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Structured analytic techniques including ACH, SWOT, Devil's Advocacy, Red Team analysis for political intelligence
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| name | intelligence-analysis-techniques |
| description | Structured analytic techniques including ACH, SWOT, Devil's Advocacy, Red Team analysis for political intelligence |
| license | Apache-2.0 |
This skill MUST be applied with the AI FIRST principle: never accept first-pass quality. ALL analysis and content MUST go through minimum 2 complete iterations. After first pass, read ALL output back completely and systematically improve every section — strengthen evidence, deepen analysis, add specific citations, broaden perspectives. Spend ALL allocated time on real work. Single-pass output is NEVER acceptable. NO SHORTCUTS.
This skill provides comprehensive structured analytic techniques (SATs) for intelligence analysis in the Riksdagsmonitor platform. It implements proven methodologies from the intelligence community to reduce cognitive bias, improve analytical rigor, and produce defensible intelligence assessments on Swedish political actors, parties, and democratic institutions.
Apply this skill when:
Do NOT use for:
graph TB
subgraph "Data Foundation"
A[OSINT Collection] --> B[Data Integration]
B --> C[Quality Validation]
end
subgraph "Structured Analytic Techniques"
C --> D[Decomposition & Visualization]
C --> E[Idea Generation]
C --> F[Hypothesis Generation & Testing]
C --> G[Scenarios & Indicators]
C --> H[Challenge Analysis]
end
subgraph "Technique Categories"
D --> D1[Key Assumptions Check]
D --> D2[Issue Redefinition]
E --> E1[Brainstorming]
E --> E2[Red Team Analysis]
F --> F1[Analysis of Competing Hypotheses]
F --> F2[Diagnostic Reasoning]
G --> G1[Alternative Futures]
G --> G2[Indicators Development]
H --> H1[Devil's Advocacy]
H --> H2[Team A/Team B]
end
subgraph "Intelligence Products"
D1 & D2 & E1 & E2 & F1 & F2 & G1 & G2 & H1 & H2 --> I[Risk Assessments]
I --> J[Political Scorecards]
I --> K[Coalition Analysis]
I --> L[Trend Reports]
end
style A fill:#e1f5ff
style F1 fill:#ffeb99
style H1 fill:#ffcccc
style I fill:#ccffcc
Purpose: Systematically evaluate multiple explanations to reduce confirmation bias and identify the most likely scenario.
When to Use:
CIA Platform Application:
Question: Which coalition is most likely to form the government?
Hypotheses:
H1: Center-right coalition (M, KD, SD, L) - 176 seats
H2: Center-right minority (M, KD, L) with SD support - 116 + 73 external
H3: Center-left coalition (S, V, MP, C) - 159 seats (minority)
H4: Grand coalition (S, M, C) - 159 seats
H5: Sweden Democrats lead coalition (SD, M, KD) - 141 seats
Evidence Matrix:
-- Generate ACH Evidence Matrix from voting patterns and party statements
WITH party_alignment AS (
SELECT
p1.party as party_a,
p2.party as party_b,
COUNT(CASE WHEN p1.vote = p2.vote THEN 1 END) as agreement_votes,
COUNT(*) as total_votes,
ROUND(100.0 * COUNT(CASE WHEN p1.vote = p2.vote THEN 1 END) / COUNT(*), 2) as alignment_percentage
FROM vote_data p1
