field-extraction-and-cim-mapping: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

Author, explain, diagnose, and validate Splunk search-time field extractions and mappings to Common Information Model (CIM) datasets from representative events, configuration, and search evidence. Use for automatic key-value extraction, regex or delimiter extraction, props.conf EXTRACT and REPORT/transforms.conf rules, SPL extraction commands, aliases, calculated fields, lookups, event types, tags, value normalization, CIM field mapping, and missing or incorrect normalization; do not use for deployment execution, ingestion transport, app installation, knowledge-object governance, data-model acceleration, or unrelated search/dashboard repair.

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Field Extraction and CIM Mapping

Produce documentation-backed, evidence-bound guidance for search-time field extraction and CIM normalization. Draft text artifacts only. Never authenticate to, modify, install on, or deploy to a Splunk environment.

Prerequisites

Record or ask for only the missing items material to the requested decision:

representative sanitized raw events, including meaningful edge cases;
sourcetype and relevant current props.conf, transforms.conf, SPL, aliases, calculated fields, lookups, event types, and tags;
desired source fields and their meanings;
target CIM dataset and installed CIM version or model details;
Splunk product/version, persistence scope, app context, and deployment topology when configuration or routing depends on them; and
observed field/search/data-model validation output when diagnosing or claiming validation.

Never invent event structure or field meaning. With partial evidence, first preserve and assess every supported object-level fact. Mark each absent field or artifact unknown, and gate only the conclusion it affects; missing context must not erase supplied evidence. Clearly separate supplied observations, documented facts, assumptions, provisional conclusions, and unverified steps.

When to Use

Use this skill when the primary outcome is documented extraction guidance, an evidence-bound extraction draft, a semantic CIM mapping, validation steps, or a normalization-gap diagnosis. Apply the boundaries below to adjacent work.

Workflow Overview

1. Select the extraction mechanism

Read public-guidance.md. Identify the documented mechanism that fits the event shape: automatic key-value extraction, regex or delimiter extraction, inline EXTRACT, reusable REPORT plus transforms.conf, or an ad hoc SPL command. Separate persistent knowledge objects from search-local SPL. Treat index-time extraction as a performance-sensitive exception, not the default.

If source events or deployment context are absent, give only cited general guidance. End the answer with a direct request for representative events, sourcetype, product and version, and desired persistence scope before proposing concrete configuration; merely listing these inputs as absent does not satisfy the request.

2. Draft an evidence-bound extraction

Use the smallest approach that targets the supplied samples. Provide concrete props.conf, transforms.conf, or SPL only when representative events and desired fields are supplied. Explain why it matches the event shape and state assumptions about sourcetype, app context, delimiters, cardinality, multivalue behavior, and persistent search-time versus ad hoc scope.

Flag broad unnecessary key-value extraction, unbounded variable-key expansion, duplicated calculated-field logic, brittle sample-only matching, and premature index-time extraction. If evidence is insufficient, ask for the missing sample and desired fields or give only a clearly marked, unvalidated template/checklist.

3. Build a semantic CIM mapping plan

Name the target CIM dataset; if event meaning permits several candidates, ask the user to choose and explain the candidates provisionally. Map by source-field semantics, not name similarity. Where evidence permits, distinguish required fields, recommended or expected fields, required tags, dataset constraints, aliases, calculated fields, lookups, event types, enrichment, and value normalization.

If the target dataset, source semantics, or installed model details are missing, preserve any supported field facts, label candidate mappings provisional, and request the smallest missing evidence. Direct the user to the Data Model Editor or installed model JSON for complete constraints and inherited fields; do not claim the public reference tables are complete.

4. Define or assess validation

Provide the relevant inspection path: normal search field inspection, Pivot/Datasets, datamodel or from datamodel, datamodelsimple, or the CIM Validation data model's Missing Extractions and Untagged Events datasets. State the expected success evidence: correct values appear, required tags/event types select the intended events, and edge cases preserve extraction behavior.

