wagf-quickstart

name	wagf-quickstart
description	First-time WAGF setup walkthrough — environment check, smoke test, first experiment, and handoff to the lifecycle skills. Use when the user says "I just cloned WAGF", "set up WAGF", "first WAGF run", "I'm new to this", "where do I start with WAGF", or opens a Claude Code session in a freshly-cloned WAGF repo without a clear task.

WAGF Quickstart — First-Time Setup

The single entry-point skill for a researcher who just cloned this repo and wants to be productive within ~40 minutes. This skill itself does very little; it orchestrates four phases and hands off to the existing lifecycle skills at the right moments.

Researcher progress is tracked in .wagf-quickstart-status.json so a returning user resumes from the last completed phase rather than starting over.

When to Use

Load this skill the moment a user opens a session in a WAGF repo and says any of:

• "I just cloned WAGF, help me set this up."
• "Set up WAGF."
• "First WAGF run."
• "I'm new to this — where do I start?"
• "Help me get WAGF working."

Also load this skill PROACTIVELY when:

• The session opens in a directory containing broker/INVARIANTS.md AND no .research/ folder AND no .wagf-quickstart-status.json — this signals a fresh clone with no work done yet.

Do NOT use this skill for:

• An experienced WAGF user who knows what they want → load the matching lifecycle skill directly (wagf-experiment-designer, llm-agent-audit-trace-analyzer, wagf-verification-ladder, model-coupling-contract-checker, abm-reproducibility-checker).
• Generic "how do I run a Python project" questions → defer to whatever the user actually wants.

The four-phase workflow

Each phase has an entry condition, a runnable artefact, and a clear hand-off rule. Skip nothing; refuse to advance if the prior phase did not produce its expected output.

Phase 1 — Environment check (~3 min)

Goal: confirm the user can run any WAGF script at all.

Run:

python .claude/skills/wagf-quickstart/scripts/check_env.py

What it validates:

• Python ≥ 3.10
• pip install -r requirements.txt resolves (probe-mode; doesn't install)
• Ollama daemon is reachable at http://localhost:11434
• At least one supported model is pulled. gemma3:4b is the recommended onboarding model (small enough to run on a CPU laptop in a pinch, large enough to produce sensible WAGF behaviour).

Outputs:

• Verdict: GREEN / YELLOW / RED.
• Numbered remediation list if YELLOW or RED (e.g., "1. Install Ollama: https://ollama.com/download"); "2. Run: ollama pull gemma3:4b").
• Records phase-1 status to .wagf-quickstart-status.json.

Refuse to advance if RED. Show the remediation list and wait for the user to fix.

Phase 2 — Smoke test (~5 min)

Goal: confirm the broker pipeline produces meaningful behavioural diff (governed vs ungoverned).

Run (in order):

python examples/quickstart/01_barebone.py
python examples/quickstart/02_governance.py

01_barebone.py runs without governance; 02_governance.py runs the same scenario through the full broker pipeline. Both are short (~5 agents × 2 years).

What to show the user after the runs:

• A 3-line diff: skill distribution from 01_ vs 02_ (typically governance reduces the increase / inaction rate).
• Confirm examples/quickstart/results/<run_dir>/simulation_log.csv was written.

Refuse to advance if either script crashes or simulation_log.csv is missing/empty. Inspect stdout for the actual error and recommend the fix from references/troubleshooting.md.

Record phase-2 status with the path of each run dir, so Phase 4's analyser can find them later.

Phase 3 — First real experiment (~30 min planning + multi-hour run)

Goal: turn the user's research question into a runnable matrix.