JOIN vote_data p2 ON p1.ballot_id = p2.ballot_id AND p1.party < p2.party
WHERE p1.vote_date >= '2022-09-11' -- Current parliament
AND p1.vote IN ('Ja', 'Nej')
AND p2.vote IN ('Ja', 'Nej')
GROUP BY p1.party, p2.party
)
SELECT * FROM party_alignment
WHERE alignment_percentage > 70 -- Strong alignment threshold
ORDER BY alignment_percentage DESC;
ACH Implementation:
@Service
public class CompetingHypothesesAnalyzer {
public ACHResult analyzeCoalitionFormation(List<Hypothesis> hypotheses, List<Evidence> evidence) {
// 1. Create evidence matrix
ACHMatrix matrix = new ACHMatrix(hypotheses, evidence);
// 2. Rate consistency of each evidence with each hypothesis
for (Hypothesis h : hypotheses) {
for (Evidence e : evidence) {
ConsistencyRating rating = rateConsistency(h, e);
matrix.setCell(h, e, rating);
}
}
// 3. Calculate hypothesis scores
Map<Hypothesis, Double> scores = new HashMap<>();
for (Hypothesis h : hypotheses) {
double score = calculateHypothesisScore(matrix, h);
scores.put(h, score);
}
// 4. Rank hypotheses by diagnostic value
List<Hypothesis> rankedHypotheses = scores.entrySet().stream()
.sorted(Map.Entry.<Hypothesis, Double>comparingByValue().reversed())
.map(Map.Entry::getKey)
.collect(Collectors.toList());
// 5. Identify most diagnostic evidence
List<Evidence> diagnosticEvidence = identifyDiagnosticEvidence(matrix);
return ACHResult.builder()
.matrix(matrix)
.rankedHypotheses(rankedHypotheses)
.diagnosticEvidence(diagnosticEvidence)
.confidence(calculateConfidence(scores))
.build();
}
private ConsistencyRating rateConsistency(Hypothesis h, Evidence e) {
// Consistency scale:
// CC (++): Consistent - expected if hypothesis is true
// C (+): Somewhat consistent
// N (neutral): Neither consistent nor inconsistent
// I (-): Somewhat inconsistent
// II (--): Inconsistent - would not expect if hypothesis is true
if (h.getName().equals("Center-right coalition") &&
e.getType().equals("VOTING_ALIGNMENT") &&
e.getParties().containsAll(Arrays.asList("M", "KD", "L"))) {
return ConsistencyRating.CONSISTENT;
}
// Additional logic for other hypothesis-evidence combinations
return ConsistencyRating.NEUTRAL;
}
private double calculateHypothesisScore(ACHMatrix matrix, Hypothesis h) {
// Score = sum of consistency ratings weighted by evidence credibility
double score = 0.0;
for (Evidence e : matrix.getEvidence()) {
ConsistencyRating rating = matrix.getCell(h, e);
score += rating.getValue() * e.getCredibility();
}
return score;
}
}
ACH Output Example:
| Evidence | H1 | H2 | H3 | H4 | H5 |
|---|---|---|---|---|---|
| M-KD-L voting alignment >80% | ++ | ++ | -- | + | + |
| SD explicitly supports M-led govt | ++ | ++ | -- | -- | + |
| S refuses SD cooperation | -- | -- | + | N | -- |
| C rejects left-wing coalition | ++ | ++ | -- | + | -- |
| L historically opposed SD | N | + | N | N | -- |
| Parliamentary math (176 seats) | ++ | + | -- | N | -- |
| TOTAL SCORE | +9 | +7 | -8 | 0 | -4 |
Conclusion: H1 (Center-right coalition with SD) most consistent with evidence. Confidence: HIGH (80%).
Purpose: Systematically assess Strengths, Weaknesses, Opportunities, and Threats for strategic planning.
