Developer Find Anywhere

Developer find-anywhere

Usage & discovery (§C3 contract)

For "where is X used / who references X / what depends on X" — for ANY component type — call sfi.run_analysis with { "name": "sfi.find_component_usages", "args": { … } }, or the family specialist (find_field_anywhere for a field, find_code_usages for code, layout_assignments for a layout). Route by VERB: describe questions (what is / list / what values) use describe tools, NOT usage tools. Never improvise a multi-tool fan-out without citing evidence tiers (graph edge confidence + grep text-match). An empty result is "no static evidence in the vault" — NEVER "nothing uses this".

Overview

This skill is the developer persona's companion to v2.2's universal- search family and v2.9's vocabulary + semantic-disambiguation tier. The developer's first-line question is one of two shapes:

1. "Where is X used?" — for a known component id (Industry__c), the answer is the structured inventory of every referrer across every code + declarative tier. sfi.find_field_anywhere is the single-call surface.
2. "Do we have a field for X?" — for a natural-language concept, the answer is the ranked list of best-matching CustomField nodes. sfi.find_semantic_field is the lexical similarity surface; sfi.field_meaning is the per-field deep dive once the candidate is identified.

Plus four secondary shapes the v2.9 vocabulary tier addresses:

3. "What does X mean in our org?" — sfi.field_meaning surfaces label, description, type, picklist values, the v2.9 sourceOfTruth and semanticCategory classifications, usage frequency, and similar fields. Org-vocabulary drift is the honesty anchor.
4. "Is Status the same as Stage here?" — sfi.disambiguate_concepts partitions fields matching each concept and computes distinguishing axes (parent-object distribution, declared types, picklist-values, usage). The Q155 honesty anchor: vocabulary is org-specific.
5. "Is this field manually entered or automated?" — sfi.field_provenance surfaces the v2.9 sourceOfTruth classification + structural trace (formula? auto-number? Apex writers? Flow writers? Trigger writers?).
6. "Find this literal / hardcoded value anywhere." — sfi.find_hardcoded_values_anywhere is the cross-corpus literal search (Apex string literals + Flow XML + metadata XML + formula expressions + Custom Metadata records).

The boundary that matters for developers: v2.2's semantic field search is lexical, not neural. TF-IDF cosine similarity over tokenized apiName + label + description. A field named Customer_Industry__c will rank highly for the query "customer health" because they share the customer token — even though the semantic meaning differs. The Q95 honesty anchor surfaces this in every result.

The v2.9 classifier-not-run boundary: sourceOfTruth and semanticCategory are populated by the v2.9 R3 extraction pass. A vault refreshed before v2.9 returns unknown for both with the verbatim "classification not run" disclosure. The tool still emits its structural surface (the writer trace, the similar-field overlap, the usage counts) so the developer can answer the question manually.

When to fire

Fire this skill on discovery / lookup / vocabulary phrasing. Concrete triggers:

• "Find every reference to / where is X used / find X anywhere." — "find every reference to Industry__c", "where is Account.Tier__c used anywhere", "find anywhere we touch this field". Use sfi.find_field_anywhere.
• "Do we have a field for / is there a field for / show me fields about." — "do we have a field for customer health", "is there a field for renewal date", "show me fields about payment terms". Use sfi.find_semantic_field.
• "What does X mean in our org / what is Tier__c." — "what does Industry__c mean", "what is Account.Tier__c", "explain this field". Use sfi.field_meaning.
• "Is Status the same as Stage / what's the difference between X and Y / how do X and Y differ." — "is Status the same as Stage", "what's the difference between Customer_Health__c and Customer_Score__c". Use sfi.disambiguate_concepts.
• "Is this field automated / where does this data come from." — "is Account.Tier__c manually entered", "what writes this field", "is this auto-populated". Use sfi.field_provenance.
• "Find this literal anywhere / find this string in metadata." — "find 'sandbox@example.com' anywhere", "where does this URL appear", "find this constant in the metadata". Use sfi.find_hardcoded_values_anywhere.
• "What does this acronym mean / decode this field name." — "what does SLA__c stand for", "decode MQL_Score__c". This is a vocabulary question; defer to sfi.field_meaning (which surfaces the description) AND to sfi.get_naming_convention_report (which surfaces the org's pattern conventions via the recognizing-naming-conventions skill).

