in Devanagari stays Devanagari; English that the transcriber wrote in Devanagari goes back into English, word for word, and the checks confirm it sounds the same. Never translate silently; gloss in square brackets if needed. Each quote appears once in the notes. - Say what you can't tell. Every read and every set of notes ends with what the interview cannot show. Flags travel with the quotes they touch.
- Opinions about the evidence, never about the person. Find how they work, what they say, choose and reject, and how they answer. Never claim what they fear, need, secretly mean or "really are". No diagnosis, no adjectives about character.
- Nothing is deleted from the base or the clean copy. The clean copy keeps every stretch, money and small talk included; Stage 1 keeps everything; the notes rank it, the read selects. Private material informs the read and never reaches anything shareable.
- Transcript only. If given audio or video, say plainly that this skill works from a transcript and suggest transcribing it first (with speaker labels on). Do not transcribe. A second transcript of the same recording (the video's own, a phone app's) is a dictionary for hard words:
ref add, never a part, never quoted. - Ask, then wait; check, then say done. When you ask the user something, wait for the answer before building on it. Before calling any stage done, run its check and read the result once yourself. Things for the user to check come as a short list.
What ships with it
12 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.
- reference/ask-mode.md 2.3 KB
- reference/calibration.md 1.4 KB
- reference/clean.md 6.4 KB
- reference/commands.md 6.4 KB
- reference/filing.md 2.6 KB
- reference/privacy.md 2.1 KB
- reference/stage1.md 3.2 KB
- reference/stage2.md 8.7 KB
- reference/transcripts.md 4.2 KB
- reference/voice.md 1.9 KB
- scripts/hinglish.py 5.9 KB runs code
- scripts/ia.py 91 KB runs code
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.
- 3d ago First seen · 111 lines · 139 tokens per session scan A 03ea4bdb62c7
interview-notes is a skill published in the GitHub repository niharya/skills-drawer (2 stars, last pushed 4d ago), licensed MIT. It adds 139 tokens to every session and 2,612 once invoked, about $0.0006 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 2026-11-08.
interview-notes compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| interview-notesniharya/skills-drawer | Skill | 2 | 4d ago | MIT | A | 139 | Claude Code |
| layout-disciplinecrazyzlnofficial/layout-discipline | Skill | 4 | 4mo ago | MIT | A | 105 | Claude Code, Codex |
| huedominikmartn/hue | Skill | 900 | yesterday | MIT | A | 84 | Claude Code, Codex |
| social-postHao0321/claude-skill-social-post | Skill | 845 | 27d ago | MIT | A | 119 | Codex |
| comfyuiSlavaSexton/ComfyUI-Agent-Kit | Skill | 120 | 8d ago | Apache-2.0 | A | 107 | Claude Code |
| socialpostHao0321/claude-skill-social-post | Skill | 845 | 27d ago | MIT | A | 92 | Codex |
| minimax-h3SlavaSexton/ComfyUI-Agent-Kit | Skill | 120 | 8d ago | Apache-2.0 | A | 102 | Claude Code |