Observability Designer
alirezarezvani/claude-skills
Design production-ready observability strategies combining metrics, logs, and traces.
Apply Bayesian averaging to rank items by combining observed ratings with prior expectations.
MITAuto-check passed
Install Algo Rank Bayesian
$ npx skills add asgard-ai-platform/skills --skill algo-rank-bayesian -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-rank-bayesian --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-rank-bayesian .claude/skills/algo-rank-bayesian && 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-rank-bayesian" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-bayesian into .claude/skills/algo-rank-bayesian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-bayesian", 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-rank-bayesianType 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-rank-bayesian -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-rank-bayesian --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-rank-bayesian .agents/skills/algo-rank-bayesian && 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-rank-bayesian" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-bayesian into .agents/skills/algo-rank-bayesian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-bayesian", 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-rank-bayesian -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-rank-bayesian --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-rank-bayesian .cursor/skills/algo-rank-bayesian && 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-rank-bayesian" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-bayesian into .cursor/skills/algo-rank-bayesian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-bayesian", 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-rank-bayesian--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-rank-bayesian -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-rank-bayesian --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-rank-bayesian .gemini/skills/algo-rank-bayesian && 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-rank-bayesian" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-bayesian into .gemini/skills/algo-rank-bayesian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-bayesian", 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-rank-bayesianInstalls 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-rank-bayesian -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-rank-bayesian .github/skills/algo-rank-bayesian && 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-rank-bayesian" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-bayesian into .github/skills/algo-rank-bayesian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-bayesian", 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-rank-bayesian -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-rank-bayesian --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-rank-bayesian .opencode/skills/algo-rank-bayesian && 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-rank-bayesian" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-rank-bayesian into .opencode/skills/algo-rank-bayesian/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-rank-bayesian", 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-rank-bayesian- GitHub stars
- 242
- Token cost
- ~1.1k tokens
- SKILL.md length
- 456 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Apply Bayesian averaging to rank items by combining observed ratings with prior expectations.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to rank items with varying review counts
- SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 4 more sections
- Runs Python scripts from its folder; calls python
What it does
Algo Rank Bayesian is an agent skill from asgard-ai-platform/skills. Apply Bayesian averaging to rank items by combining observed ratings with prior expectations. Use this skill when the user needs to rank items with varying review counts, build a 'top rated' list that handles low-sample items fairly, or implement IMDB-style weighted rating — even if they say 'weighted average rating', 'IMDB formula', or 'ranking with prior'.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `examples/sample_input.json`, `references/imdb-formula.md` and `references/multi-dimensional.md`).
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 rank items with varying review counts
- Build a top rated list that handles low-sample items fairly
- Implement IMDB-style weighted rating — even if they say weighted average rating
- Ranking with prior
Example prompts
- “top rated”
- “weighted average rating”
- “IMDB formula”
- “/algo-rank-bayesian”
Requirements
- Python 3
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
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
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 Rank Bayesian loads about 1.1k tokens when it runs, and up to ~5.4k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 456 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); the scripts in this folder are not scanned.
SKILL.md
The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 456 words, ~1,127 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-rank-bayesian/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- algo-rank-bayesian
- description
- Apply Bayesian averaging to rank items by combining observed ratings with prior expectations. Use this skill when the user needs to rank items with varying review counts, build a 'top rated' list that handles low-sample items fairly, or implement IMDB-style weighted rating — even if they say 'weighted average rating', 'IMDB formula', or 'ranking with prior'.
- metadata.category
- WP-44 排名演算法
- metadata.tags
- ranking, bayesian-average, rating, prior
Bayesian Average Rating
Overview
Bayesian average combines an item's observed average rating with a prior (global average), weighted by review count. Formula: BR = (C × m + Σrᵢ) / (C + n) where m=global mean, C=confidence parameter, n=item reviews, Σrᵢ=sum of item ratings. Items with few reviews are pulled toward the global mean.
When to Use
Trigger conditions:
- Ranking items by continuous ratings (1-5 stars) with varying review counts
- IMDB-style "Top 250" lists that balance quality and popularity
- Any rating aggregation where new items shouldn't dominate with few high ratings
When NOT to use:
- For binary (upvote/downvote) data (use Wilson Score instead)
- When all items have similar review counts (simple average is sufficient)
Algorithm
IRON LAW: The Prior Protects Against Small-Sample Extremes
Without a prior, a single 5-star review makes an item "the best."
The Bayesian average adds C "phantom votes" at the global mean m,
shrinking small-sample items toward average. C controls shrinkage
strength: higher C = more conservative (more phantom votes).
Typical C = median review count across all items.Phase 1: Input Validation
Compute: global mean rating (m) across all items, choose C (phantom vote count). Collect per item: review count (n), average rating, or sum of ratings. Gate: m computed, C selected, item data available.
Phase 2: Core Algorithm
- Global mean: m = Σ(all ratings) / Σ(all review counts)
- Bayesian average per item: BR = (C × m + n × avg_rating) / (C + n)
- Rank items by BR descending
- For items with n >> C, BR ≈ avg_rating (data dominates). For n << C, BR ≈ m (prior dominates).