When NOT to fire

Defer to another skill when:

• The user asks "what breaks if I change X?" That's impact analysis. Defer to architect-impact-analysis → sfi.get_impact.
• The user asks "is this code dead?" That's a hygiene question. Defer to developer-code-quality → sfi.find_dead_code.
• The user asks "what tests cover this method?" That's reachability. Defer to developer-impact-and-reachability → sfi.test_coverage_for_method.
• The user asks "where is this used in Apex?" When the question is strictly Apex-scoped, developer-apex-refactor → sfi.find_code_usages is the right call. sfi.find_field_anywhere is the broader sweep across every edge type and every node type.
• The user asks for a schema dump ("what fields does Account have?"). That's answering-org-questions → sfi.list_components / sfi.get_component.
• The user wants live data ("how many records have Industry='Tech'"). v2.2 is offline.
• The user wants the recognizer to FIX a hardcoded value. v2.2 is read-only; it surfaces matches but never proposes a rewrite.

The cascade

The six tools split by user intent. Pick the right entry point.

1. sfi.find_field_anywhere — known-field universal sweep

The v2.2 Q86-style discovery surface. Given a CustomField: id, it walks every incoming edge regardless of type, resolves each referrer to a node, and emits one entry per (referrer, edgeType) pair grouped by the referrer's ComponentType. Surfaces Apex reads/writes, Apex calls, Flow record-lookup/create/update, Layout placements, formula references, ValidationRule references, SharingRule criteria references, WorkflowRule references, ApprovalProcess entry criteria — the whole inventory.

Default invocation:

{ "fieldId": "CustomField:Account.Industry__c" }

Fire this when the user names the specific field and asks where it's touched. Group the response by ComponentType (Apex first, then Flow, then Layouts, then declarative-rule referrers). Within each group, distinguish readsFrom from writesTo so the developer sees reads and writes separately.

For Apex-only or LWC-only narrowing, defer to developer-apex-refactor → sfi.find_code_usages with nodeTypes.

2. sfi.find_semantic_field — natural-language field discovery

The v2.2 "do we already have a field for X?" surface. Takes a natural-language description and returns CustomField nodes whose tokenized apiName + label + description has the highest token- overlap score against the query.

Default invocation:

{ "query": "customer health score", "limit": 10, "minScore": 0.1 }

The matching is Jaccard-style token overlap (or TF-IDF cosine similarity when the v2.2 R4 search-index is built — the tool auto-detects which). The minScore default of 0.1 filters single-token matches that are too noisy to surface.

Every result carries confidence: 'heuristic' and a matchedTokens array — the tokens that appear in BOTH the query and the field's token bag. The matched-tokens array IS the user- facing explanation for "why was this field ranked highly" and the Q95 honesty surface (the user sees that Customer_Industry__c matched on customer alone and can distinguish it from Customer_Health_Score__c which matched on customer + health

• score).

Fire this when the user asks "do we have a field for X" or similar discovery phrasing. After surfacing the top candidates, offer to deep-dive on the most likely match via sfi.field_meaning.

3. sfi.field_meaning — per-field deep dive

The v2.9 R4 "what does this field actually mean?" surface. Given a CustomField id, returns:

• Declared shape — apiName, label, description, type, parent object, picklist values (when present), required flag.
• v2.9 vocabulary classifications — sourceOfTruth (manual | derived | integration-synced | manual-and-coded | unknown, with declared/heuristic confidence) and semanticCategory (identifier | status | amount | date | reference | descriptor | unknown, always heuristic confidence).
• Usage frequency — asymmetric read vs write EDGE counts. incomingReads counts every inbound edge that CONSUMES the field's value: readsFrom (Apex, Flow, condition contexts) and references (formulas, validation rules, list views, report types, Lightning pages, quick actions, web links). It is an edge count, not a referrer count, so it legitimately differs from find_formula_references's totalCount. readsByEdgeType gives the per-type breakdown, countedEdgeTypes names the vocabulary, and excludedByEdgeType publishes the inbound edges seen and rejected (usedInLayout is placement, grantedBy is permission, parentOf is structure). Quote the verbatim usageFrequency.note. High writes + low reads suggests a scratch/integration-output pattern — but incomingReads: 0 is NOT proof of disuse, and the tool attaches a boundary saying so. Never render a zero here as "nobody reads this field"; render it as "no consuming edge is modelled in this vault".
• Top-3 similar fields — by label/apiName token overlap.