CIA Platform Application:
-- Generate SWOT data from Riksdagsmonitor platform views
WITH party_metrics AS (
SELECT
party,
AVG(total_days_served) as avg_experience_days,
AVG(percent_yes + percent_no) as avg_participation_rate,
COUNT(DISTINCT person_id) as party_size
FROM view_riksdagen_politician_summary
WHERE party = 'S'
GROUP BY party
),
party_performance AS (
SELECT
party,
AVG(percentage_won) as win_rate,
COUNT(DISTINCT ballot_id) as ballots_participated
FROM view_riksdagen_party_ballot_support_annual_summary
WHERE party = 'S' AND election_year >= EXTRACT(YEAR FROM CURRENT_DATE) - 4
GROUP BY party
),
coalition_potential AS (
SELECT
'S' as party,
SUM(parliament_seats) as potential_coalition_seats
FROM sweden_political_party
WHERE party_name IN ('S', 'V', 'MP', 'C')
)
SELECT
pm.*,
pp.win_rate,
pp.ballots_participated,
cp.potential_coalition_seats
FROM party_metrics pm
JOIN party_performance pp ON pm.party = pp.party
CROSS JOIN coalition_potential cp;
SWOT Matrix Implementation:
@Service
public class SWOTAnalyzer {
public SWOTAnalysis performPartySWOT(String partyCode) {
// Strengths: Internal positive factors
List<String> strengths = new ArrayList<>();
// Query: Check party size and parliamentary seats
Integer seats = partyRepository.getParliamentSeats(partyCode);
if (seats != null && seats > 50) {
strengths.add("Large parliamentary representation (" + seats + " seats)");
}
// Query: Check government experience
Long governmentDays = partyRepository.getTotalGovernmentDays(partyCode);
if (governmentDays > 3650) { // 10+ years
strengths.add("Extensive government experience (" + (governmentDays / 365) + " years)");
}
// Query: Check voting discipline
Double discipline = partyRepository.getVotingDiscipline(partyCode);
if (discipline > 95.0) {
strengths.add("High party discipline (" + discipline + "%)");
}
// Weaknesses: Internal negative factors
List<String> weaknesses = new ArrayList<>();
// Query: Check declining support
Double supportTrend = partyRepository.getSupportTrend(partyCode, 4); // Last 4 years
if (supportTrend < -2.0) {
weaknesses.add("Declining electoral support (" + supportTrend + "% over 4 years)");
}
// Query: Check internal conflicts (defections)
Long defections = partyRepository.getDefectionCount(partyCode, 4);
if (defections > 2) {
weaknesses.add("Internal instability (" + defections + " defections in 4 years)");
}
// Opportunities: External positive factors
List<String> opportunities = new ArrayList<>();
// Query: Coalition potential
Integer potentialCoalitionSeats = partyRepository.getCoalitionSeats(partyCode);
if (potentialCoalitionSeats > 175) {
opportunities.add("Viable coalition path to majority (" + potentialCoalitionSeats + " seats)");
}
// Query: Issue ownership
List<String> ownedIssues = partyRepository.getDominantPolicyAreas(partyCode);
opportunities.add("Issue ownership in: " + String.join(", ", ownedIssues));
// Threats: External negative factors
List<String> threats = new ArrayList<>();
// Query: Competitor strength
Double competitorStrength = partyRepository.getLargestCompetitorStrength(partyCode);
if (competitorStrength > 25.0) {
threats.add("Strong competitor party (" + competitorStrength + "% support)");
}
// Query: Coalition instability
Double coalitionAlignment = partyRepository.getCoalitionAlignment(partyCode);
if (coalitionAlignment < 80.0) {
threats.add("Coalition alignment challenges (" + coalitionAlignment + "%)");
}
return SWOTAnalysis.builder()
.partyCode(partyCode)
.strengths(strengths)
.weaknesses(weaknesses)
.opportunities(opportunities)
.threats(threats)
.build();
}
}
SWOT Output Example:
═════════════════════════════════════════════════════════════
SWOT ANALYSIS: Social Democrats (S)
═════════════════════════════════════════════════════════════
STRENGTHS (Internal Positive)
─────────────────────────────────────────────────────────────
✅ Largest parliamentary party (107 seats, 30.6%)
✅ Extensive government experience (45 years total since 1970)
✅ High party discipline (96.3% voting cohesion)
✅ Strong union relationships and funding base
✅ Broad geographic support across all regions
WEAKNESSES (Internal Negative)
─────────────────────────────────────────────────────────────
❌ Declining electoral support (-3.2% since 2018)
❌ Leadership transition challenges
❌ Weak position on immigration issue (dominated by SD)
❌ Aging voter base (median age 58)
OPPORTUNITIES (External Positive)
─────────────────────────────────────────────────────────────
🔵 Coalition potential with V, MP, C = 159 seats (near majority)
🔵 Economic downturn favors welfare focus
🔵 Environmental policy increasingly salient
🔵 Potential to reclaim centrist voters from M
THREATS (External Negative)
─────────────────────────────────────────────────────────────
🔴 SD growth (20.5% in 2022, +3.1% from 2018)
🔴 Center-right coalition controls 176 seats
🔴 Fragmented left bloc (S, V, MP divergent priorities)
🔴 Policy space squeezed between left (V) and center (C)
═════════════════════════════════════════════════════════════
Purpose: Challenge prevailing assumptions by deliberately arguing against consensus position.
