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 KirKruglov/claude-skills-kit --skill hiring-pipeline-reviewergit clone --depth 1 https://github.com/KirKruglov/claude-skills-kitWrote 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/kirkruglov/claude-skills-kit/hiring-pipeline-reviewer)<a href="https://agentmods.dev/skills/kirkruglov/claude-skills-kit/hiring-pipeline-reviewer"><img src="https://agentmods.dev/badge/skills/kirkruglov/claude-skills-kit/hiring-pipeline-reviewer/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/kirkruglov/claude-skills-kit/hiring-pipeline-reviewer"><img src="https://agentmods.dev/badge/skills/kirkruglov/claude-skills-kit/hiring-pipeline-reviewer.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 | $0.00089 | $0.01741 |
| Opus 5 | $0.00044 | $0.00870 |
| Sonnet 5 | $0.00018 | $0.00348 |
| Haiku 4.5 | $0.00009 | $0.00174 |
Grade A, and why
hiring-pipeline-reviewer 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 12d 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hiring Pipeline Reviewer
Generates a structured weekly status report for all candidates in a hiring pipeline from the hiring manager's interview notes and evaluation sheets. Flags stuck candidates, consolidates scores, recommends next steps, and optionally drafts a recruiter update.
Input:
- Interview notes and/or evaluation sheets (plain text, .md, or .txt; one block or multiple files)
- Optional: position title, hiring criteria, cutoff date for "stuck" flag (default: 5 days)
Output:
- Candidate status table with stage, last action, score, next step, and flags
- Flags section (stuck candidates, missing data)
- Recommendations section (advance / decline / decision needed)
- Optional recruiter update narrative (2–4 sentences)
Language Detection
Detect the user's language from their message:
- If Russian (or contains Cyrillic): respond in Russian
- If English (or other Latin-script language): respond in English
- If ambiguous: respond in the language of the trigger phrase used
Instructions
Step 1: Collect Input
-
Check what the user already provided in their trigger message:
- Interview notes or evaluation text included → extract it
- Position title mentioned → extract it
- Hiring criteria mentioned → extract them
-
If no notes or evaluation data were provided, ask the user to paste them. Do not proceed without input data.
-
Accept any of these input formats:
- Plain text pasted directly in chat
- Single block of notes covering multiple candidates
- Separate text blocks per candidate (labeled or unlabeled)
Step 2: Extract Candidate Records
-
Parse the input to identify individual candidates:
- Look for names, initials, or role labels (e.g., "Candidate 1", "Alex M.", "Senior iOS Dev")
- Use date markers, section separators, or explicit labels as candidate boundaries
-
For each identified candidate, extract:
- Name / identifier (use provided name; if ambiguous, assign [Candidate A], [Candidate B])
- Stage (e.g., Applied, Phone Screen, Interview 1, Interview 2, Offer, Declined)
- Last action date (most recent note or evaluation date; mark as
—if absent) - Score or evaluation summary (numeric if present; narrative summary if not)
- Key strengths (up to 2 phrases)
- Key concerns (up to 2 phrases)
- Stated next step (if mentioned in notes; otherwise
—)
What ships with it
4 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.
- 12d ago First seen · 173 lines · 89 tokens per session scan A 2e5792e87848
hiring-pipeline-reviewer is a skill published in the GitHub repository KirKruglov/claude-skills-kit (18 stars, last pushed 1mo ago), licensed MIT. It adds 89 tokens to every session and 1,741 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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