Tw Legal RAG
aa0101181514/tw-legal-rag
Retrieve real Taiwan court judgments with verifiable citations before answering any question about Taiwan law or case law.
Activate this agent for any future-oriented question that requires deep quantitative analysis, historical precedents, and structured scenario planning.
$ npx skills add davepoon/buildwithclaude --skill hard-predict-future -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install davepoon/buildwithclaude hard-predict-future --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "hard-predict-future" agent skill from https://github.com/davepoon/buildwithclaude/tree/main/plugins/foresight-intelligence/skills/hard-predict-future into .claude/skills/hard-predict-future/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hard-predict-future", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/davepoon/buildwithclaude/tree/main/plugins/foresight-intelligence/skills/hard-predict-futureType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add davepoon/buildwithclaude --skill hard-predict-future -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install davepoon/buildwithclaude hard-predict-future --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davepoon/buildwithclaude.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/foresight-intelligence/skills/hard-predict-future .agents/skills/hard-predict-future && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hard-predict-future" agent skill from https://github.com/davepoon/buildwithclaude/tree/main/plugins/foresight-intelligence/skills/hard-predict-future into .agents/skills/hard-predict-future/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hard-predict-future", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add davepoon/buildwithclaude --skill hard-predict-future -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install davepoon/buildwithclaude hard-predict-future --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davepoon/buildwithclaude.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/foresight-intelligence/skills/hard-predict-future .cursor/skills/hard-predict-future && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "hard-predict-future" agent skill from https://github.com/davepoon/buildwithclaude/tree/main/plugins/foresight-intelligence/skills/hard-predict-future into .cursor/skills/hard-predict-future/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hard-predict-future", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/davepoon/buildwithclaude.git --path plugins/foresight-intelligence/skills/hard-predict-future--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add davepoon/buildwithclaude --skill hard-predict-future -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install davepoon/buildwithclaude hard-predict-future --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davepoon/buildwithclaude.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/foresight-intelligence/skills/hard-predict-future .gemini/skills/hard-predict-future && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "hard-predict-future" agent skill from https://github.com/davepoon/buildwithclaude/tree/main/plugins/foresight-intelligence/skills/hard-predict-future into .gemini/skills/hard-predict-future/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hard-predict-future", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install davepoon/buildwithclaude hard-predict-futureInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add davepoon/buildwithclaude --skill hard-predict-future -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/davepoon/buildwithclaude.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/foresight-intelligence/skills/hard-predict-future .github/skills/hard-predict-future && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "hard-predict-future" agent skill from https://github.com/davepoon/buildwithclaude/tree/main/plugins/foresight-intelligence/skills/hard-predict-future into .github/skills/hard-predict-future/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hard-predict-future", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add davepoon/buildwithclaude --skill hard-predict-future -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install davepoon/buildwithclaude hard-predict-future --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/davepoon/buildwithclaude.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/foresight-intelligence/skills/hard-predict-future .opencode/skills/hard-predict-future && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "hard-predict-future" agent skill from https://github.com/davepoon/buildwithclaude/tree/main/plugins/foresight-intelligence/skills/hard-predict-future into .opencode/skills/hard-predict-future/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hard-predict-future", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
hard-predict-futureActivate 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. 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.
12 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 10bfc43. It shows what the files ask for, not the result of running them.
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.
Ships 8 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from davepoon/buildwithclaude at commit 10bfc43, republished under its MIT licence (© davepoon). 691 words, ~4,178 tokens.
.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.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: ${CLAUDE_PLUGIN_ROOT}/skills/hard-predict-future/scripts/
python "${CLAUDE_PLUGIN_ROOT}/skills/hard-predict-future/scripts/input_validator.py" "[query]"Read exact stdout.
valid=false: output the rejection message and STOP.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:
State the inferred horizon (e.g. "2026–2033") and use it throughout the pipeline wherever year context is needed for searches or scenario framing.
Use web_search. Run 6 searches in 2 batches.
Batch 1 (current state + growth + barriers):
"[query] current status [year]""[query] growth data market size statistics""[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:
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: ${CLAUDE_PLUGIN_ROOT}/signals.json
python "${CLAUDE_PLUGIN_ROOT}/skills/hard-predict-future/scripts/signal_scorer.py" "${CLAUDE_PLUGIN_ROOT}/signals.json"Wait for exact stdout JSON. Script writes scored_signals.json. Use returned data exactly.