CIA Platform Application:
Prevailing View: "The center-right government is stable with 176 seats."
Devil's Advocate Position: "The coalition is actually fragile and may collapse within 18 months."
Challenging Arguments:
@Service
public class DevilsAdvocacyAnalyzer {
public ChallengeAnalysis challengeCoalitionStability(String coalitionName) {
ChallengeAnalysis analysis = new ChallengeAnalysis();
analysis.setPrevailingView("Coalition is stable");
analysis.setChallengePosition("Coalition is fragile");
// 1. Identify hidden weaknesses
List<String> weaknesses = new ArrayList<>();
// SQL: Check for voting defections
String sqlDefections = """
SELECT
p.party,
COUNT(CASE WHEN v.vote != modal_vote.mode THEN 1 END) as defection_count,
COUNT(*) as total_votes,
ROUND(100.0 * COUNT(CASE WHEN v.vote != modal_vote.mode THEN 1 END) / COUNT(*), 2) as defection_rate
FROM vote_data v
JOIN (
SELECT ballot_id, party, MODE() WITHIN GROUP (ORDER BY vote) as mode
FROM vote_data
GROUP BY ballot_id, party
) modal_vote ON v.ballot_id = modal_vote.ballot_id AND v.party = modal_vote.party
WHERE v.party IN ('M', 'KD', 'L')
AND v.vote_date >= CURRENT_DATE - INTERVAL '6 months'
GROUP BY p.party
HAVING ROUND(100.0 * COUNT(CASE WHEN v.vote != modal_vote.mode THEN 1 END) / COUNT(*), 2) > 5.0
""";
List<PartyDisciplineMetric> defections = jdbcTemplate.query(sqlDefections,
(rs, rowNum) -> new PartyDisciplineMetric(
rs.getString("party"),
rs.getInt("defection_count"),
rs.getDouble("defection_rate")
)
);
for (PartyDisciplineMetric metric : defections) {
weaknesses.add(String.format(
"%s showing %d defections (%.2f%%) - indicates internal dissent",
metric.getParty(), metric.getDefectionCount(), metric.getDefectionRate()
));
}
// 2. Alternative interpretation of evidence
List<String> alternativeInterpretations = new ArrayList<>();
// SQL: Check for close votes (thin majorities)
String sqlCloseVotes = """
SELECT
ballot_id,
ballot_label,
COUNT(CASE WHEN vote = 'Ja' THEN 1 END) as yes_votes,
COUNT(CASE WHEN vote = 'Nej' THEN 1 END) as no_votes,
ABS(COUNT(CASE WHEN vote = 'Ja' THEN 1 END) -
COUNT(CASE WHEN vote = 'Nej' THEN 1 END)) as vote_margin
FROM vote_data
WHERE vote_date >= CURRENT_DATE - INTERVAL '3 months'
AND vote IN ('Ja', 'Nej')
GROUP BY ballot_id, ballot_label
HAVING ABS(COUNT(CASE WHEN vote = 'Ja' THEN 1 END) -
COUNT(CASE WHEN vote = 'Nej' THEN 1 END)) < 10
ORDER BY vote_margin ASC
LIMIT 10
""";
List<CloseVote> closeVotes = jdbcTemplate.query(sqlCloseVotes,
(rs, rowNum) -> new CloseVote(
rs.getString("ballot_id"),
rs.getString("ballot_label"),
rs.getInt("vote_margin")
)
);
if (closeVotes.size() > 5) {
alternativeInterpretations.add(
String.format("Coalition has won %d ballots by <10 votes in last 3 months - " +
"indicates razor-thin majority vulnerable to defections", closeVotes.size())
);
}
// 3. Hypothetical scenarios that could invalidate prevailing view
List<String> invalidatingScenarios = new ArrayList<>();
invalidatingScenarios.add("L party decides SD support is politically toxic, withdraws from coalition");
invalidatingScenarios.add("Internal M leadership challenge leads to policy shift away from SD cooperation");
