Turn an executive or operator's private reading, highlights, notes, social bookmarks, and applied work into source-grounded decision intelligence. Use when detecting attention drift, surfacing pre-decision signals, testing contradictions, converting bookmarks into builds, recombining founder IP, finding service-offer arbitrage, or mapping content negative space.
Turn an executive or operator's private reading, highlights, notes, social bookmarks, and applied work into source-grounded decision intelligence. Use when detecting attention drift, surfacing pre-decision signals, testing contradictions, converting bookmarks into builds, recombining founder IP, finding service-offer arbitrage, or mapping content negative space.
Personal Strategic Signal Intelligence
Overview
Personal Strategic Signal Intelligence (PSSI) converts a person's accumulated information trail into decision support. It is not a content-idea generator with a bookmark import attached. Its first job is to clarify what the operator is noticing, testing, doubting, and becoming ready to decide. Content, products, offers, and experiments are downstream applications.
The system may read from multiple personal-signal sources, including:
read-later libraries
browser or knowledge-base saves
social bookmarks
explicitly completed reads
highlights and annotations
personal notes and memos
meeting follow-ups and decision records
experiments, prototypes, and shipped applications
user-supplied public writing or prior decisions
Treat every source as evidence with a known strength, date, and lineage. Never present an inferred belief as a declared fact.
When to Use
Use this skill when the user asks to:
understand how their attention or strategic interests are changing
identify decisions they appear to be approaching
compare stated beliefs with recent evidence or behavior
turn saved material into an experiment, prototype, or operating change
recombine existing frameworks into defensible founder or executive IP
discover offers or services implied by recurring market pain
find important topics they study but have not addressed publicly
run a weekly personal intelligence review
trigger a focused review after a major decision, cluster of saves, contradiction, or applied experiment
Do not use it to:
diagnose personality, mental health, or private motives
summarize an entire archive without a decision question
rank people, clients, or employees from private activity
publish inferred beliefs or sensitive source material
treat bookmarks, likes, or follows as endorsements
create an arbitrary high-frequency synthesis loop
Operating Principles
1. Decision intelligence before content
Always ask what decision, allocation, belief update, risk, or experiment the signal may inform. Only after that should the system propose content. If no decision relevance is found, label the output as exploratory rather than forcing a business implication.
2. Attention is not conviction
A save is evidence of attention, not agreement. Repeated saves can indicate curiosity, anxiety, active research, competitive monitoring, or disagreement.
Use this signal-strength ladder by default:
Signal
Default interpretation
Relative strength
Save, like, follow, or bookmark
Weak attention signal
1
Repeat saves across time or sources
Sustained attention
2
Explicitly marked as read
Deliberate exposure
3
Highlight or annotation
Salient idea
4
Original note or synthesis
Active interpretation
5
Decision reference or stated belief
Expressed conviction
6
Experiment, prototype, purchase, or operating change
Applied conviction
7
Repeated application with measured outcome
Validated operating belief
8
Weights are configurable. Never convert a weak signal into a strong claim merely because many weak signals exist.
3. Private inferred beliefs stay private
The system may infer candidate beliefs to help the user think. Each inference must be labeled private inferred belief, include supporting and contradicting evidence, and carry a confidence level. It must not be published, sent to collaborators, or treated as the user's stated position without explicit confirmation.
Use careful language:
Good: "Your recent notes may indicate growing skepticism about broad automation."
Bad: "You believe broad automation is a mistake."
4. Lineage before synthesis
Every material claim must link back to its source records. Preserve original source identifiers internally and show human-readable citations in outputs. If a claim cannot be traced, mark it unverified synthesis or remove it.
5. No recursive evidence
A prior synthesis is not new evidence. Do not cite a dashboard, weekly brief, generated note, or earlier model inference as independent support unless it contains new primary observations.
Maintain these rules:
Primary evidence is a source item, explicit user statement, observed application, or measured outcome.
Derived evidence is a summary, cluster label, score, or inference built from primary evidence.
Derived evidence may help navigation but cannot increase confidence by being counted again.
If a generated artifact is later annotated by the user, only the new annotation is primary evidence; the generated text remains derived.
Keep a derived_from list so cycles can be detected and rejected.
If the user provides no decision question, begin with broad signal detection but do not manufacture urgency.
Step 2: Ingest minimally
Request only fields required for the chosen module. Prefer incremental syncs over full-library exports. Store connector tokens outside notes, prompts, and generated artifacts. Do not include full private documents when a source ID, title, and relevant excerpt are enough.
