Agent skill

Log And Error Bucketing

MITAuto-check passed

Install Log And Error Bucketing

skills CLI

$ npx skills add mrmps/classifier-dev --skill log-and-error-bucketing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install mrmps/classifier-dev log-and-error-bucketing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/mrmps/classifier-dev.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/log-and-error-bucketing .claude/skills/log-and-error-bucketing && rm -rf skills-src

Use ~/.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/

Paste into Claude Code

Install the "log-and-error-bucketing" agent skill from https://github.com/mrmps/classifier-dev/tree/main/skills/log-and-error-bucketing into .claude/skills/log-and-error-bucketing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "log-and-error-bucketing", 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

$skill-installer install https://github.com/mrmps/classifier-dev/tree/main/skills/log-and-error-bucketing

Type 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.

skills CLI

$ npx skills add mrmps/classifier-dev --skill log-and-error-bucketing -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install mrmps/classifier-dev log-and-error-bucketing --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/mrmps/classifier-dev.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/log-and-error-bucketing .agents/skills/log-and-error-bucketing && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "log-and-error-bucketing" agent skill from https://github.com/mrmps/classifier-dev/tree/main/skills/log-and-error-bucketing into .agents/skills/log-and-error-bucketing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "log-and-error-bucketing", 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.

skills CLI

$ npx skills add mrmps/classifier-dev --skill log-and-error-bucketing -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install mrmps/classifier-dev log-and-error-bucketing --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/mrmps/classifier-dev.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/log-and-error-bucketing .cursor/skills/log-and-error-bucketing && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "log-and-error-bucketing" agent skill from https://github.com/mrmps/classifier-dev/tree/main/skills/log-and-error-bucketing into .cursor/skills/log-and-error-bucketing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "log-and-error-bucketing", 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

$ gemini skills install https://github.com/mrmps/classifier-dev.git --path skills/log-and-error-bucketing

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add mrmps/classifier-dev --skill log-and-error-bucketing -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install mrmps/classifier-dev log-and-error-bucketing --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/mrmps/classifier-dev.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/log-and-error-bucketing .gemini/skills/log-and-error-bucketing && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "log-and-error-bucketing" agent skill from https://github.com/mrmps/classifier-dev/tree/main/skills/log-and-error-bucketing into .gemini/skills/log-and-error-bucketing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "log-and-error-bucketing", 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.

GitHub CLI

$ gh skill install mrmps/classifier-dev log-and-error-bucketing

Installs 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).

skills CLI

$ npx skills add mrmps/classifier-dev --skill log-and-error-bucketing -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/mrmps/classifier-dev.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/log-and-error-bucketing .github/skills/log-and-error-bucketing && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "log-and-error-bucketing" agent skill from https://github.com/mrmps/classifier-dev/tree/main/skills/log-and-error-bucketing into .github/skills/log-and-error-bucketing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "log-and-error-bucketing", 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.

skills CLI

$ npx skills add mrmps/classifier-dev --skill log-and-error-bucketing -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install mrmps/classifier-dev log-and-error-bucketing --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/mrmps/classifier-dev.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/log-and-error-bucketing .opencode/skills/log-and-error-bucketing && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "log-and-error-bucketing" agent skill from https://github.com/mrmps/classifier-dev/tree/main/skills/log-and-error-bucketing into .opencode/skills/log-and-error-bucketing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "log-and-error-bucketing", 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: log-and-error-bucketing
GitHub stars: 424
Token cost: ~1.5k tokens
SKILL.md length: 574 words
Files: 1
Skills in repo: 21
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Works in 4 steps: collapse to templates, keep the counts → name the buckets, then classify → read the shape → …
An incident dump
SKILL.md covers What leaves the machine, When not to use it, Step 1: collapse to templates,… and Step 2: name the buckets, then…, plus 3 more sections
Calls npx

What it does

Log And Error Bucketing is an agent skill from mrmps/classifier-dev. Turn a stream of log lines or error messages into a histogram of causes — timeouts, auth, quota, bad input, upstream, unknown — by deduplicating to templates, redacting, and classifying one exemplar each, so a million lines cost a few hundred calls. Use when an incident dump, CI log or error table is too big to read. Triggers on "what is failing here", "bucket these errors", "group these exceptions", "top causes".

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Zero-shot text classification over plain HTTP — no API key, no account. One Cloudflare Worker, a CLI, and an MCP server. https://classifier.dev. The licence is MIT.

When your agent uses it

An incident dump
Error table is too big to read
What is failing here
Bucket these errors

Example prompts

“what is failing here”
“bucket these errors”
“group these exceptions”
“/log-and-error-bucketing”

Requirements

Python 3
Node.js

Workflow steps

4 steps, taken from the step headings in SKILL.md.

collapse to templates, keep the counts
name the buckets, then classify
read the shape
the gates

What it can do on your machine

Read from SKILL.md and the folder at commit 629df75. 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
Shell commands in SKILL.md call:
- npx
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- classifier.dev
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

Log And Error Bucketing loads about 1.5k tokens when it runs. Until then it costs about 110 tokens; SKILL.md has 574 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~110
When it runs · the whole SKILL.md, loaded when a task matches: ~1.5k

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 mrmps/classifier-dev at commit 629df75, republished under its MIT licence (© mrmps). 574 words, ~1,485 tokens.