invalidatingScenarios.add("SD makes excessive demands on immigration policy, causing L to defect");
invalidatingScenarios.add("Economic crisis requires unpopular budget cuts, fracturing coalition unity");
analysis.setIdentifiedWeaknesses(weaknesses);
analysis.setAlternativeInterpretations(alternativeInterpretations);
analysis.setInvalidatingScenarios(invalidatingScenarios);
return analysis;
}
}
Devil's Advocacy Output:
═════════════════════════════════════════════════════════════
DEVIL'S ADVOCACY: Coalition Stability Challenge
═════════════════════════════════════════════════════════════
PREVAILING VIEW: The center-right coalition is stable with 176 seats.
CHALLENGE POSITION: The coalition is actually fragile and may collapse.
HIDDEN WEAKNESSES:
─────────────────────────────────────────────────────────────
1. L showing 8 defections (6.2%) in last 6 months - internal dissent
2. M has 3 MPs with consistent voting against party line on SD-supported bills
3. KD unity high (98.1%) but only 19 MPs - vulnerable to single defection
ALTERNATIVE INTERPRETATIONS:
─────────────────────────────────────────────────────────────
1. Coalition won 12 ballots by <10 votes (razor-thin majority)
2. SD external support requires constant negotiation, creating friction
3. L party leadership under pressure from voters uncomfortable with SD ties
INVALIDATING SCENARIOS:
─────────────────────────────────────────────────────────────
1. L withdraws due to SD political toxicity (reduces coalition to 162 seats)
2. Economic crisis requires unpopular cuts, fracturing unity
3. SD demands on immigration exceed L tolerance, triggering defection
CONFIDENCE IN CHALLENGE: MEDIUM (60%)
═════════════════════════════════════════════════════════════
Purpose: Simulate adversary behavior to identify vulnerabilities and blind spots.
CIA Platform Application:
Scenario: You are the opposition strategist (S party). How would you destabilize the center-right government?
@Service
public class RedTeamAnalyzer {
public RedTeamReport simulateOppositionStrategy(String targetCoalition, String oppositionParty) {
RedTeamReport report = new RedTeamReport();
report.setScenario("Opposition strategy to destabilize " + targetCoalition);
report.setRedTeamParty(oppositionParty);
// 1. Identify coalition vulnerabilities
List<Vulnerability> vulnerabilities = identifyCoalitionVulnerabilities(targetCoalition);
// 2. Develop exploitation strategies
List<Strategy> strategies = new ArrayList<>();
// Strategy 1: Exploit ideological rifts
if (vulnerabilities.stream().anyMatch(v -> v.getType() == VulnerabilityType.IDEOLOGICAL_SPLIT)) {
strategies.add(Strategy.builder()
.name("Wedge Issue Exploitation")
.description("Propose legislation that divides coalition on moral issues (e.g., immigration, LGBTQ+ rights)")
.implementation("File motion on asylum policy that forces L to choose between SD and principles")
.expectedOutcome("L votes with opposition, coalition loses vote, internal tensions rise")
.successProbability(0.65)
.build());
}
// Strategy 2: Target weak coalition members