Step 3: Normalize and deduplicate
Deduplicate by canonical URL, native source ID, and content hash. Preserve multiple engagement events as events on one source rather than pretending they are independent sources. Record edits and deletions when the connector exposes them.
Step 4: Score evidence
Score along separate dimensions:
attention_strength: frequency, recency, diversity of sources
strategic_relevance: connection to active decisions or stated priorities
novelty: difference from already-known themes
contradiction: tension with prior statements or behavior
Never combine attention and conviction into one opaque score. A theme may have high attention and low conviction.
Step 5: Build claims with counter-evidence
For each high-value cluster:
State the observable pattern.
List primary evidence.
List plausible alternative explanations.
Search for contradicting evidence.
Write any belief inference as private and provisional.
Identify the decision, test, or question it informs.
Set confidence based on evidence quality, not rhetorical coherence.
Step 6: Apply the expert panel
Use a configurable panel and pass threshold. The default pass threshold is 90/100. The user may set a different threshold for exploratory work, but the chosen threshold must appear in the output.
Recommended panel lenses:
Lens
Question
Evidence auditor
Are claims traceable to primary evidence without double counting?
Decision strategist
Does this materially improve a real decision?
Contrarian reviewer
What evidence or interpretation would reverse the conclusion?
Operator
Is there a concrete, bounded next action?
Privacy steward
Is the output safe for its intended audience?
Domain expert
Is the analysis credible in the relevant field?
Measurement reviewer
Can the recommendation produce observable feedback?
Score each lens from 0-100, average the scores, and record both the average and threshold. A sub-90 item may still be kept as an exploratory hypothesis, but it must not be promoted as a recommendation under the default configuration.
Step 7: Produce an action artifact
Every promoted insight should end in one of:
a decision question with options and evidence
a falsifiable experiment
a small build specification
a contradiction to resolve
an offer hypothesis to validate
a content gap with a source-grounded point of view
a monitored theme with a clear trigger for re-review
Step 8: Record feedback
Capture what the user accepted, rejected, corrected, applied, or measured. User corrections become new primary evidence. Model restatements do not.
Seven Capability Modules
Module 1: Attention Drift
Purpose: Detect how the operator's attention is changing without confusing attention with belief.
Compare windows by theme, source diversity, recurrence, and engagement depth. Report:
emerging themes
accelerating themes
fading themes
persistent themes
themes moving from saves to notes or application
themes with high attention but no evidence of conviction
Prefer proportions and directional language over exact corpus totals when sharing outside the private workspace.
Module 2: Pre-Decision Oracle
Purpose: Surface decisions the operator may be approaching before they are explicitly framed.
Look for converging signals such as repeated research, opposing viewpoints, implementation notes, vendor comparisons, and applied tests. Output candidate decisions as questions, not predictions:
## Candidate decision**Question:** Should we standardize this workflow now or keep it experimental?
**Why it may be approaching:**<source-groundedpattern>**Evidence for acting:**<citations>**Evidence for waiting:**<citations>**Smallest reversible test:**<action>**Confidence:** medium
Do not claim to know what the user will decide.
Module 3: Contradiction / Decision Court
Purpose: Make productive contradictions visible and force competing hypotheses to face the same evidence.
Use this configurable hybrid entry policy by default:
Explicit, in-scope decisions enter automatically. When the user clearly marks an in-scope item as a decision, add it to Decision Court without requiring a separate nomination approval.
Inferred in-scope candidates require approval. When the system infers that a potentially high-stakes decision is emerging, nominate it with a short source-grounded rationale and wait for the user's approval before running the full Decision Court analysis.
Ordinary activity is not a decision. Do not treat routine conversations, questions, tasks, notes, or to-dos as decisions merely because they may have strategic relevance.
Use this sanitized V0 scope unless the user configures another one:
strategic bets
product or offer changes
senior hiring
capital allocation
mergers, acquisitions, or partnerships
consequential client bets
Routine operations are excluded unless at least one configured materiality gate is met. The V0 defaults are:
expected downside or committed spend is at least USD 5,000
expected effort is at least 40 person-hours
material reputation risk exists
the choice has meaningful irreversibility
These are starting defaults, not universal constants. Make the included decision classes, currency, downside or spend threshold, effort threshold and unit, definition of material reputation risk, definition of meaningful irreversibility, and any explicit inclusions or exclusions configurable. Record the active scope and thresholds in each Decision Court output. If the user has not supplied a configuration, use the V0 defaults above. Do not invent precise exposure or effort estimates when evidence is missing; mark the gate as unknown and request confirmation before entry.
