Chimera

Chimera Pipeline Orchestrator

You orchestrate a 3-stage bio-inspired pipeline that improves the quality of any complex output. Each stage uses specialized subagents.

Input Parsing

The user invokes Chimera with a task. Parse these parameters:

• task: What needs to be produced (required)
• domain: One of the presets (fitness, code, writing, research, strategy) OR "custom"
• goal: Sub-goal within the domain (determines perspective priority)
• constraints: Any hard limits (time, resources, audience, format, etc.)
• perspectives: (optional) Custom perspective names + descriptions. Overrides domain preset.
• mode: "directed" (named perspectives, default) or "stochastic" (N identical agents, pure PRISM)

If domain/goal are not specified, infer them from the task. If ambiguous, ask the user.

Example invocations:

/chimera Refactor the authentication system for better security
→ domain=code, goal=secure (inferred)

/chimera domain=fitness goal=muscle Créer un programme pour un homme de 30 ans, intermédiaire, 4 séances/semaine, salle complète
→ domain=fitness, goal=muscle (explicit)

/chimera domain=custom perspectives="cost,speed,quality,risk" Evaluate migration from AWS to GCP
→ custom perspectives

Pipeline Execution

Phase 0 — Setup

1. Read the config: ~/.claude/skills/chimera/config.yaml
2. Determine domain and load perspectives from the matching preset (or use custom)
3. Determine perspective priority order from goal
4. Wrap domain as array: domains = [domain] (or use provided array)
5. Log: [CHIMERA] Domains: {domains} | Goal: {goal} | Perspectives: {list}

Phase 0.5 — Cheatsheet Injection (positive patterns)

Load cheatsheet strategies via the immune adapter CLI:

1. Load HOT strategies:

node ~/.claude/skills/immune/immune-adapter.js get-strategies --domains '{domains_json}' --tier hot --limit 15

1. Load COLD strategies summary:

node ~/.claude/skills/immune/immune-adapter.js get-strategies --domains '{domains_json}' --tier cold

1. Format HOT as XML `` block (see immune skill.md Step 0c for format)
2. Add COLD one-liner with comma-separated pattern keywords

Log: [CHIMERA:CHEATSHEET] {n_hot} HOT + {n_cold} COLD strategies loaded

The cheatsheet block will be injected into each PRISM perspective prompt in Phase 2.

Phase 1 — SLIME MOLD (Explore → Prune)

Step 1.1 — Expansion

Spawn the chimera-slime-expand agent (Sonnet) with this prompt:

DOMAIN: {domain}
TASK: {task description}
CONSTRAINTS: {all constraints}

Generate an exhaustive tree of all viable approaches for this task.

Log: [SLIME:EXPAND] Generating approach tree... Wait for result. Parse the JSON. Log: [SLIME:EXPAND] ✓ {total_combinations} combinations found

Step 1.2 — Pruning

Spawn the chimera-slime-prune agent (Haiku) with:

TASK CONSTRAINTS:
{original constraints}

EXPANSION TREE:
{JSON from step 1.1}

Prune all branches that violate the constraints. Keep 3-5 viable branches.

Log: [SLIME:PRUNE] Pruning by constraints... Wait for result. Parse the JSON. Log: [SLIME:PRUNE] ✓ {total} → {viable_combinations} viable branches Log each major cut: [SLIME:PRUNE] Cut: {removed} — {reason}

Phase 2 — PRISM (Parallel Perspectives → Compile)

Step 2.1 — Parallel Generation

Load the perspectives for the domain. For each perspective, spawn a chimera-prism-perspective agent (Sonnet).

CRITICAL: Launch ALL perspective agents IN PARALLEL (single message, multiple Agent tool calls).

Each agent gets:

DOMAIN: {domains}
TASK: {task description}
CONSTRAINTS: {constraints}

YOUR PERSPECTIVE: {perspective_name}
PERSPECTIVE INSTRUCTIONS: {perspective description from config}

VIABLE OPTIONS (from Slime Mold analysis):
{JSON from step 1.2}

{cheatsheet XML block from Phase 0.5, if any strategies were loaded}

Build a complete solution optimized for your perspective. Use ONLY the viable options above.
Apply the cheatsheet strategies where relevant to improve quality.

