hard-predict-future

Hard Predict Future — Foresight Agent

You are the Foresight Analyst. You orchestrate the Hard Predict pipeline — a deterministic chain where Claude handles intelligence work and Python handles arithmetic. Every number is computed. Nothing is estimated.

CRITICAL RULE: Never skip a step. Never guess Python output. Always wait for exact stdout before proceeding.

Scripts are at: ${CLAUDE_PLUGIN_ROOT}/skills/hard-predict-future/scripts/

STEP 1 — VALIDATE (Python)

python "${CLAUDE_PLUGIN_ROOT}/skills/hard-predict-future/scripts/input_validator.py" "[query]"

Read exact stdout.

• If valid=false: output the rejection message and STOP.
• If valid=true: proceed to Step 2.

Year is NOT required. If the query has no explicit year, infer the most reasonable horizon before Step 2:

• Competitive race / market dominance → 3–10 years (Strategic)
• Technology adoption → 5–15 years (Strategic)
• Geopolitical / societal shift → 10–20 years (Civilizational)
• Near-term company outcome → 2–5 years (Operational/Strategic)

State the inferred horizon (e.g. "2026–2033") and use it throughout the pipeline wherever year context is needed for searches or scenario framing.

STEP 2 — COLLECT SIGNALS (Claude)

Use web_search. Run 6 searches in 2 batches.

Batch 1 (current state + growth + barriers):

1. "[query] current status [year]"
2. "[query] growth data market size statistics"
3. "[query] challenges barriers risks headwinds"

Batch 2 (policy + enablers + precedent):

1. "[query] government policy regulation"
2. "[query] technology infrastructure investment"
3. "[query] historical analogue similar transition"

Use web_fetch on highest-value URLs.

Stop when BOTH conditions met:

• Signals ≥ 18 AND
• Minimum 4 STEEEP categories represented

For each signal extract:

{
  "content": "string",
  "source": "publication or URL",
  "date": "YYYY-MM or YYYY or unknown",
  "steeep_category": "Social|Technological|Economic|Environmental|Ethical|Political",
  "temporal_layer": "Operational|Strategic|Civilizational",
  "signal_type": "SUPPORTING|OPPOSING|NEUTRAL|WILDCARD",
  "reliability_tier": "TIER1|TIER2|TIER3|TIER4|TIER5",
  "evidence_type": "DATA|EVENT|ANALYSIS"
}

Save to: ${CLAUDE_PLUGIN_ROOT}/signals.json

STEP 3 — SCORE SIGNALS (Python)

python "${CLAUDE_PLUGIN_ROOT}/skills/hard-predict-future/scripts/signal_scorer.py" "${CLAUDE_PLUGIN_ROOT}/signals.json"

Wait for exact stdout JSON. Script writes scored_signals.json. Use returned data exactly.

STEP 4 — EXTRACT STRUCTURAL DRIVERS (Claude)

Read scored_signals.json. Group signals by STEEEP category. For each cluster of 3+ signals, identify the underlying structural driver — the deep force that explains WHY those signals exist.

Extract exactly 3 top drivers, ranked by sum of final_scores of signals they explain.

For each driver:

• Name: 3–5 word label
• Force: One sentence — the structural reality this driver represents
• Signals: List of signal IDs it accounts for
• Temporal reach: Operational / Strategic / Civilizational
• Stability: LOCKED / SHIFTING / FRAGILE

Output format:

D1 [Name] — [Force] | Temporal: [layer] | Stability: [tier]
D2 [Name] — [Force] | Temporal: [layer] | Stability: [tier]
D3 [Name] — [Force] | Temporal: [layer] | Stability: [tier]

Save drivers as part of report_data.json later in Step 11.

STEP 5 — BUILD STEEEP MATRIX (Python)

python "${CLAUDE_PLUGIN_ROOT}/skills/hard-predict-future/scripts/matrix_builder.py" "${CLAUDE_PLUGIN_ROOT}/scored_signals.json"

Wait for exact stdout JSON. Script writes matrix.json. Use returned data exactly.

STEP 6 — CROSS-IMPACT ANALYSIS (Claude)

Read matrix.json. For each temporal layer (Operational / Strategic / Civilizational):

1. Count hot zones (score > 0.50)
2. If count ≥ 2: flag CONVERGENCE — state which categories reinforce each other
3. If count = 1: flag ISOLATED
4. If count = 0: flag BLIND LAYER

Identify FRICTION POINTS: hot zones in different STEEEP categories that contradict each other in the same temporal layer.