Default invocation:

{ "fieldId": "CustomField:Account.Customer_Health_Score__c" }

Fire this when the user asks "what is X" or "what does X mean" for a specific field id. Use the v2.9 classification + writer count + similar-fields list to give the developer the org-context answer their question deserves.

4. sfi.disambiguate_concepts — two-concept comparison

The v2.9 R4 "is Status the same as Stage here?" surface. Takes two concept tokens and returns per-concept matching-field buckets, per-axis differences (parent-object distribution, declared types, picklist-values, usage-pattern), and an optional suggestedWhenToUseEach inference.

Default invocation:

{ "conceptA": "Status", "conceptB": "Stage", "limit": 50 }

Fire this when the user asks for a comparison of two org-specific vocabulary tokens. The Q155 honesty anchor is verbatim in boundaries[]: "Vocabulary is org-specific — one org's Status is another org's Stage." Always surface this disclosure.

When conceptA === conceptB (case-insensitive trimmed), the tool returns identical buckets with empty differences. The skill refuses to fabricate a distinction; surface the Q150 refusal pattern ("the two concepts you named are the same token — no distinction to surface").

5. sfi.field_provenance — writer trace and source classification

The v2.9 R4 "is this field manually entered or automated?" surface. Given a CustomField id, returns:

• v2.9 sourceOfTruth classification + confidence.
• declaredAsFormula (formula expression when present).
• declaredAsAutoNumber (displayFormat when auto-number).
• ALL apexWriters (with isIntegrationTagged flag from v2.0a references edges to NamedCredential / ExternalDataSource).
• ALL flowWriters.
• ALL triggerWriters (ApexTrigger nodes).
• noWritersDetected boolean.

Default invocation:

{ "fieldId": "CustomField:Account.Tier__c" }

The tool lists EVERY writer, not just the ones used in the classification cascade. Callers verify the classification's basis by reading the writers list directly. Fire this when the user asks where the field's data comes from or whether it's auto-populated.

6. sfi.find_hardcoded_values_anywhere — cross-corpus literal search

The v2.2 universal literal-search surface. Searches Apex string literals (NO-STRIP pass), Flow XML <value> elements, metadata XML files (.field-meta.xml, .validationRule-meta.xml, etc.), formula expressions, and Custom Metadata records for either:

• A specific value (exact match, case-sensitive default).
• A category pattern (id / email / username / date / numeric).

Default invocation (exact value):

{ "value": "admin@example.com" }

Default invocation (category pattern):

{ "category": "id", "limit": 100 }

Fire this when the user asks for a literal across corpora. Note the distinction from sfi.find_hardcoded_values (v2.1, Apex-only, recognizer-driven): the v2.2 tool searches every corpus and emits matches at confidence: declared for exact-value mode (the source literally contains the string) and confidence: heuristic for category-pattern mode.

The numeric category has very high false-positive rate (loop counters, array indices, arithmetic constants); the tool's boundaries[] surfaces this verbatim. Suppress numeric results by default unless the user explicitly asks.

Honesty axes

v2.2 universal (every tool's boundaries[])

• Dynamic SOQL invisible. Database.query('SELECT...') is stripped before pattern passes; the embedded SQL is invisible. Reflective field access (obj.get('FieldName')) is invisible.
• Custom utility methods invisible. A search for Messaging.sendEmail will not find calls to a MyEmailHelper.send(...) wrapper unless the helper is also searched.
• Managed-package opacity. Results from managed packages are limited to metadata XML; Apex source within managed packages is not indexed. If the operation lives inside a managed-package class, search will not find it.

sfi.find_semantic_field — Q95 honesty anchor (verbatim)

This is a similarity-ranked recommendation based on overlapping tokens in the field's apiName, label, and description; it is not a declared match. A field named Customer_Industry__c will rank highly for the query customer health because they share the token customer. Verify the returned field's label and description before treating as the answer.