Call runtime validation unverified unless actual search or deployment results are supplied. When results are absent, provide only commands and expected observations. When results are present, assess only what they demonstrate.

Keep a validation-only answer bounded. Use one short status statement, at most five focused validation steps, and one compact expected-evidence checklist. Prefer one representative search per distinct validation purpose instead of enumerating variants. Do not repeat prerequisites, boundaries, citations, or the same caveat in multiple sections. A validation-only answer must be 900 words or fewer. If the user requests a longer runbook, first return a complete validation answer within this limit; provide the longer runbook only afterward.

5. Diagnose normalization gaps

Connect every suspected missing or incorrect extraction, alias, lookup, tag, event type, calculated field, or CIM mapping to a supplied raw event, configuration fragment, field output, or data-model validation result. Preserve confirmed facts even when other artifacts are absent.

Separate semantic/configuration explanations from possible deployment, app-installation, cluster-bundle, managed Cloud change, or acceleration causes. Provide a bounded semantic remediation plan and the exact evidence needed to confirm it. If only symptoms are supplied, do not diagnose: ask for the smallest safe subset of representative events, relevant current configuration, search output, target dataset, product/version, and topology needed for the pending decision.

Boundaries

Keep extraction authoring and CIM-mapping semantics here. Route only work that crosses the boundary:

governance, ownership, naming, packaging policy, and lifecycle decisions to Knowledge Object Governance;
source onboarding transport, HEC, tokens, and index routing to the relevant ingestion owner;
acceleration design, tstats tuning, summaries, and acceleration failures to Data Model and Search Acceleration;
unrelated search/dashboard remediation to its troubleshooting owner; and
app installation, managed Cloud changes, cluster bundles, approvals, and production deployment to the appropriate Splunk operator.

Do not claim runtime verification, publication readiness, prevalence, cross-system linkage, telemetry baselines, or rollback readiness without direct evidence.

Examples

“Choose a persistent extraction for these sanitized events and fields.”
“Map these existing fields to the Authentication CIM dataset.”
“Assess these Missing Extractions results against the supplied config.”

Troubleshooting

No samples: provide cited mechanism guidance or an unvalidated template, then request representative events and desired fields.
Partial artifacts: retain every supported fact, mark only missing facts unknown, and gate only the affected mapping or diagnosis.
No runtime results: provide validation commands and expected observations; label validation unverified.
Operational cause remains possible: separate it from the semantic finding and route only the operational action that crosses the boundary.

Final-answer contract

Before returning, verify:

Put a point-of-use public citation beside every decisive documentation-backed action or claim.
Before evidence-dependent diagnosis, request the smallest safe evidence set; preserve every supported object-level fact and let absent fields limit only the affected conclusion.
State assumptions, expected success evidence, and what remains provisional or unverified.
Name an owner or route only when the answer crosses this skill's boundary; otherwise state that the answer remains within bounded field-extraction and CIM-mapping scope.

View source ↗

File metadata

name: field-extraction-and-cim-mapping
description: Author, explain, diagnose, and validate Splunk search-time field extractions and mappings to Common Information Model (CIM) datasets from representative events, configuration, and search evidence. Use for automatic key-value extraction, regex or delimiter extraction, props.conf EXTRACT and REPORT/transforms.conf rules, SPL extraction commands, aliases, calculated fields, lookups, event types, tags, value normalization, CIM field mapping, and missing or incorrect normalization; do not use for deployment execution, ingestion transport, app installation, knowledge-object governance, data-model acceleration, or unrelated search/dashboard repair.
license: Apache-2.0
allowed-tools:
  - web
metadata:
  splunk:
    domain: data-normalization
    products:
      - splunk-cloud-platform
      - splunk-enterprise
    entities:
      - search-time field extractions
      - props.conf and transforms.conf
      - field aliases, calculated fields, and lookups
      - event types and tags
      - CIM datasets and normalized fields
    triggers:
      - extract fields from sample events
      - write EXTRACT or REPORT rules
      - map fields to CIM
      - validate CIM normalization
      - diagnose missing fields, aliases, lookups, tags, or event types
    not-for:
      - data-model acceleration or tstats tuning
      - HEC, transport, index routing, or source onboarding
      - app installation, Cloud changes, or cluster deployment
      - knowledge-object governance or lifecycle review
      - unrelated SPL, search, or dashboard troubleshooting
    outcomes:
      - cited extraction-mechanism guidance
      - evidence-bound extraction draft
      - semantic CIM mapping plan
      - unverified validation workflow or evidence-based result
      - bounded normalization-gap diagnosis