Download SKILL.mdSave it as .claude/skills/log-and-error-bucketing/SKILL.md (or your agent's skills folder).

name: log-and-error-bucketing
description: Turn a stream of log lines or error messages into a histogram of causes — timeouts, auth, quota, bad input, upstream, unknown — by deduplicating to templates, redacting, and classifying one exemplar each, so a million lines cost a few hundred calls. Use when an incident dump, CI log or error table is too big to read. Triggers on "what is failing here", "bucket these errors", "group these exceptions", "top causes".
license: MIT

Bucket a log stream by cause

A log is mostly the same twenty lines with different numbers in them. Read it by collapsing it to those twenty, labelling each by cause and weighting them by how often they occurred. The labelling step below is classifier.dev: keyless HTTP, your bucket names, a calibrated confidence, no text generated.

What leaves the machine

Log lines carry tokens, emails and account numbers. One redacted line per template goes out, nothing else: no file, no host name, no context.

python

import re
PATTERNS = [(r"(?i)\b(?:bearer|basic)\s+[\w.\-+/=]{8,}", "<CRED>"),
    (r"(?i)\b[\w.-]*(?:key|token|secret|password|pwd)[\w.-]*\s*[=:]\s*[^\s\"',&]{6,}", "<CRED>"),
    (r"\b[A-Za-z0-9+/]{32,}={0,2}\b", "<BLOB>"), (r"\b[0-9a-f]{16,}\b", "<BLOB>"),
    (r"[\w.+-]+@[\w-]+\.[\w.]{2,}", "<EMAIL>"), (r"\b(?:\d[ -]?){13,16}\b", "<CARD>"),
    (r"\b\d{3}-\d{2}-\d{4}\b", "<SSN>"), (r"\b\d{7,}\b", "<NUM>")]

def redact(t):
    for p, tag in PATTERNS:
        t = re.sub(p, tag, t)
    return t

def sendable(t):                      # never send a mostly-redacted line
    kept = len(re.sub(r"<[A-Z]+>", "", redact(t)))
    return kept >= 25 and kept >= 0.5 * len(t)

Two lines through it, classified:

ERROR [api] auth failed: authorization: <CRED> upstream returned 401
   -> authentication or permission denied   1.00
WARN retry: <CRED> quota exhausted for project <NUM>
   -> quota or rate limit exceeded          1.00

The credential and the project id are gone; the cause still reads at 1.00. A line that is mostly placeholders afterwards is one nobody could bucket, so sendable keeps it at home.

When not to use it

If the logs may not leave the building, keep the workflow and change the classifier: the shape is dedupe, label, weight, gate, and anything returning a calibrated confidence fits — your own model over the same bucket names, or a local zero-shot model. classifier.dev is the keyless example because it needs no account; it states that it stores no input text and passes it to the model that answers (https://classifier.dev/privacy).

Skip it as well for logs that already carry an error code (group by the code, free and exact), for triage you can read in a second, and for finding one rare line: this counts what is common.

Step 1: collapse to templates, keep the counts

python

import collections
def norm(line):
    s = re.sub(r"\b\d{1,3}(\.\d{1,3}){3}(:\d+)?", "<ip>", line)
    s = re.sub(r"\b[0-9a-f]{8,}\b", "<hex>", s)
    s = re.sub(r"\b(Mon|Tue|Wed|Thu|Fri|Sat|Sun)\b", "<day>", s)
    return " ".join(re.sub(r"\d+", "<n>", s).split())

groups = collections.OrderedDict()
for line in lines:                    # ERROR and WARN only
    groups.setdefault(norm(line), []).append(line)
exemplars = [redact(v[0]) for v in groups.values() if sendable(v[0])]

Send the first real line of each group, redacted, not the masked template: <n> and <ip> read less like language than the line they came from.

On 1,926 error and warning lines from the public loghub samples (ZooKeeper, OpenStack, Apache) this gave 21 templates: one classification per 92 lines. The weekday mask earns its place — without it the same Apache error from a Sunday and a Monday are two templates, and the list is 25.

Show full SKILL.md (258 more words)Show less

Step 2: name the buckets, then classify

POST the exemplars as inputs, your bucket names as labels, the sentence below as instructions; 21 templates came back in 268 ms. The CLI does it too, fetched at a pinned version and left uninstalled:

L='timed out waiting for something,authentication or permission denied,quota or rate limit exceeded,bad or malformed input from the caller,an upstream or dependent service failed,resource exhausted: memory disk or connections,unknown or other'
npx --yes classifier-dev@0.1.3 "$L" -i "Bucket the log line by the underlying cause of the failure it reports." --count < redacted.txt

That is one vote per template: 17 of the 21 landed in unknown or other. CLI labels are comma-separated, so no label may contain a comma.

