| name | maxtac-core-subagents |
| description | Use this skill when MaxTAC research needs goal-bounded auditor subagents, verifier debate subagents, specialist bug-class review, mitigation review, or independent votes on a vulnerability hypothesis or PoV. |
Parallel or Sequential
Use this guidance before spawning any group of auditor or debater subagents. Ideally, subagents would always run in parallel, but system resources sometimes prevent that. When resources are limited, subagents must be spawned sequentially to prevent RAM overload. Otherwise, subagents may be spawned in parallel.
Debater agents are always spawned in groups of three, so the parallel vs sequential decision is especially important for them. Auditor agents are spawned in groups of any size; the maximum number of auditors is 6, the Codex limit.
Use the active Python executable for helper scripts. If python is not on PATH in Codex Desktop, call codex_app.load_workspace_dependencies and use the bundled Python executable it returns.
To determine whether to spawn subagents in parallel or sequentially, run:
python3 <skill-dir>/scripts/readiness-check.py --kind auditor --subagents <count>
python3 <skill-dir>/scripts/readiness-check.py --kind debater --subagents <count>
Use the local helper scripts for readiness checks, prompt enrichment, debate ballot validation, and auditor selection. Domain packs publish auditor JSON catalogs; Core rebuilds a global SQLite auditor registry from the active plugin set at session start. The parent still owns final summaries.
The helper scripts store audit and debate records in <workspace-root>/workspace.sqlite. Auditor catalogs are stored separately in $CODEX_HOME/maxtac/auditors.sqlite because they are plugin/session state, not workspace findings. Use the SQLite-backed helper commands for auditor search, majority counting, detail lookup, and semantic duplicate-work checks.
Verifier-style subagents are not exempt from persistence. If a subagent is independently checking a hypothesis, closure, proof, mitigation assumption, attention decision, or "is this worth pursuing?" question, generate the prompt through audit-helper.py --prompt-file ... --prompt-kind verifier or through debate-helper.py --prompt-file when the question is a binary proposition. Do not leave verifier conclusions only in tmp/, chat text, or parent memory.
The script prints parallel or sequential after checking available system resources against the requested subagent count. It reserves 4 GiB of available memory per requested subagent plus 1 GiB of headroom. Auditor subagents have an additional safety gate: if total system RAM is unknown or below 17 GiB, auditor readiness returns sequential regardless of available memory. Debaters and generic subagents are unaffected by the 17 GiB total-RAM gate.
If the result is parallel, spawn subagents using standard Codex subagent spawning mechanisms without waiting for each to finish. If the result is sequential, spawn one subagent at a time, waiting for it to finish before spawning the next.
Goal-Bounded Subagent Runs
Always spawn auditor and debater subagents with a Codex goal prompt, not only a task prompt. Do not spawn a MaxTAC subagent from a raw audit or debate prompt. First run audit-helper.py --prompt-file, debate-helper.py --prompt-file, audit_prompt_create, or debate_prompt_create, then pass the enriched prompt to the subagent unchanged.
The enriched prompt must make goal activation the first action. The subagent's first tool call should be create_goal when available; if create_goal is unavailable but slash commands are available, the subagent should send /goal ... before doing any analysis. The goal must include a positive objective and a negative end outcome: the positive objective names the useful artifact the subagent should produce, while the negative end outcome names when the subagent should stop and persist blockers instead of expanding scope.
The generated prompt also requires a visible goal-activation record in the persisted artifact. Audit assessments must include a Goal Activation section. Debate ballots must include a goal_activation field with create_goal, /goal, or unavailable. Treat a missing goal-activation record as a prompt or execution defect to remediate before trusting the subagent result.