View original text

---
name: field-extraction-and-cim-mapping
description: Author, explain, diagnose, and validate Splunk search-time field extractions and mappings to Common Information Model (CIM) datasets from representative events, configuration, and search evidence. Use for automatic key-value extraction, regex or delimiter extraction, props.conf EXTRACT and REPORT/transforms.conf rules, SPL extraction commands, aliases, calculated fields, lookups, event types, tags, value normalization, CIM field mapping, and missing or incorrect normalization; do not use for deployment execution, ingestion transport, app installation, knowledge-object governance, data-model acceleration, or unrelated search/dashboard repair.
license: Apache-2.0
allowed-tools:
- web
metadata:
splunk:
domain: data-normalization
products:
- splunk-cloud-platform
- splunk-enterprise
entities:
- search-time field extractions
- props.conf and transforms.conf
- field aliases, calculated fields, and lookups
- event types and tags
- CIM datasets and normalized fields
triggers:
- extract fields from sample events
- write EXTRACT or REPORT rules
- map fields to CIM
- validate CIM normalization
- diagnose missing fields, aliases, lookups, tags, or event types
not-for:
- data-model acceleration or tstats tuning
- HEC, transport, index routing, or source onboarding
- app installation, Cloud changes, or cluster deployment
- knowledge-object governance or lifecycle review
- unrelated SPL, search, or dashboard troubleshooting
outcomes:
- cited extraction-mechanism guidance
- evidence-bound extraction draft
- semantic CIM mapping plan
- unverified validation workflow or evidence-based result
- bounded normalization-gap diagnosis
---

# Field Extraction and CIM Mapping

Produce documentation-backed, evidence-bound guidance for search-time field
extraction and CIM normalization. Draft text artifacts only. Never authenticate
to, modify, install on, or deploy to a Splunk environment.

## Prerequisites

Record or ask for only the missing items material to the requested decision:

- representative sanitized raw events, including meaningful edge cases;
- sourcetype and relevant current `props.conf`, `transforms.conf`, SPL, aliases,
calculated fields, lookups, event types, and tags;
- desired source fields and their meanings;
- target CIM dataset and installed CIM version or model details;
- Splunk product/version, persistence scope, app context, and deployment
topology when configuration or routing depends on them; and
- observed field/search/data-model validation output when diagnosing or
claiming validation.

Never invent event structure or field meaning. With partial evidence, first
preserve and assess every supported object-level fact. Mark each absent field
or artifact `unknown`, and gate only the conclusion it affects; missing context
must not erase supplied evidence. Clearly separate supplied observations,
documented facts, assumptions, provisional conclusions, and unverified steps.

## When to Use

Use this skill when the primary outcome is documented extraction guidance, an
evidence-bound extraction draft, a semantic CIM mapping, validation steps, or a
normalization-gap diagnosis. Apply the boundaries below to adjacent work.

## Workflow Overview

### 1. Select the extraction mechanism

Read [public-guidance.md](references/public-guidance.md). Identify the documented
mechanism that fits the event shape: automatic key-value extraction, regex or
delimiter extraction, inline `EXTRACT`, reusable `REPORT` plus
`transforms.conf`, or an ad hoc SPL command. Separate persistent knowledge
objects from search-local SPL. Treat index-time extraction as a
performance-sensitive exception, not the default.

