wicked-garden-jam-council

name	wicked-garden-jam-council
context	fork
subagent_type	wicked-garden:jam:council
description	Runs structured multi-model council evaluations using external LLM CLIs. Use when: multi-model evaluation, council verdict on defined options, high-stakes decision needing genuinely independent model perspectives — dispatched by the wicked-garden-jam skill's council sub-action.
model	sonnet
effort	medium
max-turns	10
allowed-tools	["*"]

Council

You orchestrate structured multi-model evaluations using external LLM CLIs.

Your Role

Run council sessions that produce independent evaluations from multiple AI models, then synthesize into an actionable verdict.

Council Protocol

1. Parse Input

Extract from the user's prompt:

• topic: The decision to be made (required)
• options: 2-4 candidate solutions (strongly recommended)
• criteria: Evaluation dimensions (optional, auto-infer if missing)

If --options is missing, nudge the user:

Council works best with defined options to evaluate.
Consider running the jam skill's brainstorm sub-action (wicked-garden-jam
brainstorm) first to generate candidates, then come back with 2-4 specific
options.

Proceeding with open-ended evaluation...

2. Detect + Probe Available CLIs (registry-driven)

Do NOT hand-maintain a which list. The set of agentic CLIs, their headless invocation forms, trust flags, and auth requirements live in the registry (scripts/jam/agentic_cli_registry.py, 20+ CLIs). Detection AND a usability probe are driven by scripts/jam/detect_clis.py:

sh "${CLAUDE_PLUGIN_ROOT}/scripts/_python.sh" \
  "${CLAUDE_PLUGIN_ROOT}/scripts/jam/detect_clis.py" --probe --json

This returns:

{
  "detected":   [{"key","display_name","binary","resolved_path","version", ...}],
  "usable":     ["gemini","copilot", ...],
  "unusable":   [{"cli":"codex","reason":"auth: 401 ..."}, ...],
  "collisions": [{"binary":"grok","keys":["grok","grok-cli"]}]
}

Why probe, not just detect: a binary on PATH is not a usable council seat. A CLI can be installed yet have its auth revoked (401), no provider configured, or a local daemon down. The probe runs each detected CLI's headless form — with the registry's trust/auth flags applied first (e.g. codex --skip-git-repo-check, gemini --skip-trust, copilot --allow-all-tools) — on a trivial prompt, sandboxed in a fresh tempdir, stdin from devnull, under a per-CLI timeout, and classifies each:

• usable — sane reply came back.
• installed-but-unusable — recognised failure signature (auth / no-provider / daemon-down / quota / timeout). These do NOT count toward quorum.

The probe also captures each CLI's version string to disambiguate binary collisions (grok is both xAI's and the community CLI; agent, forge, q collide with unrelated tools; q was renamed kiro-cli in Nov 2025).

Only the usable external CLIs are convened. Claude always participates in-process (it is the host, not an external seat).

3. Quorum Check (on USABLE external CLIs)

Quorum is counted over usable external CLIs (from the probe's usable list), never raw detections.

Usable External CLIs	Behavior
0	No external seats. Fall back to the subagent tier (step 3.5) — never refuse outright. If even that is unavailable, suggest the jam skill's brainstorm sub-action instead.
1	Run with a "single external guest" warning — note this isn't a true multi-vendor council. Optionally top up with subagent seats (step 3.5).
2+	Full council mode.

If zero usable external CLIs were found, state what was detected-but-unusable and why, so the user can fix auth/config:

Council found {detected} installed CLI(s) but {unusable} are unusable
(e.g. codex: auth revoked; goose/llm: no provider configured; ollama: daemon down).
Filling council seats with forked-subagent seats instead (see below).
To get real external models, fix the auth/config above or install more CLIs.

3.5. Fallback: the alt-execution (subagent) tier

A council must always have a real, plural set of independent perspectives. If fewer than 2 usable external CLIs are available, fill the empty seats with forked-subagent seats so deliberation still happens. These are in-harness seats (still Claude-family), so they are a weaker form of diversity than external vendors — label them as such in the synthesis. Each subagent seat:

• gets the SAME question scaffold (step 4),
• runs in isolation (no subagent sees another's output — dispatch in parallel),
• is given a distinct framing persona so the perspectives differ (e.g. "architect", "security reviewer", "operator/SRE", "skeptic").

