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Spl To Apl

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openclaw
spl-to-apl

Translates Splunk SPL queries to Axiom APL. Provides command mappings, function equivalents, and syntax transformations. Use when migrating from Splunk, converting SPL queries, or learning APL equivalents of SPL patterns.

Overview

Publisheropenclaw
Repositoryclawhub
Skill namespl-to-apl
Stars
9.5K
Forks
1.5K
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by openclaw on GitHub. Read the source before you install it.

Installation

Install the Spl To Apl AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/openclaw/clawhub/tree/main/.agents/skills/spl-to-apl
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/openclaw/clawhub.git /tmp/clawhub
mkdir -p .claude/skills
cp -r /tmp/clawhub/.agents/skills/spl-to-apl .claude/skills/spl-to-apl
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spl To Apl in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Spl To Apl on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Spl To Apl is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SPL to APL Translator

Type safety: Fields like status are often stored as strings. Always cast before numeric comparison: toint(status) >= 500, not status >= 500.


Critical Differences

  1. Time is explicit in APL: SPL time pickers don't translate — add where _time between (ago(1h) .. now())
  2. Structure: SPL index=... | command → APL ['dataset'] | operator
  3. Join is preview: limited to 50k rows, inner/innerunique/leftouter only
  4. cidrmatch args reversed: SPL cidrmatch(cidr, ip) → APL ipv4_is_in_range(ip, cidr)

Core Command Mappings

SPLAPLNotes
search index=...['dataset']Dataset replaces index
search field=valuewhere field == "value"Explicit where
wherewhereSame
statssummarizeDifferent aggregation syntax
evalextendCreate/modify fields
table / fieldsprojectSelect columns
fields -project-awayRemove columns
rename x as yproject-rename y = xRename
sort / sort -order by ... asc/descSort
head Ntake NLimit rows
top N fieldsummarize count() by field | top N by count_Two-step
dedup fieldsummarize arg_max(_time, *) by fieldKeep latest
rexparse or extract()Regex extraction
joinjoinPreview feature
appendunionCombine datasets
mvexpandmv-expandExpand arrays
timechart span=Xsummarize ... by bin(_time, X)Manual binning
rare N fieldsummarize count() by field | order by count_ asc | take NBottom N
spathparse_json() or json['path']JSON access
transactionNo direct equivalentUse summarize + make_list

Complete mappings: reference/command-mapping.md


Stats → Summarize

# SPL
| stats count by status

# APL  
| summarize count() by status

Key function mappings

SPLAPL
countcount()
count(field)countif(isnotnull(field))
dc(field)dcount(field)
avg/sum/min/maxSame
median(field)percentile(field, 50)
perc95(field)percentile(field, 95)
first/lastarg_min/arg_max(_time, field)
list(field)make_list(field)
values(field)make_set(field)

Conditional count pattern

# SPL
| stats count(eval(status>=500)) as errors by host

# APL
| summarize errors = countif(status >= 500) by host

Complete function list: reference/function-mapping.md


Eval → Extend

# SPL
| eval new_field = old_field * 2

# APL
| extend new_field = old_field * 2

Key function mappings

SPLAPLNotes
if(c, t, f)iff(c, t, f)Double 'f'
case(c1,v1,...)case(c1,v1,...,default)Requires default
len(str)strlen(str)
lower/uppertolower/toupper
substrsubstring0-indexed in APL
replacereplace_string
tonumbertoint/tolong/torealExplicit types
match(s,r)s matches regex "r"Operator
split(s, d)split(s, d)Same
mvjoin(mv, d)strcat_array(arr, d)Join array
mvcount(mv)array_length(arr)Array length

Case statement pattern

# SPL
| eval level = case(
    status >= 500, "error",
    status >= 400, "warning",
    1==1, "ok"
  )

# APL  
| extend level = case(
    status >= 500, "error",
    status >= 400, "warning",
    "ok"
  )

Note: SPL's 1==1 catch-all becomes implicit default in APL.


Rex → Parse/Extract

# SPL
| rex field=message "user=(?<username>\w+)"

# APL - parse with regex
| parse kind=regex message with @"user=(?P<username>\w+)"

# APL - extract function  
| extend username = extract("user=(\\w+)", 1, message)

Simple pattern (non-regex)

# SPL
| rex field=uri "^/api/(?<version>v\d+)/(?<endpoint>\w+)"

# APL
| parse uri with "/api/" version "/" endpoint

Time Handling

SPL time pickers don't translate. Always add explicit time range:

# SPL (time picker: Last 24 hours)
index=logs

# APL
['logs'] | where _time between (ago(24h) .. now())

Timechart translation

# SPL
| timechart span=5m count by status

# APL
| summarize count() by bin(_time, 5m), status

Common Patterns

Error rate calculation

# SPL
| stats count(eval(status>=500)) as errors, count as total by host
| eval error_rate = errors/total*100

# APL
| summarize errors = countif(status >= 500), total = count() by host
| extend error_rate = toreal(errors) / total * 100

Subquery (subsearch)

# SPL
index=logs [search index=errors | fields user_id | format]

# APL
let error_users = ['errors'] | where _time between (ago(1h) .. now()) | distinct user_id;
['logs']
| where _time between (ago(1h) .. now())
| where user_id in (error_users)

Join datasets

# SPL
| join user_id [search index=users | fields user_id, name]

# APL
| join kind=inner (['users'] | project user_id, name) on user_id

Transaction-like grouping

# SPL
| transaction session_id maxspan=30m

# APL (no direct equivalent — reconstruct with summarize)
| summarize 
    start_time = min(_time),
    end_time = max(_time),
    events = make_list(pack("time", _time, "action", action)),
    duration = max(_time) - min(_time)
  by session_id
| where duration <= 30m

String Matching Performance

SPLAPLSpeed
field="value"field == "value"Fastest
field="*value*"field contains "value"Moderate
field="value*"field startswith "value"Fast
match(field, regex)field matches regex "..."Slowest

Prefer has over contains (word-boundary matching is faster). Use _cs variants for case-sensitive (faster).


Reference

  • reference/command-mapping.md — complete command list
  • reference/function-mapping.md — complete function list
  • reference/examples.md — full query translation examples
  • APL docs: https://axiom.co/docs/apl/introduction

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Spl To Apl AI skill do?

Translates Splunk SPL queries to Axiom APL. Provides command mappings, function equivalents, and syntax transformations. Use when migrating from Splunk, converting SPL queries, or learning APL equivalents of SPL patterns.

Why use Spl To Apl on TypingMind?

Because you install it once and use it with any model. Spl To Apl is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Spl To Apl in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/clawhub/tree/main/.agents/skills/spl-to-apl. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Spl To Apl?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Spl To Apl?

As many as you like. As long as a model supports skills, you can use Spl To Apl with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Spl To Apl AI skill free?

Yes. It is published on GitHub by openclaw under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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