# Hiring Signal Tracker > Track public tech companies' AI investment through their official job-posting APIs (Greenhouse/Lever/Ashby) and turn hiring data into investment research signals. Use this skill whenever the user wants to collect or update hiring data, run the hiring signal report, add/remove/replace companies in the watchlist (股票池), analyze a company's AI hiring momentum, set up recurring collection, reconcile hiring signals with earnings data, or asks about 招聘信号 / AI 投入跟踪 / 招聘数据分析 — even if they don't name the skill. Also use it when a financial analyst asks how a tech company's expansion or AI strategy shows up in its hiring. - Skill: `sihanyanliu-sys/hiring-signal-tracker` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add sihanyanliu-sys/hiring-signal-tracker` - Raw SKILL.md: https://api.skillmd.com/api/skills/sihanyanliu-sys/hiring-signal-tracker/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: sihanyanliu-sys (https://skillmd.com/u/sihanyanliu-sys) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/sihanyanliu-sys/hiring-signal-tracker --- # Hiring Signal Tracker Collects job postings from listed tech companies' official ATS APIs, scores every job description against a tiered AI-skill dictionary, and accumulates a per-company time series of AI-investment signals for fundamental (value-investing) research. The output is research input, never a buy/sell recommendation. ## Directory layout ``` hiring-signal-tracker/ ├── SKILL.md this file ├── README.md user-facing docs (Chinese) ├── config.json watchlist + dictionaries + classification rules ├── scripts/ │ ├── pipeline.py collect → score → time series → HTML report (stdlib only) │ └── probe.py detect which ATS a new company uses ├── references/ │ └── methodology.md investment logic, metric definitions, caveats — READ THIS │ before interpreting any numbers for the user ├── data/ accumulating time series (jobs_*.csv, history.csv, state.json) └── reports/ generated HTML reports ``` ## Core operations **Run a collection** (the most common request — "跑一次采集 / 更新报告"): ```bash cd && python3 scripts/pipeline.py ``` Python 3.8+ standard library only; no pip installs. Takes 1–3 minutes for ~27 companies. Same-day reruns overwrite that day's snapshot safely. After running, open `reports/report_.html` for the user and summarize the notable changes (see "Interpreting results" below). **Add or replace companies** ("把 X 加进股票池"): 1. Detect the company's ATS: `python3 scripts/probe.py ...` Try the company's short name, product name, and name without spaces (e.g. for HubSpot the working token was `hubspotjobs`, not `hubspot`). 2. If found, add an entry to `companies` in config.json with name, ticker, ats, token, and a theme (used for peer grouping in reports — keep peers together). 3. If all probes miss, the company likely uses Workday or a custom system — tell the user it cannot be tracked in v1 rather than guessing. 4. Run a collection to establish the company's baseline. Companies known to be unreachable: Snowflake, CrowdStrike, Zscaler, Atlassian, UiPath (Workday/custom). Big-tech (Google/Meta/Microsoft/Amazon) is deliberately excluded: thousands of postings dilute the signal. **Set up recurring collection** ("每周自动采集"): prefer the host's scheduling feature if present. Otherwise offer a cron entry (weekly, Monday morning): ``` 0 9 * * 1 cd && /usr/bin/python3 scripts/pipeline.py ``` Report delivery by email requires the user's own connected mail tool and their explicit approval each time — never wire credentials into scripts. **Edit the skill dictionary** (config.json `skill_categories`): after any dictionary change, warn the user that density scores are no longer comparable with earlier snapshots; offer to archive `data/history.csv` into `data/archive/` and restart the baseline. ## Interpreting results Read `references/methodology.md` before writing analysis for the user. The non-negotiable rules it explains: - **Within-company time series beats cross-company levels.** JD writing styles differ by company and ATS; a company's change against its own baseline is the signal. Cross-company comparison is only meaningful within the same `theme`. - **Signal quality ranks by role type**: demand-validated roles (FDE, solutions, sales engineering — staffed against real contracts) > product engineering > research. Non-tech AI penetration (AI skills required in sales/finance/ops roles) marks AI moving from lab to business infrastructure. - **Negative signals are more reliable than positive ones.** Companies fake expansion, never contraction. Lead with red flags (batch job removals) when present. - **Boilerplate suppression**: a term hitting ≥90% of one company's JDs is marketing copy, not skill demand — the pipeline removes it automatically and lists it in the `boilerplate_suppressed` column. Mention it if a company's score shifted because of it. - Hiring is necessary but not sufficient evidence of value creation: a cash-burning expander and a profitable compounder post the same jobs. Always push toward reconciliation (below) before drawing conclusions. ## Advanced analysis (agent-run, not scripted) These are part of the research method; run them with your own web/search tools when the user asks for deeper work. **B1 — Earnings reconciliation.** For companies whose signals moved, pull the latest 10-Q/10-K from SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) and check whether revenue growth, RPO / deferred revenue, and headcount direction confirm or contradict the hiring signal. A rising demand-validated share with accelerating RPO is confirmation; with stagnant RPO it may be firefighting, not expansion. **B2 — Say-do gap.** Compare management's AI narrative in the latest earnings call with the hiring data. Specific claims (customer counts, revenue contribution) + matching hires = credible. Vision language + no AI hiring = flag the inconsistency to the user; that gap is itself a research finding. **C — Composite momentum (needs ≥3 snapshots).** Rank companies within theme on a blend of Δ total postings, Δ weighted density, Δ demand-validated share, and red-flag count. Present as a research-priority funnel ("deep-dive candidates"), never as a buy list. ## Compliance boundaries Only official, public, unauthenticated job-board APIs. Never scrape LinkedIn, Glassdoor, or Indeed (ToS-prohibited), never bypass rate limits or robots.txt, and never present output as investment advice — it is one input into fundamental research done by a human analyst.