Read scored_signals.json. Group signals by STEEEP category. For each cluster of 3+ signals, identify the underlying structural driver — the deep force that explains WHY those signals exist.
Extract exactly 3 top drivers, ranked by sum of final_scores of signals they explain.
For each driver:
Output format:
D1 [Name] — [Force] | Temporal: [layer] | Stability: [tier]
D2 [Name] — [Force] | Temporal: [layer] | Stability: [tier]
D3 [Name] — [Force] | Temporal: [layer] | Stability: [tier]Save drivers as part of report_data.json later in Step 11.
python "${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.
Read matrix.json. For each temporal layer (Operational / Strategic / Civilizational):
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]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: ${CLAUDE_PLUGIN_ROOT}/analogues.json
python "${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.
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.
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.
Write four scenarios. Each must cite its structural driver.
PROBABLE, PLAUSIBLE, POSSIBLE — each:
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]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": "", "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.
similarity=0 for all. Confidence reflects low historical grounding.© davepoon, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 8 other files (scripts) in plugins/foresight-intelligence/skills/hard-predict-future of davepoon/buildwithclaude.
Open the folder on GitHubat commit 10bfc43
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.
Hard Predict Future next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Hard Predict Future this skilldavepoon/buildwithclaude | 3.6k | 1 repos | ~4.2k | Automated safety check: Pass | MIT | |
| Tw Legal RAGaa0101181514/tw-legal-rag | 327 | — | ~580 | Automated safety check: Pass | Custom licence | |
| USPTO Patent and Trademark Datadavila7/claude-code-templates | 32k | 12 repos | ~4.6k | Automated safety check: Pass | MIT | |
| Moot Court Simulation Buildercat-xierluo/legal-skills | 713 | — | ~1.4k | Automated safety check: Pass | CC-BY-NC-4.0 | |
| Ley Armajiayu000/claude-skill-registry | 666 | 1 repos | ~709 | Automated safety check: Pass | MIT | |
| CLI DeveloperJeffallan/claude-skills | 12k | 2 repos | ~1.2k | Automated safety check: Pass | MIT |
aa0101181514/tw-legal-rag
Retrieve real Taiwan court judgments with verifiable citations before answering any question about Taiwan law or case law.
davila7/claude-code-templates
Searches USPTO patent and trademark data through its APIs: patent search, PEDS examination history, assignments, citations, office actions and TSDR status.
cat-xierluo/legal-skills
Chinese-language skill that organizes a case file into a multi-role mock trial with judge, parties and clerk, producing a transcript, issue review and a to-strengthen list.
majiayu000/claude-skill-registry
Search Argentine legal databases (SAIJ, JUBA, CSJN, JUSCABA) for jurisprudence, legislation, case summaries, and doctrine using the ley CLI.
Jeffallan/claude-skills
Walks through designing, building and polishing a command-line tool: user workflow and command hierarchy, implementation in commander, click, typer or cobra, completions and cross-platform testing.
ClickHouse/ClickHouse
Analyze a jemalloc (or other) allocation profile in collapsed stack format.
davepoon/buildwithclaude
A skill your agent uses when the user asks to "analyze video", "watch this video", "what happens in this video", "describe this clip", "review this footage", "classify these videos", "compare…
davepoon/buildwithclaude
Build, update, and apply iOS design specifications using Apple Human Interface Guidelines (HIG) source data.
davepoon/buildwithclaude
Download YouTube videos with customizable quality and format options.
davepoon/buildwithclaude
Discover Atlas Cloud image and video models, inspect their live schemas, and submit one confirmed media generation request with bounded GET polling.
davepoon/buildwithclaude
Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives.
davepoon/buildwithclaude
面向没有编程经验的用户,把想法做成可试用的浏览器插件,并完成检查、商店材料、审核提交和上线验证;也用于继续已有插件、排错和发布新版。用户说“帮我做个插件”“把插件上架”“继续我的插件”时使用。普通网站开发、仅查询插件知识不触发。
Categories
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.
Hard Predict Future fits situations like: include: Will [X]?; who will win [X]?; what happens to [X]?; prediction requests with high stakes.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.