Immune System v3 — Hybrid Cheatsheet + Immune

You operate a hybrid adaptive system with two complementary memories:

• Cheatsheet (positive patterns): domain-specific strategies injected BEFORE generation to improve output quality
• Immune (negative patterns): antibodies that detect known errors and discover new threats AFTER generation

Both memories use Hot/Cold tiering to keep context lean.

Input Parsing

The user invokes with content to scan. Parse these parameters:

• input: The text/code/content to scan (required — either inline or from context)
• domain: One of: fitness, code, writing, research, strategy, webdesign, _global (default: auto-detect)
• domains: Array of domains (overrides single domain). Example: domains=fitness,code
• constraints: Any specific requirements the output should satisfy (optional)
• mode: full (cheatsheet + scan, default) | scan-only (skip cheatsheet) | cheatsheet-only (return cheatsheet, no scan)

If no inline text is provided, scan the last substantive output in the conversation.

Domain auto-detection: Read config.yaml (co-located with this skill) and match content against domain_keywords. If no strong match, use ["_global"]. If single domain string provided, wrap in array: domains = [domain].

Execution

Step 0 — Cheatsheet Injection (positive patterns)

Skip this step if mode == "scan-only".

0a. Load cheatsheet: Read cheatsheet_memory.json (co-located with this skill).

0b. Filter by domains: Keep strategies where ANY of the strategy's domains overlaps with the detected domains, OR strategy has "_global" in its domains.

0c. Classify into tiers (same logic as antibodies): A strategy is HOT if ANY of:

• effectiveness >= 0.7
• seen_count >= 3
• last_seen less than 30 days ago

Everything else is COLD.

0d. Cap HOT strategies: Sort by effectiveness descending, then seen_count descending. Keep max 15 (from config.yaml → cheatsheet.max_hot).

0e. Build cheatsheet block: Format HOT strategies as XML:

<cheatsheet domain="{domains}">
  <strategy id="{id}" effectiveness="{effectiveness}">
    {pattern}
    Example: {example}
  </strategy>
  ...
</cheatsheet>

If there are COLD strategies, add a one-liner:

<cheatsheet_cold>Also consider: {comma-separated COLD pattern keywords}</cheatsheet_cold>

If mode == "cheatsheet-only", output the cheatsheet block and stop here.

Log:

[IMMUNE] Cheatsheet: {n_hot} HOT + {n_cold} COLD strategies (domains: {domains})

0f. Present cheatsheet to user: If running standalone (/immune), show the cheatsheet as context the user should apply to their next generation. If called by another system, return the XML block for injection into prompts.

Step 1 — Load & Classify Antibodies (Hot/Cold)

Read immune_memory.json and config.yaml (co-located with this skill).

1a. Filter by domains: Keep antibodies where ANY of the antibody's domains overlaps with detected domains, OR antibody has "_global" in its domains.

Backwards compatibility: If an antibody has "domain" (string) instead of "domains" (array), treat it as domains = [domain].

1b. Classify into tiers: For each filtered antibody, classify as HOT if any of these is true:

• severity == "critical"
• seen_count >= 3
• last_seen is less than 30 days ago (relative to today's date)

Everything else is COLD.

1c. Cap HOT antibodies: Sort HOT by: severity (critical > warning > info), then seen_count descending. Keep max 15 (from config.yaml → tiers.hot.max_per_scan). If more than 15 qualify as HOT, overflow goes to COLD.

1d. Build COLD summary: For each COLD antibody, extract a short keyword from its pattern field. Join as comma-separated list. Example: "SQL transactions, épicondylite, debug flags, tautologies"

Log:

[IMMUNE] Tier split: {n_hot} HOT + {n_cold} COLD / {total} total (domains: {domains})

Step 2 — Scan

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

<scan_request>
  <domains>{detected_domains as JSON array}</domains>
  <task>{task description or "Scan the following content for errors and threats"}</task>
  <constraints>{constraints or "none"}</constraints>

  <content>
{the input text/code/content to scan}
  </content>

  <hot_antibodies>
{JSON array of HOT antibodies — full objects with id, domains, pattern, severity, correction}
  </hot_antibodies>

  <cold_summary>
Dormant patterns (not detailed, for awareness only): {comma-separated COLD keywords}
  </cold_summary>

  <cheatsheet_applied>
{list of strategy IDs and patterns that were injected in Step 0, or "none" if scan-only mode}
  </cheatsheet_applied>
</scan_request>

Log: [IMMUNE] Scanning... ({n_hot} active antibodies) Wait for result.

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

Step 3 — Update Immune Memory (with COLD deduplication)

Read current immune_memory.json.

3a. Matched HOT antibodies: For each antibody matched by the scanner, increment seen_count and update last_seen to today.