This phase is delegated to the wagf-experiment-designer skill. Do NOT replicate its workflow here. Instead:

1. Ask the user (in plain language): "What question do you want to answer with WAGF? E.g., 'Does governance reduce hallucinated actions in flood adaptation?'"
2. Pre-fill defaults appropriate for a first-time user:
   • Domain: irrigation or flood (ask).
   • Models: 1 model, default gemma3:4b (already pulled in Phase 1).
   • Conditions: [strict, disabled].
   • Seeds: 3 (smallest meaningful paired-t; bump to 5 later).
   • Time horizon: domain default (irrigation 42 yr, flood 10 yr).
   • Social-media propagation (v0.5.1+, opt-in): OFF by default and recommended OFF for first runs. Researchers studying narrative diffusion or social-influence effects can enable it later via the two-layer flag (global_config.social_feeds.enable in YAML AND the DomainPack social_feeds_default_enabled() hook). When OFF, runs are byte-identical to pre-v0.5.1 output. Architecture: .research/social_tier_injection_reference.md §9–§10 (note: §10.5 — the cross-channel dedup module ships as infrastructure with no live consumer at v0.5.1; provider integration is deferred).
3. Hand off explicitly: "Now loading wagf-experiment-designer with these defaults — confirm or override."
4. After the matrix is written, wagf-experiment-designer produces .research/wagf_experiment_matrix.yml, .research/metrics_plan.md, and .research/run_plan.md. The researcher then runs the bat in run_plan.md.

Refuse to advance if the user has not chosen a domain or has not provided a research question (even one sentence).

Record phase-3 status with the .research/ artefact paths.

Phase 4 — First analysis (~5 min after run completes)

Goal: turn the run output into paper-ready governance metrics.

This phase is delegated to the llm-agent-audit-trace-analyzer skill.

1. Wait for simulation_log.csv to appear in the run output dir (the user runs the bat themselves; this skill does not babysit the LLM run).
2. When the user returns and says "the run is done" or "analyse the results", hand off: "Now loading llm-agent-audit-trace-analyzer to compute governance metrics."
3. After the analyser writes analysis/governance_summary.md, point the user at the next three skills:
   • wagf-verification-ladder — if they built or modified a domain and need the setup-test-smoke-real-LLM done ladder.
   • model-coupling-contract-checker — if they added an external model.
   • abm-reproducibility-checker — before they submit a paper.

Record phase-4 status with the analysis/ artefact paths.

State file

The skill maintains .wagf-quickstart-status.json at the repo root:

{
  "phase_1_env": {"completed": true, "verdict": "GREEN", "ts": "..."},
  "phase_2_smoke": {"completed": true, "ts": "...", "run_dirs": ["..."]},
  "phase_3_experiment": {"completed": false, "matrix_path": null},
  "phase_4_analysis": {"completed": false, "report_path": 

When invoked, the skill reads this file FIRST and resumes at the first incomplete phase rather than starting over.

Refusal Protocol

The skill MUST refuse to:

1. Pretend the environment is fine when check_env.py reports RED or any required tool is missing. Show the remediation list and stop.
2. Skip phases. No Phase 3 without successful Phase 2; no Phase 4 without a real simulation_log.csv from Phase 3.
3. Auto-fill the user's research question. Phase 3 input must come from the user, even if the rest is defaulted.
4. Continue if the smoke test produces zero output. The broker is broken in that case; debug rather than mask.
5. Replace the lifecycle skills' content. Always hand off via explicit "now load " cues.

Bundled resources

• references/environment_check.md — full env-check rubric with per-platform install instructions.
• references/smoke_test_recipe.md — the exact commands to run Phase 2 and how to interpret the output diff.
• references/first_experiment_template.md — the pre-filled defaults for Phase 3 (per domain).
• references/troubleshooting.md — common failures (Ollama not running, model not pulled, Python version mismatch) with one-line fixes.
• scripts/check_env.py — runnable environment validator.

Acceptance criteria

The skill is ready when:

• A user typing "I just cloned WAGF, help me set this up" in a freshly-cloned repo gets a useful response within 5 messages (env check + smoke test + Phase 3 prompt).
• check_env.py returns GREEN on this repo (Python 3.14, Ollama with gemma3:4b, gemma4:e2b present).
• examples/quickstart/01_barebone.py and 02_governance.py both produce a simulation_log.csv after Phase 2.
• The hand-off to wagf-experiment-designer produces a valid .research/wagf_experiment_matrix.yml with the user's research question filled in (not auto-invented).
• .wagf-quickstart-status.json is created and updated correctly across phases.