Bound each subagent goal tightly enough to avoid long-running drift but wide enough to be useful. For auditors, bound the goal to one hypothesis, one auditor specialty, the supplied packet/evidence, directly referenced files/functions, and immediately necessary callers/callees. For debaters, bound the goal to one binary proposition and the supplied or directly referenced evidence. Subagents must not broaden into repo-wide discovery, new fuzzing campaigns, new PoV construction, or unrelated refactors unless the prompt explicitly grants that scope.
Attention Review Subagents
When workspace_status reports an attention-lock warning, the parent may spawn a single goal-bounded auditor as an independent attention reviewer. The prompt should include the attention report, current phase, recent ledger summary, active branch, and the exact decision options: deepen, pivot, consolidate, phase-shift, or delegate-review. Use audit-helper.py --prompt-file or audit_prompt_create; do not spawn the reviewer from a raw prompt. The reviewer should persist an assessment into workspace.sqlite that recommends one action and names the evidence or absence of evidence behind it.
For attention reviewers, prefer audit-helper.py --prompt-file tmp/<prompt>.md --prompt-kind attention-review --context-query "<active boundary or subsystem>" so later agents can distinguish the result from a vulnerability audit while still searching it through audit memory.
Auditor Subagents
An auditor subagent is a specialist vulnerability researcher for an individual bug class, mitigation, program, or other security topic. Core owns the goal-bounded prompt, persistence, and debate mechanics. Installed domain packs own most auditor catalogs.
Auditor Flow
Every audit follows the same 5-step flow.
- Choose the auditor: before creating a new audit, search existing audit memory for the hypothesis, boundary, and key component names:
python3 <skill-dir>/scripts/audit-helper.py --root <workspace-root> --audit-search "<hypothesis boundary component>"
If a matching assessment already answered the question, reuse it or record a narrowed delta prompt instead of duplicating the audit. Then search the SQLite auditor registry populated from the active MaxTAC plugin packs.
The registry is rebuilt automatically by the Core SessionStart hook. If a session started before the hook was installed, or if plugins changed mid-session, rebuild it manually:
python3 <skill-dir>/scripts/auditor_registry.py rebuild
List, filter, and show auditors through the Core helper rather than reading on-disk JSON directly:
python3 <skill-dir>/scripts/audit-helper.py --catalogs
python3 <skill-dir>/scripts/audit-helper.py --catalog apple --filter tcc
python3 <skill-dir>/scripts/audit-helper.py --catalog apple --show apple-tcc-bypass
Use --catalog web, --catalog binary, --catalog cloud, --catalog supply-chain, --catalog android, --catalog apple, or --catalog microsoft for domain auditors. Use --catalog core for Core's small general-purpose catalog. The --list and --filter options print condensed information for a list of auditors, while --show prints the full markdown instructions for a specific auditor. When a domain triage packet suggests auditor filters, use those filters against the matching domain catalog in the registry.
Prefer focused auditors over broad review. For example, use a specific logic-* auditor for authentication state, entitlement, replay, approval, tenant relationship, destructive action, AI-agent authority, or specification drift instead of asking for generic logic analysis. Use generic business logic only when the target is truly workflow-centered and no narrower logic auditor fits.
- Write the audit prompt: after reading the specialist usage and related markdown, write a prompt for its subject matter and the current target. Do not use the specialist markdown directly as a prompt; instead, adapt its guidance for the current context. Persist the prompt draft to
<workspace-root>/tmp/, then run:
python3 <skill-dir>/scripts/audit-helper.py --root <workspace-root> \
--prompt-file tmp/<prompt-name>.md \
--context-query "<hypothesis boundary component>"
The above script prints an enriched prompt; enrichment prepends Codex goal instructions, embeds corpus orientation and model search output for the context query, and appends instructions to persist the audit assessment into workspace.sqlite. It also creates the SQLite audit record for the generated audit ID. If a context query is genuinely irrelevant, omit --context-query only after the prompt itself explains why corpus/model orientation does not apply.
Use --prompt-kind verifier, --prompt-kind attention-review, or --prompt-kind mitigation-review for non-auditor verification checks that still need durable assessment memory. The record is stored in the audit table so --audit-search, --audit-list, and --audit-show find it later.