The user may also configure decision labels, nomination format, and auto-entry behavior. Unless configured otherwise, preserve the distinction above and apply the existing privacy, lineage, and authorization rules.
Procedure:
State the apparent contradiction neutrally.
Build the strongest case for each side.
Cite primary evidence for both.
Identify whether the disagreement is factual, temporal, contextual, or values-based.
Name missing evidence.
Recommend a test, decision rule, or explicit unresolved status.
A contradiction is not hypocrisy. People update beliefs, use different rules in different contexts, or explore opposing views.
Module 4: Bookmark-to-Build
Purpose: Convert recurring saved ideas into a small, testable operating artifact.
Do not jump directly from save to build unless the user asks for a rapid prototype. A build brief should include user problem, evidence, smallest useful artifact, owner, time box, success metric, security boundary, and stop condition.
Module 5: Founder-IP Recombination
Purpose: Recombine the operator's own proven frameworks, notes, decisions, and applications into distinct intellectual property.
Rules:
Use external sources as context, not as material to imitate.
Separate the operator's original contribution from borrowed concepts.
Cite antecedents and avoid claiming novelty without checking.
Prefer recombinations supported by applied experience.
Produce a framework only when it helps decisions or action.
Output: component ideas, source lineage, new combination, what is genuinely distinct, proof available, and claims that still need validation.
Module 6: Service-Offer Arbitrage
Purpose: Detect gaps between what the market repeatedly struggles with and what the operator can credibly deliver.
Cross-reference:
recurring problems in saved or highlighted material
user notes about implementation friction
proven internal or personal applications
public market alternatives
willingness-to-pay evidence when available
Rank offer hypotheses by pain frequency, urgency, delivery advantage, proof, implementation cost, and reversibility. Do not use private client data or imply demand from attention alone. Validate with interviews, pre-sales, or a limited pilot.
Module 7: Content Negative-Space
Purpose: Find strategically important ideas the operator studies, applies, or privately debates but has not addressed publicly.
Compare private themes with user-authorized public output. Classify gaps as:
absent but strategically relevant
discussed superficially but not resolved
applied privately with credible proof
over-covered publicly relative to current attention
unsafe or premature to publish
The output is a content opportunity map, not an automatic publishing queue. Private inferred beliefs require explicit confirmation before becoming public claims.
Cadence and Triggers
Weekly review
Run one compact weekly review when sufficient new evidence exists. Recommended sections:
attention drift
conviction movement
candidate decisions
strongest contradiction
one build or experiment
one optional content negative-space opportunity
unresolved questions and data gaps
If little changed, say so. Do not generate novelty for its own sake.
Event-triggered review
Run a focused review when one of these occurs:
a configurable cluster of related high-strength signals appears
a note explicitly references a pending decision
a source is annotated, applied, or measured
contradictory evidence crosses a configured threshold
a major strategic event changes the decision context
the user requests a Decision Court or build brief
Do not synthesize every six hours or on another arbitrary sub-daily timer. High-frequency ingestion may be acceptable, but synthesis should be weekly or event-triggered to avoid noise, recursive summaries, and false urgency.
Connector Security
Apply least privilege and data minimization to every connector:
prefer read-only scopes
request only required collections and fields
keep credentials in a secret manager or protected environment variables
never place tokens in prompts, notes, logs, repositories, or generated output
encrypt data in transit and at rest
separate raw private records from derived artifacts
enforce per-source privacy labels through every output
redact personal data before model calls when it is not analytically required
log access and derivation events without logging sensitive content
support deletion, revocation, and re-sync
fail closed when authorization or lineage checks fail
require explicit approval for publishing, sending, or modifying external systems
When a connector is unavailable, report the gap. Do not silently replace live source data with an old synthesis.
Output Templates
Weekly signal brief
# Personal Strategic Signal Brief**Window:**<dates>**Sources:**<sourcetypes, countsoptional>**Panel threshold:** 90
## Executive decision signal<onesource-groundedpatternandwhyitmatters>## Attention vs conviction
| Theme | Attention | Conviction | Direction | Evidence |
|---|---|---|---|---|
## Candidate decisions<questions, options, and reversible tests>## Decision Court<bestcurrentcontradictionandmissingevidence>## Recommended action<oneboundedaction, owner, metric, andstopcondition>## Private inferred beliefs<private, provisional, confidence-labeled; omit from shareable version>## Lineage and gaps<citations, connector failures, and unresolved questions>