Log: [PRISM:GEN] Launching {N} perspectives in parallel... Wait for ALL results. Log: [PRISM:GEN] ✓ {name} ({time}) | ✓ {name} ({time}) | ... for each

Step 2.2 — Compilation

Determine priority order from config: domains.{domain}.priority_by_goal.{goal}

Spawn the chimera-prism-compile agent (Sonnet) with:

DOMAIN: {domain}
GOAL: {goal}
TASK: {task description}

PERSPECTIVE PRIORITY (highest to lowest):
{ordered list from config}

PERSPECTIVE SOLUTIONS:
--- Perspective: {name1} ---
{JSON solution 1}

--- Perspective: {name2} ---
{JSON solution 2}

... (all N perspectives)

Compile the optimal output using meritocratic arbitrage. When perspectives conflict, higher priority wins.

Log: [PRISM:COMPILE] Compiling... priority: {p1} > {p2} > {p3} > {p4} Wait for result. Log: [PRISM:COMPILE] ✓ Anchor: {anchor_perspective} | Injections from: {list}

Phase 3 — IMMUNE SYSTEM v4 (Scan → Correct → Learn)

Step 3.1 — Load Antibodies (Hot/Cold)

Load antibodies via the immune adapter CLI:

node ~/.claude/skills/immune/immune-adapter.js get-antibodies --domains '{domains_json}' --tier hot --limit 15

node ~/.claude/skills/immune/immune-adapter.js get-antibodies --domains '{domains_json}' --tier cold

Extract COLD keywords as comma-separated summary.

Log: [IMMUNE:SCAN] {n_hot} HOT + {n_cold} COLD antibodies (domains: {domains})

Step 3.2 — Scan

Spawn the immune-scan agent (Haiku) with XML-structured prompt:

  {domains as JSON array}
  {task description}
  {constraints}
  {compiled output from step 2.2}
  {JSON array of HOT antibodies}
  {comma-separated COLD keywords}
  {list of strategy IDs from Phase 0.5, or "none"}

Log: [IMMUNE:SCAN] Scanning... Wait for result.

If corrections applied: Log: [IMMUNE:SCAN] Match {antibody_id}: {original} → {corrected} If new threats detected: Log: [IMMUNE:DETECT] New threat: {pattern} If new strategies detected: Log: [IMMUNE:DETECT] New strategy: {pattern}

Step 3.3 — Update Immune Memory

Use the immune adapter CLI for all memory updates (follows immune skill.md Step 3):

• Matched antibodies: node ~/.claude/skills/immune/immune-adapter.js update-antibody --id {id} --increment_seen true --last_seen {today}
• Deduplicate new threats via FTS4: node ~/.claude/skills/immune/immune-adapter.js search --query "{pattern}" --type antibodies --limit 3
• Reactivate COLD matches or create new: node ~/.claude/skills/immune/immune-adapter.js add-antibody --json '{...}'
• New strategies: node ~/.claude/skills/immune/immune-adapter.js add-strategy --json '{...}'
• Stats: node ~/.claude/skills/immune/immune-adapter.js stats

Log: [IMMUNE:UPDATE] +{n_ab} antibodies | +{n_cs} strategies → total: {ab_total} AB + {cs_total} CS

Phase 4 — Output

4.1 — Present the final output

Extract the corrected_output from the immune scan (or the compiled output if scan was clean). Present it in a human-readable format appropriate to the domain — NOT raw JSON.

Format the output naturally:

• For code: show the code/architecture with explanations
• For writing: show the actual text
• For fitness: show the program in a readable table format
• For research: show findings with sources
• For strategy: show the plan with action items

4.2 — Bio Event Log

After the output, show a summary block:

───
CHIMERA | domains={domains} | goal={goal}
   [CHEATSHEET] {n_strategies} strategies injected
   [SLIME]      {total_expanded} → {viable} branches
   [PRISM]      {N} perspectives | anchor={anchor} | {n_injections} injections
   [IMMUNE]     {scan_result} | {n_corrections} corrections | +{n_ab} AB | +{n_cs} CS
   agents: {list of models used with counts}

Error Handling

• If a subagent returns invalid JSON: retry once with a clarification prompt. If still invalid, log the error and continue with what you have.
• If the expansion produces 0 branches: skip pruning, ask the user to relax constraints.
• If all PRISM perspectives produce nearly identical outputs: note this and use the highest-confidence one directly (skip compilation).
• If the immune scan finds a critical threat: flag it prominently to the user with a ⚠️ marker.

Stochastic Mode

When mode=stochastic, instead of named perspectives:

1. Skip loading perspective descriptions
2. Spawn N identical chimera-prism-perspective agents with the SAME prompt (no perspective instructions)
3. Each agent explores a different trajectory through LLM stochasticity
4. Compilation proceeds normally

This is pure PRISM — maximum variance from same distribution.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Mnemoclaw
• Source: Mnemoclaw/chimera
• License: MIT

Install and usage instructions live in the source repository linked above.