if (vulnerabilities.stream().anyMatch(v -> v.getType() == VulnerabilityType.MEMBER_VULNERABILITY)) {
strategies.add(Strategy.builder()
.name("Individual MP Persuasion")
.description("Identify vulnerable MPs and offer political cover for defection")
.implementation("Private negotiations with L MPs facing local backlash over SD cooperation")
.expectedOutcome("1-2 MP defections, coalition loses majority")
.successProbability(0.35)
.build());
}
// Strategy 3: Force unpopular votes
if (vulnerabilities.stream().anyMatch(v -> v.getType() == VulnerabilityType.UNPOPULAR_POLICY)) {
strategies.add(Strategy.builder()
.name("Force Unpopular Policy Votes")
.description("Demand votes on SD priorities that are unpopular with L voter base")
.implementation("Use interpellations to force government to defend SD positions")
.expectedOutcome("L public support declines, internal pressure to exit coalition")
.successProbability(0.55)
.build());
}
report.setVulnerabilities(vulnerabilities);
report.setStrategies(strategies);
return report;
}
private List<Vulnerability> identifyCoalitionVulnerabilities(String coalition) {
List<Vulnerability> vulnerabilities = new ArrayList<>();
// SQL: Identify voting divergence between coalition partners
String sql = """
WITH coalition_votes AS (
SELECT
ballot_id,
party,
MODE() WITHIN GROUP (ORDER BY vote) as party_position
FROM vote_data
WHERE party IN ('M', 'KD', 'L')
AND vote_date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY ballot_id, party
)
SELECT
ballot_id,
COUNT(DISTINCT party_position) as position_count
FROM coalition_votes
GROUP BY ballot_id
HAVING COUNT(DISTINCT party_position) > 1
""";
Integer divergentVotes = jdbcTemplate.queryForObject(sql, Integer.class);
if (divergentVotes > 20) {
vulnerabilities.add(Vulnerability.builder()
.type(VulnerabilityType.IDEOLOGICAL_SPLIT)
.description("Coalition partners voted differently on " + divergentVotes + " ballots")
.severity(SeverityLevel.HIGH)
.build());
}
return vulnerabilities;
}
}
Purpose: Explicitly identify and challenge assumptions underlying analysis.
Example: Coalition Formation Assumptions
ASSUMPTION #1: "Party voting alignment predicts coalition likelihood"
├─ Validity: STRONG - Historical evidence supports (r=0.82)
├─ Dependency: Analysis depends heavily on this assumption
├─ Challenge: What if parties prioritize electoral positioning over ideology?
└─ Monitoring: Track voting alignment vs. public coalition statements
ASSUMPTION #2: "SD will support M-led government consistently"
├─ Validity: MODERATE - No formal coalition agreement
├─ Dependency: Critical for government majority
├─ Challenge: SD may withdraw support to gain concessions
└─ Monitoring: Count SD-government voting alignment monthly
ASSUMPTION #3: "No mid-term defections will occur"
├─ Validity: WEAK - Historical precedent exists (3 defections 2014-2018)
├─ Dependency: Coalition stability assumption based on this
├─ Challenge: Personal scandals or ideological conflicts could trigger defections
└─ Monitoring: Track individual MP voting patterns and public statements
Purpose: Create observable metrics to validate or invalidate hypotheses.
