Immune

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)

<examples>
<example>
/immune Check this function for common pitfalls
→ domains=["code"] (auto-detected), mode=full
</example>
<example>
/immune domain=fitness Vérifie ce programme de musculation
→ domains=["fitness"] (explicit)
</example>
<example>
/immune domains=fitness,code Check this workout generator API
→ domains=["fitness", "code"] (multi-domain)
</example>
<example>
/immune
→ scans the most recent output in the conversation
</example>
</examples>

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].

Task-Conditioned Retrieval (v3.1.0+)

Antibodies and cheatsheet strategies may carry an optional triggers field for per-task filtering. This implements the read phase of the Memento-Skills reflective loop (arXiv 2603.18743) — entries are ranked by lexical overlap between the current task and their historical contexts.

Schema (additive, optional):

{
  "id": "AB-042",
  "domains": ["code"],
  "pattern": "SQL injection via string concatenation",
  "severity": "critical",
  "correction": "Use parameterized queries",
  "triggers": {
    "task_signatures": ["code query review sql", "audit code sql"],
    "domains": ["code"]
  }
}

Back-compat: entries without triggers behave as always-on (the v3.0.0 behavior). The task_conditioned_retrieval: true flag in config.yaml enables the filter. When disabled, all entries load regardless of task.

Ranking helper: scripts/retrieve.py implements the scoring logic. Callers (including the scanner agent, the skill-librarian in P2, and the skill-router in P3) invoke retrieve(prompt, entries, active_domains, historical_success) to obtain the ranked subset before tier classification. Scoring uses Jaccard similarity over normalized task signatures from scripts/task_signature.py, multiplied by an optional per-entry success rate derived from evals/history.jsonl.

During load (Phase 0 cheatsheet, Phase 1 antibodies), after the domain filter and before Hot/Cold tier classification, apply the retrieve step:

1. Compute task_signature(prompt) for the current task.
2. Pass entries through retrieve(...) with the active domains set.
3. Feed only the returned subset into the tier classification below.

Entries filtered out at retrieval never reach Hot/Cold — this is what keeps the scan context lean and task-focused.

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}