Token Research

token-research (CLI-driven EVM token analysis)

Drive the token-research CLI to produce on-chain-grounded, provenance-tracked token analysis. The CLI is the engine; this skill routes the request to the right command(s) and reads back the structured output.

Step 0: Route first (data regime)

Apply this BEFORE running anything:

EVM token with a known or derivable address (Ethereum / Base / Arbitrum / Optimism)?
 ├─ yes → use this skill (below)
 └─ no  → STOP. Hand off to the `cpd-crypto-analysis` skill:
            • non-EVM L1 (HBAR, SOL, ATOM, TIA, TON, native BTC…)
            • pre-TGE / no token yet (e.g. a points program)
            • a purely qualitative trust / team / governance question

The CLI is EVM-first and token-centric. Its refusals are the hand-off signal, not a failure. See docs/skills-architecture.md.

Setup (once)

pip install -e .[dev]            # installs the `token-research` console script
# optional, for momentum/portfolio price features:
pip install -e ".[ccxt]"

Run with the console script token-research <command> …, or without installing via PYTHONPATH=src python3 -m token_research <command> …. Add TOKEN_RESEARCH_OFFLINE=1 to skip all network calls (deterministic, for smoke checks).

Always pass --address and --chain when known — it pins the exact contract and skips resolver guesswork (squatter-token defense). Default output is JSON; deep-report also supports --format markdown.

Modes (pick by purpose × depth)

Mode: quick — triage in seconds

For "is this worth a closer look?" Run the two scoring systems and read the headlines:

token-research score <TOKEN> --chain <CHAIN> --address <ADDR>   # 6-pillar structural quality 0–100
token-research risk  <TOKEN> --chain <CHAIN> --address <ADDR>   # CPD 2.0 risk %/tier

Report: composite score + the weakest pillar, risk tier, and any warnings / coverage_gaps. A high score with many coverage gaps is a weak read — say so.

Mode: deep — full dossier

For a real write-up. One command runs every collector + the 6-pillar score + narrative:

token-research deep-report <TOKEN> --chain <CHAIN> --address <ADDR> --format markdown

Read the output structure: scores (pillars + composite + coverage_ratio), narrative, evidence_summary, per-module metrics, warnings, coverage_gaps. Persisted to .token-research/reports/ unless --no-persist.

When a pillar surprises you, drill in with the targeted command: compare (effective circulation / protocol-owned vs EOA), holders (concentration, gini, nakamoto), liquidity (depth, LP locks, slippage), unlocks (cliff overhang; add --with-premium for curated Tokenomist data), locks / staking (custody fingerprinting), yields (TVL, fees, revenue model).

Mode: compare — several tokens side by side

The compare command is a single-token effective-circulation view, so for head-to-head run the same command per token and diff:

for each token: token-research score <TOKEN> --chain <CHAIN> --address <ADDR>

Present a table: composite, the driving pillars, and risk tier per token. Reconcile differences — two tokens with the same composite can have very different pillar shapes (see docs/walkthrough.md).

Reading the output (the contract)

Every command returns a CommandResult: metrics, sources, warnings, coverage_gaps, resolved_identity, generated_at. Three rules:

1. Trace, don't trust — every metric carries its sources (on-chain RPC > explorer > vendor).
2. Coverage gaps are signal — they list what couldn't be answered and how to fill it; the composite is penalized for them. Surface them, never hide them.
3. Pillars over composite — report which pillar is weak, not just the number. Pillar definitions, thresholds, and market-dependency: docs/scoring-overview.md and articles/01-estimation-methodology.md.

Compose with CPD for a complete report

For a full "is this a good, trustworthy token?" answer, run this skill for the on-chain numbers, then the cpd-crypto-analysis skill for the sourced trust layer, and merge. The CLI risk and the CPD score share one source of truth (docs/risk_revenue_estimation.md); where they diverge, that divergence is itself a finding worth reporting.

Reference

• Command surface + flags + output schema: docs/cli-reference.md
• Scoring systems (score / risk / signal / fairlaunch): docs/scoring-overview.md
• End-to-end walkthrough: docs/walkthrough.md
• Skill routing rationale: docs/skills-architecture.md