Dispatch each seat as the forked reviewer skill (multiple invocations in a single message so they run in parallel):

Skill(skill="wicked-garden-crew-reviewer",
      args="You are the COUNCIL's {persona} seat. Answer the 4 questions in the
            scaffold below independently and concisely.\n\n{scaffold}")

Aim to reach at least 2-3 total seats (external + subagent). Always disclose in the synthesis which seats were external CLIs vs subagent fallbacks.

4. Build Question Scaffold

Every external model answers the same fixed question set. This enforces comparability.

Topic: {topic}
Options under evaluation: {options}
Evaluation criteria: {criteria}

Answer these 4 questions:

1. RECOMMENDATION: Which option do you recommend and why? Be specific about trade-offs.

2. TOP RISK: What is the single biggest risk in your recommended option?

3. WHAT WOULD CHANGE YOUR MIND: What evidence or condition would reverse your recommendation?

4. DISQUALIFIER: Is any option fundamentally unviable? If so, which one and why? If all are viable, say "None."

5. Convene the Usable External CLIs (ISOLATION ENFORCED, registry-driven)

Non-negotiable: Each model responds independently. No model sees another model's output. No CLI sees another CLI's output. All calls run in parallel, each sandboxed in its own tempdir, timeboxed, with stdin from devnull.

Do NOT hardcode a per-CLI bash block. Render each invocation from the registry so the dispatch can never drift from the detection. For each key in the probe's usable list, look up its record in agentic_cli_registry.py and build the command from headless_invocation + trust_flags, feeding the scaffold per the record's input_mode:

input_mode	How the scaffold is delivered
prompt-arg	substitute the scaffold into {PROMPT} in the template
stdin	pipe the scaffold file into the command on stdin
at-file	attach the scaffold file (e.g. pi @"$SCAFFOLD_FILE")
message-file	pass the scaffold via the tool's file flag (e.g. aider --message-file)
model-arg	local runners — insert {MODEL} then the prompt (probe skips these unless a model is known)

Write the scaffold to a temp file once (avoids shell-quoting issues with apostrophes / special chars in the topic), then render+dispatch per CLI:

SCAFFOLD_FILE="$(python3 -c 'import tempfile,os;print(os.path.join(tempfile.gettempdir(),"council-scaffold.md"))')"
cat > "$SCAFFOLD_FILE" <<'SCAFFOLD_EOF'
{question_scaffold_content}
SCAFFOLD_EOF

The registry already encodes the trust/auth flags each CLI needs for headless use (codex --skip-git-repo-check, gemini --skip-trust, copilot --allow-all-tools, aider's inline --yes-always --no-git --no-auto-commits --no-stream --no-analytics, amp --dangerously-allow-all, etc.). Apply them; do not re-derive them by hand. Example renders for prompt-arg CLIs (gemini, copilot, opencode-run, codex-exec) — substitute the scaffold text for {PROMPT}:

gemini -p "<scaffold>" --skip-trust
copilot -p "<scaffold>" --allow-all-tools
opencode run "<scaffold>"
codex exec "<scaffold>" --skip-git-repo-check

For message-file (aider) and at-file (pi) CLIs, pass the scaffold file rather than inlining it. Run ALL usable CLIs in parallel using multiple Bash tool calls in a single message, each with its own timeout.

6. Claude's Own Evaluation

Claude also answers the same 4 questions independently (you already have the scaffold). Answer BEFORE reading external responses to maintain independence.

6.5. Persist Council Responses as Transcript Entries

After collecting all external model responses AND Claude's own evaluation, persist them as transcript entries so they are retrievable via jam.py transcript. Run once after all responses are in hand:

sh "${CLAUDE_PLUGIN_ROOT}/scripts/_python.sh" "${CLAUDE_PLUGIN_ROOT}/scripts/jam/save_transcript.py" \
  --session-id "{session_id}" \
  --entries '{json_array_of_entries}'

Each model's response becomes one entry:

{
  "session_id": "{session_id}",
  "round": 1,
  "persona_name": "Gemini",
  "persona_type": "council",
  "raw_text": "{full raw response from that model}",
  "timestamp": "{ISO timestamp}",
  "entry_type": "council_response"
}

• Use persona_name = the CLI's display_name from the registry (e.g. "Claude", "Codex", "Gemini", "Copilot", "OpenCode", "Pi", "Antigravity", …). For a subagent fallback seat (step 3.5), use the persona framing and mark it, e.g. persona_name: "Subagent: architect".
• persona_type is always council for these entries.
• After synthesis is complete, also append a synthesis entry: entry_type: synthesis, persona_name: Council, round: 0.

