Trading Skills Navigator

The interactive on-ramp for this repository. It turns a user's goal into a concrete recommendation: which workflow to run, which skillset (skills-index category) it belongs to, the API requirement, and the setup path for Claude Web App or Claude Code.

A new user faces 70 skills + 11 workflows with no router. This skill is that router. It is deterministic — a Python recommender (scripts/recommend.py) consumes the repo metadata; this SKILL.md narrates the result conversationally.

When to Use

• The user expresses a trading/investing goal and asks where to start or which skill/workflow to use ("どれを使えばいい", "where do I start").
• The user asks what works without paid API keys.
• The user wants the no-API vs API path separated, or a beginner path.
• The user describes a persona ("part-time swing trader", "dividend investor", "I want to short", "I want to backtest ideas") and needs routing.

Do not use this skill to execute trades, place orders, or auto-run other skills. It recommends and explains only.

Workflow

Step 1 — Capture the goal and constraints

From the user's message, extract:

• The natural-language goal (verbatim is fine).
• Optional constraints: no-API only? a daily time budget (15m/30m/60m/90m)? experience level (beginner/intermediate/advanced)?

Ask at most one brief clarifying question only if the goal is empty or has no discernible intent. Otherwise proceed — the recommender degrades gracefully.

Step 2 — Run the recommender

python3 skills/trading-skills-navigator/scripts/recommend.py \
  --query "<the user's goal, verbatim>" \
  --format json
  # optional: --no-api  --time-budget 15m|30m|60m|90m|any
  #           --experience beginner|intermediate|advanced

• In Claude Code the script reads the repo-root SSoT (skills-index.yaml + workflows/*.yaml) automatically.
• In the Claude Web App there is no repo root; the script transparently falls back to the bundled assets/metadata_snapshot.json. The recommendation is byte-identical in both environments — no behavior change for the user.

Step 3 — Narrate the result conversationally

Parse the JSON and explain, in the user's language:

• Primary workflow — display_name, cadence, ~estimated_minutes, api_profile. State plainly what it does and when to run it.
• Secondary workflows — if any, how they relate (e.g. "run the regime check first, then this when it allows risk").
• Skillset — the skillset.id (skills-index category). manifest_status: active means a curated skillsets/<id>.yaml bundle ships for this category (market-regime, core-portfolio, swing-opportunity, trade-memory) — mention it as the install bundle for the recommended workflow. manifest_status: deferred means no manifest yet (e.g. honest-gap categories); the recommendation is workflow-based only.
• No-API vs API — read no_api_path: true → the entire recommended path works without paid API keys (state this plainly); false → tell the user which paid key(s) the path needs; null → honest gap, no path. (no_api is the request flag — whether no-API mode was active — not whether the path is free; always narrate no_api_path.) If a workflow was excluded under --no-api, surface the rationale entry naming the paid integration (e.g. "swing-opportunity-daily needs FMP").
• Honest gap — if honest_gap is true there is no shipped workflow for this intent. Say so directly, then present suggested_skills from the relevant category and relay the note. Never invent a workflow.
• Always read the rationale array and explain why this was recommended.

Step 4 — Explain the setup path

Read references/setup_paths.md and walk the user through installing setup_bundle — the recommender's deterministic install union over the primary skillset and every secondary workflow (so nothing is dropped for a multi-workflow recommendation). Enumerate setup_bundle.required → recommended → optional, cite setup_bundle.sources to explain why each skill is needed, and name skillset.manifest.related_workflows for how the bundle is run. Narrate skillset.manifest (when present) as "what the recommended skillset is". On an honest gap install suggested_skills. Do this for whichever environment the user is in (Claude Web App .skill upload, or Claude Code folder copy); call out any paid API keys those skills need.

Step 5 — Point to the learning loop

Close by pointing the user at trader-memory-core and the trade-memory-loop / monthly-performance-review workflows so every recommended path feeds the Plan → Trade → Record → Review → Improve loop.

Output Format

The JSON the recommender emits (stable, idempotent, sort_keys):

Resources

• scripts/recommend.py — the deterministic recommender (single source of truth for routing).
• scripts/build_snapshot.py — regenerates assets/metadata_snapshot.json from the SSoT; --check guards drift (pre-commit + CI).
• references/intent_routing.md — the persona table, the 10-question contract, the --no-api credential rule, and scoring tie-breaks.
• references/setup_paths.md — Claude Web App vs Claude Code setup steps.
• assets/metadata_snapshot.json — generated SSoT digest for the Web App fallback. Never edit by hand; run build_snapshot.py.