Additional v2.2 disclosures the skill surfaces:

• No synonym handling. The similarity ranking treats customer health and client wellness as completely unrelated queries — there is no synonym dictionary, no spelling correction, and no semantic understanding. If your org uses domain-specific terminology different from the standard Salesforce vocabulary, the search may miss relevant fields.
• Description-tokenization caveat. Field descriptions are tokenized as bags of words; a description that contains "this field is NOT the customer health score; see X instead" will still match a "customer health score" query because the tokens are present. Read the surfaced descriptions to disambiguate.

sfi.find_field_anywhere — graph-coverage honesty

• Sparse-graph misses. The tool walks listEdges on the field's incoming edges; referrer nodes missing from the graph are silently dropped. If a referrer exists on disk but wasn't extracted (managed package, unsupported metadata type), it won't surface here.
• Cross-corpus consistency. Apex reads/writes are predominantly confidence: parsed (the default-on Apex AST pass, source: apex-ast), dropping to heuristic only on the recall scanner's backfill edges (files the AST could not parse, or unresolved patterns); Flow record-ops are parsed; layout placements and validation references are declared. Confidence is per-edge — state it per bucket, and don't assume Apex means heuristic.

v2.9 vocabulary tools — classifier-not-run boundary (verbatim)

The v2.9 sourceOfTruth and semanticCategory classifications are populated by the v2.9 R3 extraction pass. If your vault was last refreshed before v2.9 shipped, both fields will be unknown and this boundary surfaces. The structural surface (writers list, declared shape, similar fields, usage frequency) remains intact regardless — you can answer the underlying question manually from the trace.

Additional v2.9 disclosures the skill surfaces:

• Vocabulary is org-specific (Q155 anchor). One org's Status is another org's Stage; the tool reports what THIS org's metadata declares, not industry convention. Always surface for sfi.disambiguate_concepts.
• Classification is heuristic on writes-fabric inference. The sourceOfTruth classifier reads the field's writer set and infers a category. A field with a single integration writer AND a Flow writer is classified manual-and-coded even though the developer's intent might be "integration with override". When confidence is heuristic, surface the disclosure.
• Semantic category is name-pattern, not type-semantic. The semanticCategory classifier matches the field's apiName / label against ~25 keyword families (status, stage, owner, etc.). A field named Customer_Status_Code__c matches status even if the type is Number. Always surface when semanticCategory !== 'unknown'.
• Usage frequency is static analysis only. Incoming readsFrom / writesTo edges count code/declarative references, not runtime invocations. A field referenced by a trigger that fires once a year and a field referenced by a trigger that fires every save have the same readsFromCount.

sfi.find_hardcoded_values_anywhere — corpus honesty

• Numeric category high FP. Suppress by default; opt in only when looking for specific hardcoded numbers.
• Test-class context. Matches in @isTest-annotated classes may be intentional test fixtures.
• Category-pattern matches are heuristic; exact-value matches are declared. State the distinction in the response.

Worked example

User: "Do we already have a field for customer health score in our org?"

Claude's flow:

1. Classify → natural-language field discovery. Fire sfi.find_semantic_field.
2. Fire with { "query": "customer health score", "limit": 10 }.
3. Receive (illustrative):

{
  "data": {
    "matches": [
      { "fieldId": "CustomField:Account.Customer_Health_Score__c", "score": 0.78, "matchedTokens": ["customer", "health", "score"], "label": "Customer Health Score", "description": "A 0-100 score representing overall customer engagement; computed nightly by HealthScoreJob.", "confidence": "heuristic" },
      { "fieldId": "CustomField:Account.Customer_Industry__c", "score": 0.21, "matchedTokens": ["customer"], "label": "Customer Industry", "description": "Industry segment for the customer's parent company.", "confidence": "heuristic" },
      { "fieldId": "CustomField:Opportunity.Customer_Score__c", "score": 0.45, "matchedTokens": ["customer", "score"], "label": "Customer Score", "description": "Legacy field; do not use. Replaced by Account.Customer_Health_Score__c.", "confidence": "heuristic" }
    ],
    "boundaries": ["This is a similarity-ranked recommendation based on overlapping tokens... [Q95 verbatim]"]
  }
}

4. Respond:

Three candidate fields for "customer health score" (heuristic token-overlap ranking; v2.2 is lexical, not neural):

Best match (score 0.78)

• CustomField:Account.Customer_Health_Score__c — Customer Health Score. Matched on customer, health, score. Description: "A 0-100 score representing overall customer engagement; computed nightly by HealthScoreJob."

