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 74 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 — displayname, cadence, ~estimatedminutes,

api_profile. State plainly what it does and when to run it.

• Routing diagnostics — read routing_diagnostics.status. For

ambiguous, name every candidate_personas entry and explain that the ordered first match was selected. For fallback, state that no persona matched and ask the user to rephrase; never present the beginner fallback as an exact intent match.

• 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).

manifeststatus: 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. manifeststatus: deferred means no manifest yet (e.g. honest-gap categories); the recommendation is workflow-based only.

• No-API vs API — read noapipath: 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. (noapi is the request flag — whether no-API mode was active — not whether the path is free; always narrate noapi_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.

• Operational roles — narrate the operational_roles entry for every

skill in the setup bundle or honest-gap suggestion. Explain standalone rationale when present; keep enum values unchanged.

• Always read the rationale array and explain why this was recommended.

Step 4 — Explain the setup path

Read references/setuppaths.md and walk the user through installing setupbundle — the recommender's deterministic install union over the primary skillset and every secondary workflow (so nothing is dropped for a multi-workflow recommendation). Enumerate setupbundle.required → recommended → optional, cite setupbundle.sources to explain why each skill is needed, and name skillset.manifest.relatedworkflows for how the bundle is run. Narrate skillset.manifest (when present) as "what the recommended skillset is". On an honest gap install suggestedskills. 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/buildsnapshot.py — regenerates assets/metadatasnapshot.json

from the SSoT; --check guards drift (pre-commit + CI).

• scripts/intent_benchmark.py — validates the fail-closed bilingual routing

corpus, persona shadowing contracts, and metamorphic invariants.

• 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.

• assets/intentbenchmarkv1.json — 211 explicitly labeled EN/JA routing

cases with 1.0 precision/recall and per-persona/workflow coverage gates.