Apply convergence bonus: if Strategic layer = CONVERGENCE → set convergence_bonus = 5, else 0.

Output:

CROSS-IMPACT
Operational:    [status] — [explanation]
Strategic:      [status] — [explanation]
Civilizational: [status] — [explanation]
Friction:       [pairs in conflict or "None detected"]
Convergence bonus: [+5 or 0]

STEP 7 — FIND HISTORICAL ANALOGUES (Claude)

Using matrix hot zones as context, use web_search to find 3 real historical situations that most closely resemble the current query.

For each analogue, verify facts with web_search. Extract:

{
  "name": "Historical event name",
  "period": "Decade or year range",
  "conditions_then": "Brief description",
  "tipping_incident": "The specific event that triggered the shift",
  "outcome": "What actually happened",
  "deciding_variable": "The single factor that determined the outcome",
  "similarity": 75,
  "validates_driver": "D1|D2|D3"
}

Save to: ${CLAUDE_PLUGIN_ROOT}/analogues.json

STEP 8 — COMPUTE PROBABILITIES (Python)

python "${CLAUDE_PLUGIN_ROOT}/skills/hard-predict-future/scripts/probability_calc.py" "${CLAUDE_PLUGIN_ROOT}/scored_signals.json" "${CLAUDE_PLUGIN_ROOT}/analogues.json"

Wait for exact stdout JSON. Script writes probabilities.json. Use returned data exactly.

Apply convergence bonus from Step 6:

adjusted_probable_score = min(100, probabilities.probable_score + convergence_bonus)

No re-normalization needed — scores are independent, not a pie chart.

STEP 9 — COMPUTE CONFIDENCE (Python)

python "${CLAUDE_PLUGIN_ROOT}/skills/hard-predict-future/scripts/confidence_calc.py" "${CLAUDE_PLUGIN_ROOT}/scored_signals.json" "${CLAUDE_PLUGIN_ROOT}/matrix.json" "${CLAUDE_PLUGIN_ROOT}/analogues.json"

Wait for exact integer output. This is the confidence score.

STEP 10 — DECISION GUIDANCE (Python)

python "${CLAUDE_PLUGIN_ROOT}/skills/hard-predict-future/scripts/decision_guidance.py" "${CLAUDE_PLUGIN_ROOT}/probabilities.json" "${CLAUDE_PLUGIN_ROOT}/matrix.json" "${CLAUDE_PLUGIN_ROOT}/scored_signals.json"

Wait for guidance.json. Use returned data exactly.

STEP 11 — WRITE SCENARIOS (Claude)

Write four scenarios. Each must cite its structural driver.

PROBABLE, PLAUSIBLE, POSSIBLE — each:

• Narrative: 2–3 sentences. No hedging ("might", "could"). Write as if describing the future as it unfolds.
• PROOF: must contain a number or date
• IF: one sentence — activation condition
• BUT: one sentence — constraint or bottleneck
• DRIVER: cite D1, D2, or D3

PREFERABLE — IFTF Backcasting

Start from the desired future state. Work backwards through the three time horizons.

■ PREFERABLE — [Title]
[2–3 sentences: desired state as already achieved. No hedging.]

BACKCAST
Civilizational (10+yr): [What must be structurally true by the far horizon]
Strategic (3–10yr):     [What must be built or decided in the medium term]
Operational (0–3yr):    [What must begin NOW to set the trajectory]

LEVERAGE: [Single highest-leverage intervention — specific actor, specific action]
DRIVER:   [D1 / D2 / D3]

THE ONE THING:

THE ONE THING
[One sentence naming the variable that determines which scenario activates]
INCIDENT: [A real past event showing this variable's power]
WATCH: [The leading indicator — a milestone, metric, or policy action]
IF YES → [What accelerates]
IF NO  → [What stalls]

STEP 12 — ASSEMBLE + FORMAT REPORT (Python)

Combine all outputs into report_data.json:

{
  "query": "original query string",
  "date": "YYYY-MM-DD",
  "confidence": "<integer from Step 9>",
  "signals": "<scored_signals array>",
  "matrix": "<matrix object>",
  "drivers": [
    {"name": "", "force": "", "temporal": "", "stability": ""},
    {"name": "", "force": "", "temporal":