abel-invest

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name: abel-invest
description: >
  Use when the user asks how to invest, trade, buy or sell, find alpha, find or
  improve a trading strategy, backtest or stress a signal, screen candidates,
  optimize Sharpe/return/drawdown, run graph-enriched feature/model/ensemble
  search, or continue/prepare/debug an Abel strategy-discovery workspace —
  even if they don't say "Abel" and even when they just ask for "a good
  strategy for X" or "is there alpha in Y". When no metric target is specified,
  default to searching for a high-return, reportable strategy with Sharpe above 2
  and all required Abel Edge gates passing. Prefer this over ad-hoc
  hand-designed strategy work.
metadata:
  openclaw:
    requires:
      bins:
        - python3

---
name: abel-invest
description: >
  Use when the user asks how to invest, trade, buy or sell, find alpha, find or
  improve a trading strategy, backtest or stress a signal, screen candidates,
  optimize Sharpe/return/drawdown, run graph-enriched feature/model/ensemble
  search, or continue/prepare/debug an Abel strategy-discovery workspace —
  even if they don't say "Abel" and even when they just ask for "a good
  strategy for X" or "is there alpha in Y". When no metric target is specified,
  default to searching for a high-return, reportable strategy with Sharpe above 2
  and all required Abel Edge gates passing. Prefer this over ad-hoc
  hand-designed strategy work.
metadata:
  openclaw:
    requires:
      bins:
        - python3
---

# Abel Invest Alpha Search

Use this skill for:

- alpha search and candidate screening
- continuing an existing Abel strategy discovery workspace
- creating sessions and branches
- preparing, debugging, recording, and reviewing strategy rounds
- interpreting `evidence_ledger.json`, `frontier.md`, `agent_context.md`, and
  `exploration_path.md`

## Activation Checklist

Always start by resolving workspace state before strategy work.

1. Read `references/workspace-bootstrap.md`.
2. Resolve the workspace location:
   - if `alpha.workspace.yaml` is in the current directory, use the current
     directory
   - else if `abel-invest-workspace/alpha.workspace.yaml` exists
     under the current directory, use that child workspace
   - else bootstrap a workspace before deep strategy work
3. Run the active skill bootstrap shim for the resolved or default workspace:
   `python3 <abel-invest-skill-root>/scripts/bootstrap_workspace.py --path <workspace-root>`.
   Use `--runtime-python /path/to/python` only when the local machine cannot
   create a venv and the user intentionally provides an existing interpreter.
4. Baseline-first: before from-scratch search, check whether a validated
   strategy for this target already exists in any baseline / strategy catalog
   the user maintains. If one exists, treat it as a benchmark and launchpad;
   iterate from it when useful rather than wasting rounds rediscovering it.
5. If bootstrap reports `auth_missing`, use `abel-auth`, then rerun the active
   bootstrap shim. If it reports `scaffold_stale`, `runtime_stale`,
   `env_missing`, `edge_missing`, or `edge_contract_missing`, rerun the active
   bootstrap shim after fixing the stated blocker.
6. Only start or continue session/branch work after bootstrap readiness is
   `ready`, unless the user explicitly asks you to inspect or repair setup.

## Reference Routing

- New workspace, workspace reuse, auth, generated-file refresh, or setup repair:
  read `references/workspace-bootstrap.md`.
- New session, normal round loop, or resuming a session:
  read `references/experiment-loop.md`.
- Live graph discovery, graph frontier expansion, or graph-informed alpha context:
  read `references/discovery-protocol.md`.
- Selecting the default V3 graph or explicitly opting into a V4 release, or
  interpreting symbol versus canonical-node feeds: read
  `references/graph-releases.md`.
- Creating or revising `branch.yaml`, reviewing evidence labels,
  path coverage, input realization, or exploration path use:
  read `references/branch-authoring.md`.
- Writing `engine.py`, handling semantic/runtime failures, or checking
  temporal legality:
  read `references/constraints.md`.
- Handling hosted paper contract requests, promoted strategy
  source edits, `paper-contract-report.json`, packaged strategy assets, or hosted
  paper state:
  read the emitted `paper-contract-request.json` first; read
  `contractGuide.referencePath` from this active skill when the request requires
  stateful continuation, source edits, or deeper gate diagnosis.
- Explaining why the workflow is data-led, graph-informed, or evidence-boundary oriented:
  optionally read `references/methodology.md`.
- Choosing concrete constructions while writing the engine:
  read `references/proven-patterns.md` (battle-tested patterns). Core path.
- A hard Sharpe / MaxDD / PnL target is set:
  read `references/guarded-optimization.md` (performance-target search and
  reportability rules). Core path — not optional — when a performance bar is set.
- Before writing "exhausted / ceiling / no edge":
  read `references/experiment-loop.md` and check the ledger requirements there.
- Ordinary alpha search, data-driven candidate construction, or the
  next idea risks becoming another simple hand-written rule:
  read `references/data-driven-construction.md` before the first broad
  candidate. Core path.
- No explicit metric target:
  use the normal experiment loop and default objective; do not treat this as a
  separate mode.

