Agent skill

Hard Predict Future

by davepoon in davepoon/buildwithclaude

Activate this agent for any future-oriented question that requires deep quantitative analysis, historical precedents, and structured scenario planning.

MITAuto-check passedLegal & Compliance

Install Hard Predict Future

skills CLI
$ npx skills add davepoon/buildwithclaude --skill hard-predict-future -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install davepoon/buildwithclaude hard-predict-future --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/davepoon/buildwithclaude.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/foresight-intelligence/skills/hard-predict-future .claude/skills/hard-predict-future && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
hard-predict-future
GitHub stars
3.6k
Used in
1 other repo
Token cost
~4.2k tokens
SKILL.md length
691 words
Files
9 (incl. scripts)
Skills in repo
245
Repo updated
First seen
Licence
MIT

At a glance

Activate this agent for any future-oriented question that requires deep quantitative analysis, historical precedents, and structured scenario planning.

  • Works in 12 steps: VALIDATE (Python) → COLLECT SIGNALS (Claude) → SCORE SIGNALS (Python) → …
  • Include: Will [X]?
  • SKILL.md covers STEP 1 — VALIDATE (Python), STEP 2 — COLLECT SIGNALS…, STEP 3 — SCORE SIGNALS (Python) and STEP 4 — EXTRACT STRUCTURAL…, plus 9 more sections
  • Runs Python scripts from its folder; calls python

What it does

Hard Predict Future is an agent skill from davepoon/buildwithclaude. Activate this agent for any future-oriented question that requires deep quantitative analysis, historical precedents, and structured scenario planning. Triggers include: "Will [X]?", "Who will win [X]?", "What happens to [X]?", prediction requests with high stakes, foresight analysis, STEEEP scenario planning, futures cone, competitive race analysis, technology adoption curves, geopolitical shifts, or any question about a future outcome that deserves rigorous multi-step analysis. This agent runs a 12-step…

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts (for example `scripts/confidence_calc.py`, `scripts/decision_guidance.py` and `scripts/input_validator.py`).

It sits in Legal & Compliance, covering Legal research. It works with Python and Bash. The repository describes itself as: A single hub to find Claude Skills, Agents, Commands, Hooks, Plugins, and Marketplace collections to extend Claude Code, Claude Desktop, Agent SDK and OpenClaw. The licence is MIT.

When your agent uses it

  • Include: Will [X]?
  • Who will win [X]?
  • What happens to [X]?
  • Prediction requests with high stakes

Example prompts

  • “Will [X]?”
  • “Who will win [X]?”
  • “What happens to [X]?”
  • “/hard-predict-future”

Requirements

  • Python 3

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. VALIDATE (Python)
  2. COLLECT SIGNALS (Claude)
  3. SCORE SIGNALS (Python)
  4. EXTRACT STRUCTURAL DRIVERS (Claude)
  5. BUILD STEEEP MATRIX (Python)
  6. CROSS-IMPACT ANALYSIS (Claude)
  7. FIND HISTORICAL ANALOGUES (Claude)
  8. COMPUTE PROBABILITIES (Python)
  9. COMPUTE CONFIDENCE (Python)
  10. DECISION GUIDANCE (Python)
  11. WRITE SCENARIOS (Claude)
  12. ASSEMBLE + FORMAT REPORT (Python)

What it can do on your machine

Read from SKILL.md and the folder at commit 10bfc43. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 8 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hard Predict Future loads about 4.2k tokens when it runs. Until then it costs about 193 tokens; SKILL.md has 691 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~193
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from davepoon/buildwithclaude at commit 10bfc43, republished under its MIT licence (© davepoon). 691 words, ~4,178 tokens.

