ifuryst/aifi · Archived

investment-thesis-synthesis

Synthesize archived AIFi evidence into an investment thesis, risk register, bull/base/bear scenarios, open questions, watch items, and decision frame. Use after collecting filings, news, earnings, financials, market signals, and competitive context for a target.

First seen Aug 8, 2026

Installation

$ npx skills add ifuryst/aifi --skill investment-thesis-synthesis

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 22
License LICENSE
Default branch main
Open issues 0
Status Archived

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,715 B
  • docs SUMMARY.md 297 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2 installs

SKILL.md

Investment Thesis Synthesis

Use this skill at the end of a research workflow. It should transform evidence into a decision-support artifact while keeping facts, interpretation, and action options separate.

Workflow

  1. Load the target profile, archive index, and relevant evidence notes.
  2. Check source coverage before writing the thesis.
  3. Extract high-confidence facts and unresolved contradictions.
  4. Build bull, base, and bear cases from evidence.
  5. Create a risk register with probability, impact, evidence, and mitigants when

known.

  1. Produce watch items and catalysts that can update the thesis later.
  2. Save the artifact under

research/targets/<target>/artifacts/decision-frames/ or research/targets/<target>/artifacts/memos/.

Read references/thesis-frame-template.md before writing the final artifact.

Output

Return:

  • evidence coverage summary
  • bull, base, and bear cases
  • risk register
  • open questions
  • watchlist triggers
  • decision frame with user-controlled options
  • archive files created or updated

Quality Gate

Before finishing:

  • every material claim links to evidence or is labeled as inference
  • missing evidence is visible in the coverage summary
  • facts and interpretation are separated
  • no single opaque buy/sell recommendation is presented as truth
  • decision language preserves user control and avoids certainty about returns