3b. New threats — deduplicate against COLD: For each new threat in new_threats_detected:

1. Compare its pattern against ALL COLD antibodies (fuzzy match — same domains + similar keywords).
2. If it matches a COLD antibody → REACTIVATE:
   • Increment the COLD antibody's seen_count
   • Update its last_seen to today
   • Log: [IMMUNE] Reactivated COLD antibody {id}: {pattern}
   • Do NOT create a new antibody (prevents duplicates)
3. If no COLD match AND auto_add_threats is true → CREATE new antibody:
   • id: "AB-{next_number}"
   • domains: from the threat's recommended_antibody.domains (array)
   • pattern, severity, correction: from the threat's recommended_antibody
   • seen_count: 1
   • first_seen: today's date
   • last_seen: today's date
   • Log: [IMMUNE] + New antibody {id}: {pattern}

3c. Update stats:

• Increment stats.outputs_checked
• Increment stats.issues_caught by number of corrections + new threats
• Update stats.antibodies_total to current antibody count

Write back to immune_memory.json.

Log: [IMMUNE] Memory: {total} antibodies ({n_hot} hot, {n_cold} cold) | +{new} added | Reactivated: {reactivated}

Step 3b — Update Cheatsheet Memory (positive patterns)

Skip if mode == "scan-only" or no new_strategies_detected in scan result.

Read current cheatsheet_memory.json.

3b-i. Deduplicate: For each new strategy in new_strategies_detected:

1. Compare against ALL existing strategies (fuzzy match — overlapping domains + similar pattern).
2. If it matches an existing strategy → REINFORCE:
   • Increment seen_count
   • Update last_seen to today
   • Adjust effectiveness: new_eff = old_eff * 0.8 + reported_eff * 0.2 (exponential moving average)
   • Log: [IMMUNE] Reinforced strategy {id}: {pattern} (eff: {old}→{new})
3. If no match AND auto_add_strategies is true → CREATE new strategy:
   • id: "{prefix}-{next_number}" (prefix from config.yaml → cheatsheet.id_prefix.{domain})
   • domains: from the strategy (array)
   • pattern, example: from the strategy
   • effectiveness: from the strategy (or config.yaml → cheatsheet.default_effectiveness)
   • seen_count: 1
   • first_seen: today's date
   • last_seen: today's date
   • Log: [IMMUNE] + New strategy {id}: {pattern}

3b-ii. Prune low-effectiveness: If any strategy has effectiveness < config.cheatsheet.min_effectiveness AND seen_count >= 5:

• Remove it
• Log: [IMMUNE] - Pruned strategy {id}: {pattern} (eff: {eff})

3b-iii. Update stats:

• Increment stats.outputs_assisted
• Increment stats.strategies_applied by number of cheatsheet strategies that were used
• Update stats.strategies_total to current count

Write back to cheatsheet_memory.json.

Log: [IMMUNE] Cheatsheet: {total} strategies | +{new} added | Reinforced: {reinforced}

Step 4 — Output

If clean:

───
IMMUNE v3 | domains={domains} | Status: CLEAN
   Cheatsheet: {n_strategies} strategies applied | Antibodies: {n_hot}/{max} HOT, {n_cold} COLD
   No issues detected
───

If corrections or threats found:

───
IMMUNE v3 | domains={domains} | Status: {CORRECTED|FLAGGED}

Corrections Applied:
  [AB-XXX] {pattern} → {correction}

New Threats Detected:
  [{severity}] {pattern} — {suggested_correction}

Reactivated:
  [AB-XXX] {pattern} (was COLD, now HOT)

New Strategies Learned:
  [CS-XXX] {pattern} (eff: {effectiveness})

───
Corrected Output:
{the corrected content, formatted for the domain}
───
Memory: {total_ab} antibodies + {total_cs} strategies | +{new_ab} AB | +{new_cs} CS
───

Then present the corrected output in a human-readable format appropriate to the domain.

Error Handling

• If immune_memory.json does not exist: create it with {"version": 3, "antibodies": [], "stats": {"outputs_checked": 0, "issues_caught": 0, "antibodies_total": 0}}
• If cheatsheet_memory.json does not exist: create it with {"version": 3, "strategies": [], "stats": {"outputs_assisted": 0, "strategies_applied": 0, "strategies_total": 0}}
• If immune_memory.json has "version": 2: auto-migrate by converting each antibody's "domain" to "domains": ["domain_value"] and set version to 3. Write back immediately.
• If the agent returns invalid JSON: retry once. If still invalid, report the raw output with a warning.
• If no input is provided and no recent output exists: ask the user what to scan.
• If all antibodies are COLD (none qualify as HOT): still send the scan with empty hot_antibodies array and full cold_summary. Haiku can still detect new threats via Phase 2.