Download SKILL.mdSave it as .claude/skills/hard-predict-future/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
hard-predict-future
description
Activate this agent for any future-oriented question that requires deep quantitative analysis, historical precedents, and structured scenario planning. Triggers include: "Will [X]?", "Who will win [X]?", "What happens to [X]?", prediction requests with high stakes, foresight analysis, STEEEP scenario planning, futures cone, competitive race analysis, technology adoption curves, geopolitical shifts, or any question about a future outcome that deserves rigorous multi-step analysis. This agent runs a 12-step deterministic pipeline: Claude handles intelligence, Python handles all arithmetic. Year is NOT required — the engine infers the horizon. REQUIRES: Bash tool + Python 3.x. Not compatible with claude.ai web (use Soft Predict Future instead).
category
specialized-domains

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): 4. "[query] government policy regulation" 5. "[query] technology infrastructure investment" 6. "[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:

json
{
  "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]

Show full SKILL.md (263 more words)Show less

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:

json
{
  "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:

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": "", "stability": ""},
    {"name": "", "force": "", "temporal": "", "stability": ""}
  ],
  "cross_impact": {
    "operational": "", "strategic": "", "civilizational": "",
    "friction_points": [], "convergence_bonus": 0
  },
  "analogues": "<analogues array>",
  "probabilities": "<probabilities object>",
  "guidance": "<guidance object>",
  "scenarios": {
    "probable":   {"name": "", "description": "", "proof": "", "if_condition": "", "but_condition": "", "driver": ""},
    "plausible":  {"name": "", "description": "", "proof": "", "if_condition": "", "but_condition": "", "driver": ""},
    "possible":   {"name": "", "description": "", "proof": "", "if_condition": "", "but_condition": "", "driver": ""},
    "preferable": {
      "name": "", "description": "",
      "backcast": {"civilizational": "", "strategic": "", "operational": ""},
      "leverage": "", "driver": ""
    }
  },
  "the_one_thing": {"reframe": "", "incident": "", "watch_signal": "", "if_yes": "", "if_no": ""},
  "region": "detected region or null"
}
python "${CLAUDE_PLUGIN_ROOT}/skills/hard-predict-future/scripts/report_formatter.py" "${CLAUDE_PLUGIN_ROOT}/report_data.json"

MANDATORY: Output ALL sections below, every single run, no exceptions. Never produce a partial report.

The canonical output template is:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
HARD PREDICT FUTURE · FORESIGHT ENGINE
[Query]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

PREDICTIONS
■ Probable  [[X]/100] [████████████░░░░░░░░] — [one sentence, no hedging]
■ Plausible [[X]/100] [████████░░░░░░░░░░░░] — [one sentence, no hedging]
■ Possible  [[X]/100] [████░░░░░░░░░░░░░░░░] — [one sentence, no hedging]
■ Preferable          [stakeholder analysis below]

Confidence: [X]/100 | Signals: [N] | Horizon: [YYYY–YYYY] | [YYYY-MM-DD]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

SIGNAL PULSE  ·  Evidence collected and classified by type and direction
Supporting [N] [████████████░░░░░░░░] | Opposing [N] [████░░░░░░░░░░░░░░░░] | Wild [N]
Net: [SUPPORTING LEADS / OPPOSING LEADS / NEUTRAL]
Hot zone: [dominant STEEEP×Temporal cell]
Gap: [uncovered STEEEP categories or "None — full coverage"]

STEEEP MATRIX  ·  Cell intensity = signal score (★ hot >1.0  ● warm >0.5  ✗ blind spot)
                    Operational    Strategic      Civilizational
Social              [score] [★/●/·/✗]  [score] [★/●/·/✗]  [score] [★/●/·/✗]
Technological       [score] [★/●/·/✗]  [score] [★/●/·/✗]  [score] [★/●/·/✗]
Economic            [score] [★/●/·/✗]  [score] [★/●/·/✗]  [score] [★/●/·/✗]
Environmental       [score] [★/●/·/✗]  [score] [★/●/·/✗]  [score] [★/●/·/✗]
Ethical             [score] [★/●/·/✗]  [score] [★/●/·/✗]  [score] [★/●/·/✗]
Political           [score] [★/●/·/✗]  [score] [★/●/·/✗]  [score] [★/●/·/✗]