Step 3: read the shape

Weighted by group size, those seven buckets put 1,805 of the 1,926 lines in unknown or other, mostly at 0.4 to 0.7 confidence. That is not a broken model: a fat unknown bucket at middling confidence means your labels are missing a cause. These lines were peer churn and worker lifecycle. Add two labels, rerun the same 21:

   1138  a worker or child process started, exited or restarted
    711  a network connection to a peer dropped or was reset
     41  unknown or other
     32  authentication or permission denied
      3  an upstream or dependent service failed

unknown fell from 1,805 lines to 41, in 254 ms. Four of the 21 templates are still under 0.5: read those yourself.

Step 4: the gates

0.9 and above — file the template under that cause.
0.5 to 0.9 — file it, but show the exemplar in the report, or re-ask on --smart ("tier": "smart").
below 0.5 — leave it in unknown; --review 0.5 prints those rows.
confidence: null with an unscored reason — no comparable provider score is available, so leave the row for review. Scores do not validate the input: include noise or separator and unknown or other when those inputs are possible.

Done looks like

A table of causes with line counts, each traceable to a template and a redacted line; the sub-0.5 and unscored rows listed apart.

Files

Just SKILL.md in skills/log-and-error-bucketing of mrmps/classifier-dev.

Open the folder on GitHubat commit 629df75

Compare with similar skills

Log And Error Bucketing 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.

Log And Error Bucketing compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Log And Error Bucketing this skillmrmps/classifier-dev	424	—	~1.5k	Automated safety check: Pass	MIT	2 days ago
Messages Opsaffaan-m/ECC	276k	1 repos	~724	Automated safety check: Pass	MIT	4 days ago
AWS Messaging And Streamingaws/agent-toolkit-for-aws	2.8k	—	~3.1k	Automated safety check: Pass	Apache-2.0	today
Investigating LogsPostHog/posthog	40k	—	~2.1k	Automated safety check: Pass	Custom licence	today
Logging and Error Reporting for Warpwarpdotdev/warp	65k	1 repos	~5.6k	Automated safety check: Pass	AGPL-3.0	today
Growth Logaffaan-m/ECC	276k	1 repos	~1.7k	Automated safety check: Pass	MIT	4 days ago

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Questions about Log And Error Bucketing

What does Log And Error Bucketing do?

Turn a stream of log lines or error messages into a histogram of causes — timeouts, auth, quota, bad input, upstream, unknown — by deduplicating to templates, redacting, and classifying one exemplar…. Log And Error Bucketing is an agent skill from mrmps/classifier-dev. Turn a stream of log lines or error messages into a histogram of causes — timeouts, auth, quota, bad input, upstream, unknown — by deduplicating to templates, redacting, and classifying one exemplar each, so a million lines cost a few hundred calls.

When should I use Log And Error Bucketing?

Log And Error Bucketing fits situations like: an incident dump; error table is too big to read; what is failing here; bucket these errors.

How do I install Log And Error Bucketing in Claude Code?

Run `npx skills add mrmps/classifier-dev --skill log-and-error-bucketing -a claude-code`. Or copy the skill folder (skills/log-and-error-bucketing in mrmps/classifier-dev) into .claude/skills/log-and-error-bucketing in your project. Claude Code loads it when a task matches its description.

How do I install Log And Error Bucketing in Codex?

Run `npx skills add mrmps/classifier-dev --skill log-and-error-bucketing -a codex`. Or copy the skill folder (skills/log-and-error-bucketing in mrmps/classifier-dev) into .agents/skills/log-and-error-bucketing in your project. Codex loads it when a task matches its description.

Can I use Log And Error Bucketing 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 mrmps/classifier-dev --skill log-and-error-bucketing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/log-and-error-bucketing, .gemini/skills/log-and-error-bucketing, .github/skills/log-and-error-bucketing and .opencode/skills/log-and-error-bucketing in your project.

What does Log And Error Bucketing need to run?

Going by SKILL.md and its folder, Log And Error Bucketing needs the command-line tools its instructions call (npx). Our summary lists: Python 3; Node.js.

Does Log And Error Bucketing access the network?

SKILL.md names 1 domain. As links in the text: classifier.dev. This is read from the text; nothing was executed.

Is Log And Error Bucketing 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 Log And Error Bucketing use?

Log And Error Bucketing is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Log And Error Bucketing use?

About 1.5k tokens (SKILL.md is roughly 5.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Log And Error Bucketing?

Skills that share tags, products or a category with Log And Error Bucketing: Messages Ops (affaan-m/ECC, 276k stars), AWS Messaging And Streaming (aws/agent-toolkit-for-aws, 2.8k stars), Investigating Logs (PostHog/posthog, 40k stars) and Logging and Error Reporting for Warp (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Log And Error Bucketing?

mrmps (a GitHub user) maintains it in mrmps/classifier-dev, which has 424 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on October 7, 2026.

Source: mrmps/classifier-dev on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Log And Error Bucketing

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Bucket a log stream by cause

What leaves the machine

When not to use it

Step 1: collapse to templates, keep the counts

Step 2: name the buckets, then classify

Step 3: read the shape

Step 4: the gates

Done looks like

Files

Compare with similar skills

Similar skills

More from mrmps/classifier-dev

Questions about Log And Error Bucketing