Example: Government Collapse Indicators
-- Create Early Warning Indicators for Coalition Collapse
CREATE MATERIALIZED VIEW coalition_stability_indicators AS
WITH voting_discipline AS (
SELECT
party,
COUNT(CASE WHEN vote = modal_vote THEN 1 END)::decimal / COUNT(*) as discipline_rate,
DATE_TRUNC('month', vote_date) as month
FROM vote_data v
JOIN (
SELECT ballot_id, party, MODE() WITHIN GROUP (ORDER BY vote) as modal_vote
FROM vote_data
GROUP BY ballot_id, party
) mv ON v.ballot_id = mv.ballot_id AND v.party = mv.party
WHERE party IN ('M', 'KD', 'L')
AND vote_date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY party, DATE_TRUNC('month', vote_date)
),
close_votes AS (
SELECT
DATE_TRUNC('month', vote_date) as month,
COUNT(*) as close_vote_count
FROM (
SELECT
ballot_id,
vote_date,
ABS(SUM(CASE WHEN vote = 'Ja' THEN 1 WHEN vote = 'Nej' THEN -1 ELSE 0 END)) as margin
FROM vote_data
GROUP BY ballot_id, vote_date
) margins
WHERE margin < 10
GROUP BY DATE_TRUNC('month', vote_date)
),
interpellations AS (
SELECT
DATE_TRUNC('month', made_date) as month,
COUNT(*) as interpellation_count
FROM document_element
WHERE document_type = 'ip' -- Interpellations
AND made_date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY DATE_TRUNC('month', made_date)
)
SELECT
vd.month,
AVG(vd.discipline_rate) as avg_coalition_discipline,
cv.close_vote_count,
ip.interpellation_count,
CASE
WHEN AVG(vd.discipline_rate) < 0.90 THEN '🔴 CRITICAL'
WHEN AVG(vd.discipline_rate) < 0.95 THEN '🟠 WARNING'
ELSE '🟢 STABLE'
END as stability_status
FROM voting_discipline vd
LEFT JOIN close_votes cv ON vd.month = cv.month
LEFT JOIN interpellations ip ON vd.month = ip.month
GROUP BY vd.month, cv.close_vote_count, ip.interpellation_count
ORDER BY vd.month DESC;
A.5.1 - Policies for Information Security
A.5.7 - Threat Intelligence
A.5.9 - Inventory of Information and Other Associated Assets
IDENTIFY (ID)
DETECT (DE)
RESPOND (RS)
CIS Control 16: Application Software Security
CIS Control 17: Incident Response Management
Information Security Policy
Threat Modeling
Risk Assessment Methodology
Data Classification Policy
Official Documentation:
CIA Platform Documentation:
Academic Sources:
This skill is consumed by the 11 agentic news workflows in .github/workflows/news-*.md. The authoritative contract lives in .github/prompts/README.md; this skill supplies domain expertise on top of that contract.
ai-driven-analysis-guide.md + every template in analysis/templates/.analysis/daily/$ARTICLE_DATE/$SUBFOLDER/; 05-analysis-gate.md is the single blocking gate.Authoritative source:
.github/prompts/ext/long-horizon-forecasting.md. Runtime helper:scripts/horizon-context.ts.
Structured analytic techniques must tag every probabilistic judgement with a horizon band and respect the WEP language ceiling:
| Band | Days | WEP language ceiling | Intelligence application |
|---|---|---|---|
72h | 3 | very likely / very unlikely | Imminent event outcome (ACH with strong diagnostic evidence) |
week | 7 | likely / unlikely | Near-term scenario node (scheduled votes, ministerial actions) |
month | 30 | likely / unlikely | Mid-range scenario branches (budget cycle, consultation outcomes) |
quarter | 90 | roughly even / about even | Session-level outlook (4 scenarios, 0 wildcards) |
year | 365 | roughly even; stronger requires ≥ 3 cycle-aged sources | Strategic forecast (4 scenarios + 5 wildcards, IMF T+1 projections) |
cycle | 1460 | roughly even / unlikely; never likely without ≥ 3 cycle-aged sources | Long-range intelligence forecast (4 scenarios + 5 wildcards, IMF T+5) |
election | 1460 | scenario-driven; coalition outcomes never above "likely" | Coalition-formation intelligence (4 × 3 branches = 12 leaves) |
Cycle-rollover rules (±30 days of election anchor) are defined in .github/prompts/ext/cycle-rollover.md.
Master GitHub Agentic Workflows authoring - markdown syntax, natural language instructions, YAML frontmatter, compilation, and workflow patterns
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