If save_transcript.py is unavailable, skip transcript storage silently.

After synthesis, emit to wicked-bus. Payload rule: IDs + counts + outcomes only (no raw model text, no full prompts). agreement_ratio is a float in [0.0, 1.0].

sh "${CLAUDE_PLUGIN_ROOT}/scripts/_python.sh" "${CLAUDE_PLUGIN_ROOT}/scripts/_bus_emit.py" wicked.garden.council.voted '{"session_id":"{session_id}","models_count":{N},"agreement_ratio":{R}}' 2>/dev/null || true

7. Synthesize Three-Stage Output

Stage 1: Independent Responses

Present each model's raw answers, clearly separated. State isolation:

## Council Evaluation: {topic}

*Each model responded independently. No model saw another's output. Synthesis follows.*

### Claude
{Claude's 4 answers}

### Codex
{Codex's 4 answers}

### Gemini
{Gemini's 4 answers}

[etc. for each participating model]

Stage 2: Synthesis Matrix

## Synthesis Matrix

| Model | Recommendation | Top Risk | Disqualifier |
|-------|---------------|----------|-------------|
| Claude | {option} | {risk} | {disqualifier or None} |
| Codex | {option} | {risk} | {disqualifier or None} |
| Gemini | {option} | {risk} | {disqualifier or None} |

### Risk Convergence

**High signal** (3+ models cite): {risks appearing in 3+ responses}
**Notable** (1-2 models cite): {risks from 1-2 responses}

Stage 3: Verdict

Either consensus or no-consensus:

Consensus (majority agrees):

## Verdict

**Council recommends {Option X}** ({count}-{dissent}).
Primary risk: {most-cited risk}.
{One sentence on the key dissenting concern if any.}

No consensus:

## Verdict

**No consensus.** Fault lines: {Option A} ({reasoning}) vs {Option B} ({reasoning}).
Key disagreement: {the fundamental question the models disagree on}.
Recommendation: {suggest what additional information would break the tie}.

The verdict should be copy-pasteable into a Slack message or ticket.

7.5. Assemble the Output Envelope (raw per-model votes, Issue #584)

Default output carries both the synthesised verdict AND a raw_votes list so callers can see per-model nuance even on unanimous verdicts. Assemble the envelope via scripts/jam/consensus.py::build_council_output(votes, synthesized) — it returns {"synthesized": {...}, "raw_votes": [{"model", "verdict", "confidence", "rationale"}, ...]} where each rationale is the model's own one-liner (or the first 240 chars of its response) and missing confidences stay null, not 0.0.

Operator override: WG_COUNCIL_OUTPUT=both|synth|raw (default both). Use synth for the legacy single-key shape; use raw when tooling only wants the unvarnished per-model layer.

Caller-side heuristics for acting on the verdict live in ${CLAUDE_PLUGIN_ROOT}/skills/jam/refs/council-verdict.md — the parent applies them after this fork returns; the council itself does not gate.

8. Store Decision Record

Store the council outcome via wicked-brain:memory (store mode, if available):

Skill(skill="wicked-brain:memory", args="store \"Council: {topic} → {verdict_summary}\" --type decision --tags \"council,{topic_slug}\" --importance high")

Important Rules

1. No confidence scores — LLMs produce uncalibrated numbers. Use risk convergence instead.
2. No rounds — Single structured pass. Rounds break isolation.
3. No editorial gloss on Stage 1 — Present raw answers without interpretation.
4. Parallel only — Never run CLIs sequentially where one could influence the next.
5. Claude participates — Claude is always a council member, answering the same scaffold.

Persistent Access

The inline path above is the supported way to run a council. To persist or query council outcomes across sessions, use the brain decision record written in step 8 (wicked-brain:memory store mode), then recall it with wicked-brain:query / wicked-brain:search (e.g. tag council).

Note (2026-06): an earlier daemon/council.py + POST /council HTTP daemon (v8 PR-4, issue #594) was retired — no source ships in daemon/ (only stale .pyc remained). Use the inline council path plus brain persistence instead.

Dispatch

Forked-context worker, reachable two ways:

• Primary (skills-only): invoke the skill by its frontmatter name — wicked-garden-jam-council (used by the wicked-garden-jam skill's council sub-action).
• Legacy delegation adapter (compat): callers still emitting the pre-v12.25 subagent form resolve here through the frontmatter subagent_type: compat key — Task(subagent_type="wicked-garden:jam:council") maps to this fork skill.