Selects optimal sources for tool calls, balancing accuracy with token cost. Use before research tasks or when deciding whether a claim needs verification.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Selects optimal sources for tool calls, balancing accuracy with token cost. Use before research tasks or when deciding whether a claim needs verification.
Intelligent sourcing that requires citations only when the cost is justified by the value of verification.
Philosophy
Full sourcing is prohibitively expensive (10-16x token increase). Smart sourcing targets high-value claims where verification materially improves accuracy.
When to Source
REQUIRE Sources
Claim Type
Example
Why Source
Version numbers
"Python 3.12 added..."
Versions change, easy to verify
Performance claims
"30% faster than..."
Quantitative claims need evidence
Security recommendations
"Use bcrypt for..."
Security advice must be current
API specifications
"The function accepts..."
APIs change between versions
Release dates
"Released in Q4 2025"
Factual, verifiable
Pricing/limits
"Free tier allows 1000 requests"
Business terms change
Deprecated features
"X was removed in version Y"
Breaking changes need verification
DO NOT Require Sources
Claim Type
Example
Why No Source
General concepts
"Async improves concurrency"
Foundational knowledge
Code examples
Demonstrative snippets
Illustrative, not factual claims
Opinion/preference
"Consider using..."
Clearly framed as suggestion
Common knowledge
"Git tracks changes"
Universal understanding
Logical derivations
"Therefore, X implies Y"
Reasoning, not factual claim
Syntax
"Use def for functions"
Language documentation is stable
Decision Framework
Is this a factual claim?
├── No → No source needed
└── Yes → Would being wrong cause harm?
├── No (minor inconvenience) → No source needed
└── Yes → Is verification cost-effective?
├── No (>5k tokens for low-value claim) → State uncertainty
└── Yes → Perform web search, cite source
Implementation
Inline Citations
When sourcing is warranted:
Python 3.12 introduced `type` statement for type aliases[1].
Sources:
1. [PEP 695](https://peps.python.org/pep-0695/)
Uncertainty Markers
When sourcing isn't cost-effective but uncertainty exists:
Python 3.12 introduced the `type` statement (verify in release notes for your version).
Confidence Levels
Confidence
Action
High (stable facts)
State directly
Medium (may have changed)
Add verification note
Low (uncertain)
Source or acknowledge uncertainty
Token Cost Analysis
Approach
Token Cost
Use Case
No sourcing
Baseline
General knowledge
Uncertainty marker
+5-10 tokens
Medium confidence
Single source
+2-5k tokens
High-value claim
Multiple sources
+10-20k tokens
Research tasks
Examples
Good: Source Version-Specific Claim
User: What's new in FastAPI 0.109?
Response: FastAPI 0.109 added Pydantic v2 support as default[1] and
improved OpenAPI schema generation[2].
Sources:
1. [FastAPI Release Notes](https://fastapi.tiangolo.com/release-notes/)
2. [GitHub Release 0.109.0](https://github.com/tiangolo/fastapi/releases/tag/0.109.0)
Good: No Source for General Concept
User: How does async/await work?
Response: Async/await enables non-blocking I/O by allowing coroutines
to yield control while waiting for operations. The event loop manages
execution, resuming coroutines when their awaited operations complete.
Good: Uncertainty Marker
The connection pool default is typically 10 connections (verify in your
database driver documentation as this varies by library and version).
conserve:token-conservation - Weighs source cost vs value
memory-palace:knowledge-intake - Full sourcing for knowledge corpus
When To Use Full Sourcing
When NOT To Use
Internal project code that doesn't need citations
Casual conversation without factual claims
Escalate to full sourcing (accept high token cost) for:
Knowledge corpus entries (permanent documentation)
Security advisories (safety-critical)
Compliance/legal claims (audit requirements)
Research tasks (user expects thorough investigation)
For these cases, use memory-palace:knowledge-intake workflow which is designed for thorough sourcing.
Exit Criteria
Every claim categorized as requiring a source (version numbers,
performance claims, security recommendations, API specs, release
dates, pricing/limits, deprecated features) has an inline
citation or a verification note
No web search performed for claims categorized as general
concepts, code examples, opinions, common knowledge, logical
derivations, or stable syntax
Claims with medium confidence carry an explicit uncertainty
marker (e.g., "verify in release notes for your version") rather
than being stated as fact or sourced at high token cost
Decision tree applied: factual claim → harm if wrong →
verification cost-effective → source; each branch followed
explicitly for claims exceeding medium confidence