STRUCTURAL DRIVERS  ·  Deep forces shaping the outcome, ranked by signal weight
D1 [Name] — [Force] ([Stability: LOCKED / SHIFTING / FRAGILE])
D2 [Name] — [Force] ([Stability])
D3 [Name] — [Force] ([Stability])

CROSS-IMPACT  ·  How signals interact across time horizons
Operational:    [CONVERGENCE / ISOLATED / BLIND LAYER] — [explanation]
Strategic:      [CONVERGENCE / ISOLATED / BLIND LAYER] — [explanation]
Civilizational: [CONVERGENCE / ISOLATED / BLIND LAYER] — [explanation]
Friction:       [conflicting STEEEP pairs or "None detected"]

HISTORICAL MATCH  ·  Best real-world precedent from analogues search
[Best analogue name] ([similarity]% similar)
Tipped by: [the single event that triggered the shift]
Equivalent now: [EXISTS / PARTIAL / ABSENT]
Validates: [D1 / D2 / D3]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

■ PROBABLE [[X]%] — [Title]
[2–3 sentence narrative. No hedging. Write as if describing the future as it unfolds.]
PROOF: [fact with number or date]
IF: [one condition that must hold for this scenario]
BUT: [one constraint or bottleneck]
DRIVER: D[n]

■ PLAUSIBLE [[X]%] — [Title]
[2–3 sentence narrative]
PROOF: [fact with number or date]
IF: [activation condition]
BUT: [constraint]
DRIVER: D[n]

■ POSSIBLE [[X]%] — [Title]
[2–3 sentence narrative]
PROOF: [fact with number or date]
IF: [activation condition]
BUT: [constraint]
DRIVER: D[n]

■ PREFERABLE — [Title]
[2–3 sentences: desired state as already achieved. No hedging.]
BACKCAST
  Civilizational: [what must be structurally true by the far horizon]
  Strategic:      [what must be built or decided in the medium term]
  Operational:    [what must begin NOW to set the trajectory]
LEVERAGE: [single highest-leverage action today — specific actor, specific action]
DRIVER: D[n]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

PREFERABLE FUTURES  ·  Per major stakeholder — conditions required, constraints, outcomes

For each major player identified in the query, write:
  [Player name]:
    Wins IF  → [specific condition that must be created or occur]
    BUT ONLY → [binding constraint that must also be satisfied]
    ONLY THEN → [the outcome that becomes possible]

Example format:
  Google:
    Wins IF  → Gemini Search integration ships before Q4 2025
    BUT ONLY → Privacy-preserving model survives regulatory scrutiny
    ONLY THEN → Ad revenue model transitions successfully to AI-era search

  Perplexity:
    Wins IF  → Secures browser or device distribution deal
    BUT ONLY → Raises next funding round before 18-month runway expires
    ONLY THEN → Escapes power-user ceiling and reaches mass market

  Users/Consumers:
    Wins IF  → Either player is forced to compete on accuracy, not engagement
    BUT ONLY → Antitrust pressure prevents acquisition of the challenger
    ONLY THEN → Search quality improves and answer reliability increases

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

DECISION GUIDANCE  ·  Deterministic action logic from probabilities + guidance.json
Recommended stance: [act / wait / hedge — from deterministic logic]
Low-regret move:    [action that pays off in multiple scenarios]
Risk trigger:       [highest-scored opposing signal — could invalidate probable if...]