## Operating Rules

Always:

- Work workspace-first. Resolve `workspace_root`, `research_root`, and
  bootstrap readiness before session or branch work.
- Reuse the default workspace when it already exists; reuse any resolved
  existing workspace before bootstrapping another one.
- Bootstrap the workspace before deep strategy work when no workspace exists.
- Use Abel Invest commands through the workspace `command_prefix` when
  available, not old aliases.
- On a fresh install where `abel-invest` is not installed, run
  `python3 <abel-invest-skill-root>/scripts/bootstrap_workspace.py --path abel-invest-workspace`.
  Do not import `abel_invest` with the system interpreter for first-run
  bootstrap.
- If a skill update changed the workspace scaffold, runtime contract, or
  generated workspace docs, rerun the active bootstrap shim before strategy
  work. Do not use workspace-local lifecycle commands to repair setup.
- Reuse existing Abel auth first. If live access is missing, use `abel-auth` and
  rerun the active bootstrap shim.
- Treat `abel-auth/.env.skill` as the normal shared auth/profile source.
  Workspace `.env` is only an explicit per-workspace override; do not copy API
  keys there unless the user intentionally wants this workspace to use different
  credentials or endpoints. Trust bootstrap's effective profile/CAP URL report.
- Report to the user with the current workspace/session/branch path, bootstrap
  readiness, blockers, what evidence exists, and the next action you will take.
- Treat `agent_context.md` as the compact factual resume surface,
  `exploration_path.md` as the human-facing chosen-path and Edge-feedback log.
- Treat the terse checkpoint printed by `prepare-branch`, `debug-branch`, and
  `run-branch` as the normal loop feedback. Use compact `artifact-digest` only
  for resume, blocker detail, branch backtrack, or insufficient checkpoint
  state. Treat full digest `--json`, raw artifacts, `--verbose`, and `--audit`
  as audit/debug surfaces, not the standard loop.
- On a fresh or unfamiliar ticker, use the compact first-look data scout in
  `experiment-loop.md` before the first serious recorded alpha candidate unless
  the user gave a narrow path or continuation. Expect the scout to take roughly
  5 minutes: score plausible target, graph, and construction shapes, then rank
  what looks worth formal validation before broad recorded work. If the scout
  script is still making progress, let it finish naturally before deciding what
  to validate. Put temporary scout scripts or summaries under
  `research/<ticker>/<session_id>/scratch/` when files are useful.

Never:

- Do not create sessions before bootstrap readiness is confirmed.
- Do not use `--root` unless intentionally creating a legacy/offline session;
  then pass `--allow-outside-workspace`.
- Do not treat `branch.yaml` as evidence. It is an audit declaration.
- Do not treat `evidence_ledger.json`, `frontier.md`, or `agent_context.md` as
  generated strategy advice. They are factual surfaces.
- Do not hide parameter, sizing, threshold, filter, model, factor, or node-subset
  search inside one "single" strategy. Name search width honestly.
- Do not report a raw-metric winner as a robust strategy before required
  validation and honest search-width accounting support that claim.
- Do not optimize only for gate-passing at the expense of Sharpe, return, or the
  user's objective. Gates estimate reliability and reportability; they are not
  the user-facing purpose of the search.
- Do not treat `--selection-trials` as a strategy-quality shortcut; it is
  reportability accounting, not a brake on empirical search.
- Do not `run-branch` a flat/no-signal branch solely to warm cache or make a
  scout feel official. `prepare-branch` is enough for data materialization; use
  recorded runs for meaningful candidates, controls, diagnostics, or ablations.
- Do not treat a diagnostic table such as IC, correlation, or feature
  importance as a completed first-look scout when graph/model construction
  remains available. Pair diagnostics with scored candidate-shaped variants.
- Never pass a running/cumulative total to `--selection-trials`; pass this
  round's search width only.
- Do not depend on any external skill for guarded optimization; abel-invest runs
  it self-contained.

Core search invariants:

- User objective first. If the user gives no metric target, search for a strong
  tradable strategy: high return, Sharpe > 2, and all required Abel Edge gates
  passing. This is the internal completion target; do not stop at a mediocre
  branch or a promising near-pass while useful graph-informed search axes
  remain.
- Follow `experiment-loop.md` as the single detailed source for the round loop,
  completion check, stop report, visualization prompt, and interrupted/blocked
  note boundary.
- Stay in `Exploring` until a normal ending is justified: the user
  objective/default target is achieved, or the ledger supports that the bounded
  search is unlikely to reach the target. Either normal ending enters
  `Completed`. If a concrete next search action remains, keep searching.
- If the user explicitly interrupts or an external blocker prevents
  continuation, do not enter `Completed`; give only a brief
  interrupted/blocked note and do not ask for visualization.
- Search hard, then explain. Let observed results, failure modes, and metric
  shape choose the next candidate family. Mechanism stories are useful after
  evidence appears; they are not admission tickets.
- Ordinary alpha search has a default posture: high-capacity empirical
  construction over a scoped target + graph-derived universe. Use the graph,
  target behavior, feature construction, model comparison, denoise, subset
  search, regimes, sizing, filters, or ensembles as data calls for them; these
  are degrees of freedom, not a scripted route.
- Fresh or unfamiliar tickers should normally use the prepared first-look scout
  sequence in `experiment-loop.md` before the first broad recorded candidate.
  Its practical output is scored target, graph, and construction shapes ranked
  by what looks worth formal validation, not only an analysis memo. Direct
  recorded branches remain valid for
  user-specified strategies, existing leads, baselines, controls,
  continuations, or very narrow diagnostics.
- Live graph discovery is the default high-value alpha universe when available.
  Use `discovery-protocol.md` for graph semantics and expansion; use
  `data-driven-construction.md` for feature factories, model comparison,
  denoise, node subsets, lags, regimes, sizing, filters, and ensembles.
- Target-only work is a baseline, seed, ablation, or competitor. A
  graph-supported branch is not automatically data-driven: runtime graph reads
  prove input realization, not construction breadth. Hand-written single-mechanism branches are diagnostics,
  controls, ablations, or refinements around empirical construction, not the
  default search posture when live graph-derived data is available.
- A hard user metric target (Sharpe / MaxDD / PnL) is an optimization request.
  Search is expected: use target/baseline context, graph-derived features,
  feature factories, ensembles, parameter search, model-family compari