Stock API
zhangxiangliang/stock-api
Fetch real-time stock quotes, K-line (candlestick) history, and search symbols for China A-shares, Hong Kong, and US markets.
Conduct compensation benchmarking analysis to position salaries against market data.
Install Algo Hr Compensation
$ npx skills add asgard-ai-platform/skills --skill algo-hr-compensation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-hr-compensation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-hr-compensation .claude/skills/algo-hr-compensation && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "algo-hr-compensation" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-compensation into .claude/skills/algo-hr-compensation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-compensation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-compensationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add asgard-ai-platform/skills --skill algo-hr-compensation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-hr-compensation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-hr-compensation .agents/skills/algo-hr-compensation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "algo-hr-compensation" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-compensation into .agents/skills/algo-hr-compensation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-compensation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-hr-compensation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-hr-compensation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-hr-compensation .cursor/skills/algo-hr-compensation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "algo-hr-compensation" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-compensation into .cursor/skills/algo-hr-compensation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-compensation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/asgard-ai-platform/skills.git --path algo-hr-compensation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add asgard-ai-platform/skills --skill algo-hr-compensation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-hr-compensation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-hr-compensation .gemini/skills/algo-hr-compensation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "algo-hr-compensation" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-compensation into .gemini/skills/algo-hr-compensation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-compensation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install asgard-ai-platform/skills algo-hr-compensationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add asgard-ai-platform/skills --skill algo-hr-compensation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-hr-compensation .github/skills/algo-hr-compensation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "algo-hr-compensation" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-compensation into .github/skills/algo-hr-compensation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-compensation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-hr-compensation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills algo-hr-compensation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-hr-compensation .opencode/skills/algo-hr-compensation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "algo-hr-compensation" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-compensation into .opencode/skills/algo-hr-compensation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-hr-compensation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
algo-hr-compensation- GitHub stars
- 242
- Token cost
- ~1.1k tokens
- SKILL.md length
- 397 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Conduct compensation benchmarking analysis to position salaries against market data.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to assess pay competitiveness
- SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Algo Hr Compensation is an agent skill from asgard-ai-platform/skills. Conduct compensation benchmarking analysis to position salaries against market data. Use this skill when the user needs to assess pay competitiveness, build salary bands, or analyze pay equity — even if they say 'are we paying market rate', 'salary benchmarking', or 'compensation analysis'.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/band-design.md` and `references/pay-equity.md`).
It sits in Business, Finance & HR, covering Stock and market analysis. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.
When your agent uses it
- The user needs to assess pay competitiveness
- Build salary bands
- Analyze pay equity — even if they say are we paying market rate
- Salary benchmarking
Example prompts
- “are we paying market rate”
- “salary benchmarking”
- “compensation analysis”
- “/algo-hr-compensation”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Algo Hr Compensation loads about 1.1k tokens when it runs, and up to ~6.6k if it reads all its reference files. Until then it costs about 78 tokens; SKILL.md has 397 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 397 words, ~1,089 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-hr-compensation/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-hr-compensation
- description
- Conduct compensation benchmarking analysis to position salaries against market data. Use this skill when the user needs to assess pay competitiveness, build salary bands, or analyze pay equity — even if they say 'are we paying market rate', 'salary benchmarking', or 'compensation analysis'.
- metadata.category
- WP-42 HR 演算法
- metadata.tags
- hr, compensation, benchmarking, salary-analysis
Compensation Benchmarking
Overview
Compensation benchmarking compares internal pay levels against external market data to assess competitiveness. Uses compa-ratio (actual pay / market midpoint) and percentile positioning. Informs salary band design, pay adjustments, and equity analysis.
When to Use
Trigger conditions:
- Evaluating whether current salaries are competitive with the market
- Designing or updating salary bands and pay structures
- Identifying pay equity gaps across demographics or roles
When NOT to use:
- For individual performance-based pay decisions (use performance management)
- When no market data is available (need at least survey benchmarks)
Algorithm
IRON LAW: Benchmarking Is Only Valid With COMPARABLE Jobs
Matching by job TITLE alone is unreliable — "Senior Engineer" means
vastly different things at different companies. Match by: job content
(duties, scope), level (IC vs manager, experience band), industry,
geography, and company size. Poor job matching produces misleading
market rates.Phase 1: Input Validation
Collect: internal compensation data (base, bonus, equity), market survey data (P25, P50, P75 by role), job matching between internal roles and survey benchmarks. Gate: Jobs properly matched, survey data current (< 18 months).