Partial matches

• CustomField:Opportunity.Customer_Score__c (score 0.45) — Customer Score. Matched on customer, score. Description flags it as legacy ("do not use; replaced by Account.Customer_Health_Score__c"). Don't pick this.
• CustomField:Account.Customer_Industry__c (score 0.21) — Customer Industry. Matched on customer ONLY. Surfaced because of the token overlap; the actual semantic is different.

The best match is Account.Customer_Health_Score__c — its description matches your query, the matched-tokens list includes all three of yours, and the legacy field's description explicitly redirects there.

Important caveats (v2.2 honesty anchor):

• This ranking is lexical token overlap; "customer health" and "client wellness" would be treated as unrelated. If your org has alternative phrasing (e.g., a "wellness" or "engagement" variant), they won't surface in a "customer health" query.
• The matched-tokens list is your verification axis. Customer_ Industry__c matched on customer ALONE — its semantic meaning differs from yours despite the rank.

Next steps:

• For a deeper look at the best match (description, writers, v2.9 source-of-truth classification, similar fields), run sfi.field_meaning on CustomField:Account.Customer_Health_Score__c.
• For "where is Customer_Health_Score__c used anywhere in the org?" — every Apex / Flow / Layout / Validation / Workflow referrer in one call — run sfi.run_analysis with { "name": "sfi.find_field_anywhere", "args": { … } }.
• To distinguish Customer_Health_Score__c from Customer_Score__c (the legacy field), run sfi.disambiguate_concepts with conceptA: 'Customer Health Score' and conceptB: 'Customer Score'.

The response surfaces the matched-tokens array (the Q95 anchor's verification surface), names the legacy field's intent explicitly, and offers three concrete next-step tool calls.

Anti-patterns

See also

• developer-apex-refactor — for strictly Apex/LWC/Aura/VF code reference questions. Narrower scope than find_field_anywhere; picks the right scanner-tier when the user pins to a single source surface.
• developer-code-quality — for hardcoded-value scanning inside Apex (sfi.find_hardcoded_values — recognizer-driven, Apex-only) vs the v2.2 cross-corpus tool here.
• architect-impact-analysis — for "what breaks if I delete this field" cross-component impact analysis.
• recognizing-naming-conventions — for org-vocabulary pattern detection ("our convention is Status__c, not State__c"). Vocabulary tools here are per-field; convention recognition is org-wide.
• developer-field-deep-dive — for the full v3.0 synthesis (sfi.field_360, sfi.field_lineage) once a field is picked.

Verification

Before sending a response, confirm:

• I classified the question into one of the six shapes (known- field universal, natural-language discovery, per-field meaning, two-concept disambiguation, writer trace, cross-corpus literal) and fired the right tool.
• For find_semantic_field results, I surfaced the matchedTokens array verbatim per result so the user can verify which tokens matched.
• For find_field_anywhere results, I grouped by ComponentType and within each group distinguished readsFrom from writesTo. I cited the per-bucket confidence.
• For field_meaning / field_provenance / disambiguate_concepts, I surfaced the v2.9 vocabulary disclosures verbatim (org-specific, heuristic classifier, name-pattern semantic category) when relevant.
• For disambiguate_concepts with identical concepts, I surfaced the Q150 refusal pattern and did NOT fabricate a distinction.
• For find_hardcoded_values_anywhere numeric category, I suppressed or warned about the FP rate.
• I cited every canonical id (CustomField:, ApexClass:, etc.) in backticks.
• I did NOT present a confidence: heuristic result as ground truth or recommend an action without naming the verification step.
• When the v2.9 classification is unknown, I surfaced the classifier-not-run boundary and still gave the structural trace answer.

Grounding & routing (shared contract). For a vague or broad ask, call sfi.route_question first — in the default hybrid mode it returns a meaning-ranked toolCandidates shortlist (which YOU pick from) plus a suggested plane and a route hint (and whether to sfi.resolve a name first). Default tool profile is core: only the core spine (including sfi.live_consent) is directly invokable. For every other sfi.* analysis, call sfi.run_analysis with { "name": "sfi.<tool>", "args": { … } } (or follow route_question.invoke, which already wraps non-core steps). Optional: sfi.describe_analysis first when args are unclear. Every org fact must come from an sfi.* tool call, cited by its canonical id — never from memory. Build the answer only from what the tools returned, then pass it through sfi.synthesize_answer, which flags any hallucinatedIds (canonical ids no tool produced). Full cascade: using-sf-intelligence.