Include the triage packet, relevant graph evidence, OpenGrep result summaries, and exact file/function references in the prompt. Omit unrelated checklist text and avoid asking the auditor to rediscover the whole target.
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Spawn the auditor: use the enriched prompt unchanged, including its first-action goal activation instructions, to spawn the auditor subagent. There is no script for this phase; use standard Codex subagent spawning mechanisms. If the session is at maximum subagents and one is stopped, replace a stopped subagent. See the "Parallel or Sequential" section above for parallelism guidance.
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Wait for the auditor: during the wait, continue normal operations as the main agent in parallel, even for serial subagent spawns. The subagent should record its final assessment through the helper command embedded in the enriched prompt.
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Complete the audit: after the auditor subagent finishes its work, verify audit-helper.py --audit-show <audit-id> returns an assessment. If not, quickly remediate by reviewing the subagent session and recording a proper assessment. Then refresh the SQLite audit index:
python3 <skill-dir>/scripts/audit-helper.py --root <workspace-root> --audit-sync
Use --audit-list, --audit-show <audit-id>, or --audit-search "<text>" for later lookup of assessment details, artifacts, and conclusions.
Debater Subagents
A debater subagent reviews a debate topic, then votes on a ballot and explains its reasoning.
Debater Flow
Every debate follows the same 5-step flow.
- Write the debate prompt: write a prompt for the debate topic. Since each debater must rate yes or no, the prompt requires a precise binary proposition: "yes means X, no means Y." Persist the prompt draft to
<workspace-root>/tmp/, then run:
python3 <skill-dir>/scripts/debate-helper.py --root <workspace-root> --prompt-file tmp/<prompt-name>.md
The above script prints an enriched prompt; enrichment prepends Codex goal instructions and appends instructions to persist each debate ballot into workspace.sqlite. It also creates the SQLite debate record for the generated debate ID.
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Spawn debater subagents: use the enriched prompt unchanged, including its first-action goal activation instructions, to spawn three debater subagents. There is no script for this phase; use standard Codex subagent spawning mechanisms. If the session is at maximum subagents and one is stopped, replace a stopped subagent. See the "Parallel or Sequential" section above for parallelism guidance.
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Wait for the debaters: during the wait, continue normal operations as the main agent in parallel, even for serial subagent spawns. Each subagent records its ballot through the helper command embedded in the enriched prompt, using a JSON structure as follows:
{
"debate": "debate-id",
"subagent": "subagent-name",
"goal_activation": "create_goal",
"choice": "yes",
"confidence": 85,
"reasoning": "detailed reasoning for the choice",
"evidence": "detailed evidence supporting the reasoning",
"blockers": "nullable; any blockers or concerns about the debate topic"
}
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Complete the debate: after the debater subagents finish their work, verify debate-helper.py --show <debate-id> lists each expected ballot. If not, quickly remediate by reviewing the subagent session(s) and recording appropriate ballot(s).
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Tally the debate: after all ballots are persisted, tally the debate by reviewing each ballot and writing a summary of the results, including which side won and why, supported by evidence from the ballots. To store the machine totals, ballot-by-ballot review, and parent-facing summary in workspace.sqlite, run:
python3 <skill-dir>/scripts/debate-helper.py --root <workspace-root> --debate <debate-id> --tally
The parent agent owns the final decision. Review debate-helper.py --show <debate-id> before using the result for a ledger state transition.
The tally command also refreshes the SQLite debate index. Use these commands when reviewing previous votes:
python3 <skill-dir>/scripts/debate-helper.py --root <workspace-root> --list
python3 <skill-dir>/scripts/debate-helper.py --root <workspace-root> --show <debate-id>
python3 <skill-dir>/scripts/debate-helper.py --root <workspace-root> --search "<proposition or evidence>"