If source events or deployment context are absent, give only cited general
guidance. End the answer with a direct request for representative events,
sourcetype, product and version, and desired persistence scope before proposing
concrete configuration; merely listing these inputs as absent does not satisfy
the request.

### 2. Draft an evidence-bound extraction

Use the smallest approach that targets the supplied samples. Provide concrete
`props.conf`, `transforms.conf`, or SPL only when representative events and
desired fields are supplied. Explain why it matches the event shape and state
assumptions about sourcetype, app context, delimiters, cardinality, multivalue
behavior, and persistent search-time versus ad hoc scope.

Flag broad unnecessary key-value extraction, unbounded variable-key expansion,
duplicated calculated-field logic, brittle sample-only matching, and premature
index-time extraction. If evidence is insufficient, ask for the missing sample
and desired fields or give only a clearly marked, unvalidated template/checklist.

### 3. Build a semantic CIM mapping plan

Name the target CIM dataset; if event meaning permits several candidates, ask
the user to choose and explain the candidates provisionally. Map by source-field
semantics, not name similarity. Where evidence permits, distinguish required
fields, recommended or expected fields, required tags, dataset constraints,
aliases, calculated fields, lookups, event types, enrichment, and value
normalization.

If the target dataset, source semantics, or installed model details are
missing, preserve any supported field facts, label candidate mappings
provisional, and request the smallest missing evidence. Direct the user to the
Data Model Editor or installed model JSON for complete constraints and inherited
fields; do not claim the public reference tables are complete.

### 4. Define or assess validation

Provide the relevant inspection path: normal search field inspection,
Pivot/Datasets, `datamodel` or `from datamodel`, `datamodelsimple`, or the CIM
Validation data model's Missing Extractions and Untagged Events datasets. State
the expected success evidence: correct values appear, required tags/event types
select the intended events, and edge cases preserve extraction behavior.

Call runtime validation `unverified` unless actual search or deployment results
are supplied. When results are absent, provide only commands and expected
observations. When results are present, assess only what they demonstrate.

Keep a validation-only answer bounded. Use one short status statement, at most
five focused validation steps, and one compact expected-evidence checklist.
Prefer one representative search per distinct validation purpose instead of
enumerating variants. Do not repeat prerequisites, boundaries, citations, or
the same caveat in multiple sections. A validation-only answer must be 900
words or fewer. If the user requests a longer runbook, first return a complete
validation answer within this limit; provide the longer runbook only afterward.

### 5. Diagnose normalization gaps

Connect every suspected missing or incorrect extraction, alias, lookup, tag,
event type, calculated field, or CIM mapping to a supplied raw event,
configuration fragment, field output, or data-model validation result. Preserve
confirmed facts even when other artifacts are absent.

Separate semantic/configuration explanations from possible deployment,
app-installation, cluster-bundle, managed Cloud change, or acceleration causes.
Provide a bounded semantic remediation plan and the exact evidence needed to
confirm it. If only symptoms are supplied, do not diagnose: ask for the smallest
safe subset of representative events, relevant current configuration, search
output, target dataset, product/version, and topology needed for the pending
decision.

## Boundaries

Keep extraction authoring and CIM-mapping semantics here. Route only work that
crosses the boundary:

- governance, ownership, naming, packaging policy, and lifecycle decisions to
Knowledge Object Governance;
- source onboarding transport, HEC, tokens, and index routing to the relevant
ingestion owner;
- acceleration design, `tstats` tuning, summaries, and acceleration failures to
Data Model and Search Acceleration;
- unrelated search/dashboard remediation to its troubleshooting owner; and
- app installation, managed Cloud changes, cluster bundles, approvals, and
production deployment to the appropriate Splunk operator.

Do not claim runtime verification, publication readiness, prevalence,
cross-system linkage, telemetry baselines, or rollback readiness without direct
evidence.