Phase 2: Core Algorithm
- Match internal jobs to market benchmarks by content, level, and scope
- Age survey data to current date: apply projected market movement rate
- Compute compa-ratio per employee: actual base / market P50
- Compute percentile positioning: where does actual pay fall in market distribution
- Analyze: by department, level, tenure, demographics for equity gaps
Phase 3: Verification
Check: compa-ratios cluster around 0.85-1.15 (normal range). Flag outliers (< 0.80 underpaid, > 1.20 overpaid). Test demographic equity. Gate: Distribution reasonable, equity analysis completed.
Phase 4: Output
Return benchmarking results with band recommendations.
Output Format
{
"summary": {"avg_compa_ratio": 0.97, "below_band_pct": 12, "above_band_pct": 8},
"by_role": [{"role": "Software Engineer", "market_p50": 1800000, "avg_actual": 1750000, "compa_ratio": 0.97}],
"equity_flags": [{"dimension": "gender", "gap_pct": 3.2, "statistically_significant": true}],
"metadata": {"employees": 500, "survey_source": "Mercer", "survey_date": "2025-H2"}
}Examples
Sample I/O
Input: 50 engineers, market P50=NT$1.8M, actual range NT$1.5M-2.1M Expected: Avg compa-ratio ~0.97, some below-band employees flagged for adjustment.
Show full SKILL.md (157 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| Hot market (tech boom) | Market data rapidly outdated | Apply higher aging factor |
| Remote work mixed | Location-adjusted bands needed | SF vs Taipei market rates differ 2-3x |
| Small company, no survey match | Use broader industry proxies | Imperfect but better than nothing |
Gotchas
- Total compensation: Base salary benchmarking alone misses equity, bonuses, and benefits. Compare total comp for accurate positioning.
- Survey data lag: Published surveys reflect data collected 6-18 months ago. In fast-moving markets, age the data forward.
- Internal equity vs external competitiveness: Aligning with market may create internal inequities (new hire paid more than tenured employee). Balance both.
- Geographic differentials: Remote work complicates location-based pay. Define a clear policy: pay by HQ location, employee location, or hybrid.
- Pay equity legal risk: Unexplained demographic pay gaps expose legal liability. Conduct regression-based equity analysis controlling for legitimate factors (experience, performance, level).
References
- For salary band design methodology, see
references/band-design.md - For pay equity regression analysis, see
references/pay-equity.md
© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in algo-hr-compensation of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/band-design.md
- references/pay-equity.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Hr Compensation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Algo Hr Compensation this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Stock APIzhangxiangliang/stock-api | 2k | — | ~507 | Automated safety check: Pass | MIT | |
| Tushare Datazillionare/zillionare | 322 | 2 repos | ~2.3k | Automated safety check: Pass | None | |
| Tradingview MCPatilaahmettaner/tradingview-mcp | 5k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Digital Oraclekomako-workshop/digital-oracle | 878 | — | ~5.9k | Automated safety check: Pass | MIT | |
| Longbridge Researchhelsome/folio | 271 | 3 repos | ~2.1k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Algo Hr Compensation
What does Algo Hr Compensation do?
Conduct compensation benchmarking analysis to position salaries against market data. Algo Hr Compensation is an agent skill from asgard-ai-platform/skills. Conduct compensation benchmarking analysis to position salaries against market data.
When should I use Algo Hr Compensation?
Algo Hr Compensation fits situations like: the user needs to assess pay competitiveness; build salary bands; analyze pay equity — even if they say are we paying market rate; salary benchmarking.
How do I install Algo Hr Compensation in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-hr-compensation -a claude-code`. Or copy the skill folder (algo-hr-compensation in asgard-ai-platform/skills) into .claude/skills/algo-hr-compensation in your project. Claude Code loads it when a task matches its description.
How do I install Algo Hr Compensation in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-hr-compensation -a codex`. Or copy the skill folder (algo-hr-compensation in asgard-ai-platform/skills) into .agents/skills/algo-hr-compensation in your project. Codex loads it when a task matches its description.
Can I use Algo Hr Compensation in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill algo-hr-compensation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-hr-compensation, .gemini/skills/algo-hr-compensation, .github/skills/algo-hr-compensation and .opencode/skills/algo-hr-compensation in your project.
What does Algo Hr Compensation need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Hr Compensation is instructions for the agent only.
Does Algo Hr Compensation access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Algo Hr Compensation safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Algo Hr Compensation use?
Algo Hr Compensation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Algo Hr Compensation use?
About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.5k tokens, read only when the agent opens those files.
What are the alternatives to Algo Hr Compensation?
Skills that share tags, products or a category with Algo Hr Compensation: Stock API (zhangxiangliang/stock-api, 2k stars), Tushare Data (zillionare/zillionare, 322 stars), Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars) and Digital Oracle (komako-workshop/digital-oracle, 878 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Hr Compensation?
asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.
Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.