Phase 3: Verification
Check: items with very few reviews should be near global mean. Items with many reviews should be near their actual average. Ranking is intuitive. Gate: Shrinkage behavior confirmed, top items have both high ratings AND sufficient reviews.
Phase 4: Output
Return ranked items with Bayesian scores.
Output Format
{
"rankings": [{"item": "Movie_A", "bayesian_avg": 8.7, "raw_avg": 9.1, "reviews": 5000, "shrinkage": 0.04}],
"metadata": {"global_mean": 6.8, "confidence_C": 500, "items_ranked": 10000}
}Examples
Sample I/O
Input: m=7.0, C=100. Item A: avg=9.5, n=5. Item B: avg=8.5, n=500. Expected: BR_A = (100×7 + 5×9.5)/(105) = 7.12. BR_B = (100×7 + 500×8.5)/(600) = 8.25. B ranks higher.
Show full SKILL.md (184 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| n=0 | BR = m (global mean) | No data, fully prior-driven |
| n=100000 | BR ≈ raw average | Massive sample overwhelms prior |
| All items same n | Equivalent to simple average ranking | Uniform shrinkage, ordering preserved |
Gotchas
- C selection is subjective: Common choices: median review count, minimum reviews for "reliable" rating (IMDB uses top 25,000 voters with min votes). No universally correct value.
- Rating scale matters: A 4.0 on a 5-point scale means something different than 4.0 on a 10-point scale. Normalize or use the same scale.
- Category-specific priors: A 4.0 average in "horror movies" might be exceptional, while 4.0 in "Studio Ghibli" might be below average. Consider category-level priors.
- Temporal bias: Old items accumulate reviews. Unless you weight recent reviews more, established items permanently dominate "top" lists.
- Review gaming: Bayesian average doesn't prevent review manipulation — it only mitigates small-sample extremes. Pair with fraud detection.
Scripts
| Script | Description | Usage |
|---|---|---|
scripts/bayesian_avg.py | Rank items using Bayesian average to handle small-sample extremes | python scripts/bayesian_avg.py --help |
Run python scripts/bayesian_avg.py --verify to execute built-in sanity tests.
References
- For IMDB weighted rating formula, see
references/imdb-formula.md - For multi-dimensional Bayesian rating, see
references/multi-dimensional.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 4 other files (scripts, references) in algo-rank-bayesian of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_input.json
- references/imdb-formula.md
- references/multi-dimensional.md
- scripts/bayesian_avg.py
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Rank Bayesian 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 Rank Bayesian this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Observability Designeralirezarezvani/claude-skills | 28k | — | ~3.5k | Automated safety check: Pass | MIT | |
| Langsmith ObservabilityOrchestra-Research/AI-Research-SKILLs | 13k | 2 repos | ~2.4k | Automated safety check: Pass | MIT | |
| ObservabilityBuilderIO/agent-native | 7.1k | — | ~7.3k | Automated safety check: Pass | None | |
| Frontend Observabilitysickn33/agentic-awesome-skills | 47k | 1 repos | ~5.1k | Automated safety check: Pass | MIT | |
| Ebpf Observabilitysickn33/agentic-awesome-skills | 47k | 2 repos | ~3.3k | Automated safety check: Notes | MIT |
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Questions about Algo Rank Bayesian
What does Algo Rank Bayesian do?
Apply Bayesian averaging to rank items by combining observed ratings with prior expectations. Algo Rank Bayesian is an agent skill from asgard-ai-platform/skills. Apply Bayesian averaging to rank items by combining observed ratings with prior expectations.
When should I use Algo Rank Bayesian?
Algo Rank Bayesian fits situations like: the user needs to rank items with varying review counts; build a top rated list that handles low-sample items fairly; implement IMDB-style weighted rating — even if they say weighted average rating; ranking with prior.
How do I install Algo Rank Bayesian in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-rank-bayesian -a claude-code`. Or copy the skill folder (algo-rank-bayesian in asgard-ai-platform/skills) into .claude/skills/algo-rank-bayesian in your project. Claude Code loads it when a task matches its description.
How do I install Algo Rank Bayesian in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-rank-bayesian -a codex`. Or copy the skill folder (algo-rank-bayesian in asgard-ai-platform/skills) into .agents/skills/algo-rank-bayesian in your project. Codex loads it when a task matches its description.
Can I use Algo Rank Bayesian 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-rank-bayesian -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-rank-bayesian, .gemini/skills/algo-rank-bayesian, .github/skills/algo-rank-bayesian and .opencode/skills/algo-rank-bayesian in your project.
What does Algo Rank Bayesian need to run?
Going by SKILL.md and its folder, Algo Rank Bayesian needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Algo Rank Bayesian 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 Rank Bayesian 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Algo Rank Bayesian use?
Algo Rank Bayesian 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 Rank Bayesian use?
About 1.1k tokens (SKILL.md is roughly 4.5k 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 4.3k tokens, read only when the agent opens those files.
What are the alternatives to Algo Rank Bayesian?
Skills that share tags, products or a category with Algo Rank Bayesian: Observability Designer (alirezarezvani/claude-skills, 28k stars), Langsmith Observability (Orchestra-Research/AI-Research-SKILLs, 13k stars), Observability (BuilderIO/agent-native, 7.1k stars) and Frontend Observability (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Rank Bayesian?
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.