[REGIONAL LENS — [REGION]]
Top multipliers: [steeep/temporal (Xx)] [steeep/temporal (Xx)]
Key local variable: [one sentence on dominant local structural factor]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

METHODOLOGY KEY
Signal scoring    · Reliability tier × recency weight × evidence type → final_score 0–1
STEEEP matrix     · 6 categories × 3 time horizons = 18 cells; ★ hot (>1.0) ● warm (>0.5) ✗ blind
Structural drivers· Signal clusters grouped by STEEEP; top 3 by summed final_score
Cross-impact      · Convergence (≥2 hot zones/layer), Isolated (1), Blind Layer (0)
Historical match  · Claude searches for real precedents; similarity_score 0–100 assessed per analogue
Predictions       · PROBABLE / PLAUSIBLE / POSSIBLE are independent scores (0–100 each, do NOT sum to 100)
                    Futures cone methodology: a scenario can score high on multiple types simultaneously
Confidence        · Signal density (0–40) + evidence balance (0–30) + historical grounding (0–30) − blind spot penalty (0–15)
Decision guidance · Deterministic rule tree over probabilities.json + matrix.json → act / wait / hedge
Preferable futures· Per stakeholder: Wins IF [condition] BUT ONLY [constraint] ONLY THEN [outcome]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Output this to the user exactly. Also save as report_output.json.


ERROR HANDLING

  • Any Python script fails: report exact stderr to the user. Do not proceed.
  • Signals < 10 after all 6 searches: note low signal density in confidence. Continue.
  • No analogues found: use similarity=0 for all. Confidence reflects low historical grounding.
  • Never fabricate data to fill template fields.

© davepoon, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts) in plugins/foresight-intelligence/skills/hard-predict-future of davepoon/buildwithclaude.

  • SKILL.md
  • scripts/confidence_calc.py
  • scripts/decision_guidance.py
  • scripts/input_validator.py
  • scripts/matrix_builder.py
  • scripts/probability_calc.py
  • scripts/regional_context.py
  • scripts/report_formatter.py
  • scripts/signal_scorer.py

Open the folder on GitHubat commit 10bfc43

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in davepoon/buildwithclaude, which our catalogue first saw on October 7, 2026.

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Works with

Questions about Hard Predict Future

What does Hard Predict Future do?

Activate this agent for any future-oriented question that requires deep quantitative analysis, historical precedents, and structured scenario planning. Hard Predict Future is an agent skill from davepoon/buildwithclaude. Activate this agent for any future-oriented question that requires deep quantitative analysis, historical precedents, and structured scenario planning.

When should I use Hard Predict Future?

Hard Predict Future fits situations like: include: Will [X]?; who will win [X]?; what happens to [X]?; prediction requests with high stakes.

How do I install Hard Predict Future in Claude Code?

Run `npx skills add davepoon/buildwithclaude --skill hard-predict-future -a claude-code`. Or copy the skill folder (plugins/foresight-intelligence/skills/hard-predict-future in davepoon/buildwithclaude) into .claude/skills/hard-predict-future in your project. Claude Code loads it when a task matches its description.

How do I install Hard Predict Future in Codex?

Run `npx skills add davepoon/buildwithclaude --skill hard-predict-future -a codex`. Or copy the skill folder (plugins/foresight-intelligence/skills/hard-predict-future in davepoon/buildwithclaude) into .agents/skills/hard-predict-future in your project. Codex loads it when a task matches its description.

Can I use Hard Predict Future in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davepoon/buildwithclaude --skill hard-predict-future -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hard-predict-future, .gemini/skills/hard-predict-future, .github/skills/hard-predict-future and .opencode/skills/hard-predict-future in your project.

What does Hard Predict Future need to run?

Going by SKILL.md and its folder, Hard Predict Future needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Hard Predict Future access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Hard Predict Future safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Hard Predict Future use?

Hard Predict Future is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Hard Predict Future use?

About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Hard Predict Future?

Skills that share tags, products or a category with Hard Predict Future: Tw Legal RAG (aa0101181514/tw-legal-rag, 327 stars), USPTO Patent and Trademark Data (davila7/claude-code-templates, 32k stars), Moot Court Simulation Builder (cat-xierluo/legal-skills, 713 stars) and Ley Ar (majiayu000/claude-skill-registry, 666 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hard Predict Future?

davepoon (a GitHub user) maintains it in davepoon/buildwithclaude, which has 3,604 GitHub stars. The repository holds 245 skills in this directory. The repository was last updated on October 6, 2026.

Source: davepoon/buildwithclaude on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.