## Examples

- “Choose a persistent extraction for these sanitized events and fields.”
- “Map these existing fields to the Authentication CIM dataset.”
- “Assess these Missing Extractions results against the supplied config.”

## Troubleshooting

- No samples: provide cited mechanism guidance or an unvalidated template, then
request representative events and desired fields.
- Partial artifacts: retain every supported fact, mark only missing facts
unknown, and gate only the affected mapping or diagnosis.
- No runtime results: provide validation commands and expected observations;
label validation unverified.
- Operational cause remains possible: separate it from the semantic finding and
route only the operational action that crosses the boundary.

## Final-answer contract

Before returning, verify:

- Put a point-of-use public citation beside every decisive
documentation-backed action or claim.
- Before evidence-dependent diagnosis, request the smallest safe evidence set;
preserve every supported object-level fact and let absent fields limit only
the affected conclusion.
- State assumptions, expected success evidence, and what remains provisional or
unverified.
- Name an owner or route only when the answer crosses this skill's boundary;
otherwise state that the answer remains within bounded field-extraction and
CIM-mapping scope.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: Apache-2.0

Permission surface may require sandboxing
No critical issues identified. The skill is advisory-only, restricts tool use to web, and explicitly prohibits authentication, modification, installation, or deployment to Splunk environments.
SKILL.md does not include a full worked example, but this is a minor completeness opportunity rather than a functional defect.
Low GitHub adoption signal
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 31 GitHub stars
Stars/forks activity: 31 stars, 7 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, shell or command execution

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1Read the source. Confirm the input, expected output, dependencies and permissions.
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Source & usage notes

IndexedAI Reviewed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: splunk/splunk-agent-skills
License: Apache-2.0
Version: Unknown
Last GitHub push: Sep 11, 2026
Registry updated: Sep 12, 2026
Instruction path: skills/field-extraction-and-cim-mapping/SKILL.md @ 488d7c76d9f2

Version reported in registry metadata; check source releases before relying on it.

Quality

62/100

Promising

Trust

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This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

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More details

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    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Permission surface may require sandboxing",
      "No critical issues identified. The skill is advisory-only, restricts tool use to web, and explicitly prohibits authentication, modification, installation, or deployment to Splunk environments.",
      "SKILL.md does not include a full worked example, but this is a minor completeness opportunity rather than a functional defect.",
      "Low GitHub adoption signal",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 31 GitHub stars",
      "Stars/forks activity: 31 stars, 7 forks; issue activity unavailable in current metadata"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 62,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "7d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "No critical issues identified. The skill is advisory-only, restricts tool use to web, and explicitly prohibits authentication, modification, installation, or deployment to Splunk environments.",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Permission surface may require sandboxing",
    "SKILL.md does not include a full worked example, but this is a minor completeness opportunity rather than a functional defect."
  ],
  "agent_contract": {
    "task_input": "Use field-extraction-and-cim-mapping in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 68/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 39/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "splunk-field-extraction-and-cim-mapping (field-extraction-and-cim-mapping)",
      "install_command": "npx skills add splunk/splunk-agent-skills --skill field-extraction-and-cim-mapping",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "splunk-field-extraction-and-cim-mapping",
      "task": "Use field-extraction-and-cim-mapping in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/splunk-field-extraction-and-cim-mapping",
    "api": "https://www.openagentskill.com/api/agent/skills/splunk-field-extraction-and-cim-mapping",
    "audit": "https://www.openagentskill.com/skills/splunk-field-extraction-and-cim-mapping/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=splunk-field-extraction-and-cim-mapping&task=Use%20field-extraction-and-cim-mapping%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20field-extraction-and-cim-mapping%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20field-extraction-and-cim-mapping%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/splunk-field-extraction-and-cim-mapping/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/splunk-field-extraction-and-cim-mapping"
  }
}

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Creator: splunk
Source: splunk/splunk-agent-skills
Indexed by: OpenAgentSkill community index

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