plan-swarm-review

Plan Swarm Review

Iterative plan hardening through multisampling and focused decomposition.

Core insight: a single agent misses issues due to attention budget limits. Multiple independent agents reading the same document find different problems (stochastic diversity). Focused decomposition further improves depth per aspect. Iterative fix-then-re-review uncovers issues previously masked by other bugs.

Source: deksden (@deksden_notes) — "Plan Swarming" technique, April 2026. Related: Anthropic Harness Design (Generator-Evaluator), deep-review (parallel competency code review).

Research backing:

• [2502.11027] Sampling diversity in LLM inference — diverse prompts beat identical: +10.8% reasoning, +9.5% code
• [2602.09341] AgentAuditor — reasoning tree audit beats majority voting, recovers 65-82% of minority-correct findings
• [2602.17875] MultiVer — 4 parallel agents hit 82.7% recall on vulnerability detection (beats fine-tuned models)
• [2510.00317] MAVUL — multi-agent vuln detection: +600% vs single-agent
• Anthropic Code Review (Mar 2026) — parallel agents raise substantive findings from 16% to 54%

Modes

This skill works in two modes:

Plan mode (default): review design docs, specs, ADRs, RFCs before implementation. Code mode: review code files for bugs and vulnerabilities. Activated when user passes code files instead of a plan, or says "review code", "find vulnerabilities", "security audit". In code mode, aspects shift from plan-oriented (contracts, completeness) to code-oriented (injection, auth bypass, race conditions, memory).

---

Step 0: Identify the target document

Ask the user which document to review if not obvious from context.

Plan mode: PLAN.md, ADR, spec, design doc, RFC, or any structured document describing what will be built and how.

Code mode: source code files, a module, or a directory. Best for security audits, bug hunts, or pre-release quality checks.

Read the target document(s) fully. Note:
- Total size (lines, sections/files)
- Key components/modules described or implemented
- Interfaces between components
- Data flows and mutations
- External dependencies and trust boundaries

If the target is <100 lines with 1-2 simple components, suggest a single-pass review instead — swarming is overkill for small targets.

---

Step 1: ROUND 1 — Broad Review (single agent)

Purpose: catch obvious issues before spending tokens on multisampling.

Launch ONE Agent with this prompt:

You are a senior architect reviewing a plan document before implementation.
Your goal: find issues that would cause bugs, rework, or confusion during
implementation.

## Plan to review
{paste or reference the plan document path}

Read the entire plan. Then check for:

1. CONTRACTS — are interfaces between components fully specified?
   Types, error codes, required vs optional fields, versioning.
2. DATA FLOW — is data transformation described end-to-end?
   What happens at each boundary? Backward compatibility?
3. NEGATIVE SCENARIOS — what happens when things fail?
   Timeouts, partial failures, invalid input, race conditions.
4. CONSISTENCY — do different sections contradict each other?
   Same entity described differently in two places?
5. COMPLETENESS — are there gaps? Steps that say "TBD" or "later"?
   Scenarios mentioned but not covered?
6. DEPENDENCIES — is implementation order clear?
   Are blocking dependencies identified? Circular deps?
7. AMBIGUITY — could two engineers read a section and implement
   it differently? Vague terms like "handle appropriately"?

## Output format
For EACH finding:

FINDING: {one-line description}
SECTION: {which section of the plan}
SEVERITY: HIGH | MEDIUM | LOW
EVIDENCE: {quote the problematic text, max 2 lines}
FIX: {concrete change to the plan text}

If the plan is clean — output: "NO_FINDINGS — plan review clean."
Do NOT pad with praise. Only problems.

After Round 1

Collect findings. If 0 findings → plan is clean, congratulate user, stop.

If findings exist:

1. Present findings to user grouped by severity
2. Ask: "Apply these fixes and continue to Round 2 (multisampling)?"
3. If user approves fixes → apply them to the plan document
4. If user says stop → stop

---

Step 2: ROUND 2 — Diverse Multisampling (N parallel agents, varied perspectives)

Purpose: stochastic diversity catches what one pass missed.

IMPORTANT: do NOT use identical prompts for all agents. Research [2502.11027] shows identical prompts produce correlated errors — agents "cluster" on the same issues and miss the same blind spots. Instead, give each agent a DIFFERENT perspective while reviewing the same document.

Launch 3 agents in parallel (or 5 for critical plans), each with a different reviewer persona:

CRITICAL: launch all agents in a SINGLE message (parallel tool calls). Each agent has isolated context — no cross-contamination.

Plan mode perspectives

Agent	Persona	Focus bias
1	Skeptical implementer	"I have to code this tomorrow — what's unclear, contradictory, or impossible?"
2	Security auditor	"Where are the trust boundaries? What happens with malicious input?"
3	QA engineer	"How do I test this? What edge cases aren't covered? What breaks at scale?"
4	New team member	"I just joined — what terms are undefined? What implicit knowledge is required?"
5	Ops/SRE	"What fails at 3am? What's the rollback plan? What's unmonitored?"

Code mode perspectives

Agent	Persona	Focus bias
1	Attacker	"How do I exploit this? Injection, auth bypass, privilege escalation?"
2	Concurrency specialist	"What races, deadlocks, or ordering issues exist?"
3	Performance engineer	"What's O(n^2)? What allocates unbounded memory? What blocks the event loop?"
4	Error recovery auditor	"What happens when X fails? Is cleanup correct? Are resources leaked?"
5	Integration tester	"Do contracts match? Are types compatible? What breaks at boundaries?"

You are a {PERSONA} reviewing {plan/code} before implementation/deployment.
Your perspective: {FOCUS_BIAS}

Review the ENTIRE document through your specific lens.
{same checklist and output format as Round 1}

After Round 2

1. Deduplicate: group findings by section + issue type. When multiple agents find the same issue → mark as HIGH CONFIDENCE (consensus).
2. Preserve minority findings: a finding from only 1 of 5 agents is NOT automatically low-value. Research [2602.09341] shows minority-correct findings are often the most valuable — non-obvious bugs that only one perspective catches. Flag these as UNIQUE CATCH, do not discard.
3. Synthesize: produce merged report. Separate consensus vs unique catches.
4. Present to user with round report format (see Output Format below).
5. Ask: "Apply fixes and continue to Round 3 (focused review)?"

Stop criteria: if Round 2 found 0 high + <=2 medium → STOP. Plan is solid.

---

Step 3: ROUND 3 — Focused Review (decompose into aspects)

Purpose: narrow scope = deeper analysis per aspect.

Step 3a: Determine focus aspects

Based on the target content, select 3-7 aspects.

Plan mode aspects

Aspect	When to include
Contracts & Interfaces	Plan describes >2 interacting components
Data Flow & Migrations	Plan involves data transformation, DB changes, or state migration
Negative Scenarios	Plan describes user-facing features or distributed systems
Consistency	Plan is >300 lines or written by multiple authors
Completeness	Plan references external systems or has phased rollout
Security & Trust	Plan involves auth, user input, or external APIs
Dependencies & Order	Plan has >5 implementation steps or parallel workstreams

Code mode aspects (for bug/vulnerability hunting)

Before launching agents: read references/vulnerability-kb.md for condensed detection heuristics per CWE class. Feed the relevant CWE heuristics into each agent's prompt. Full Vul-RAG entries with code examples: knowledge-vault/docs/security/cwe/.

Based on MultiVer [2602.17875] and VulAgent [2509.11523] patterns:

Aspect	What to trace
Injection & Input Validation	SQL/NoSQL/command/LDAP injection, XSS, path traversal, template injection
Auth & Access Control	Auth bypass, privilege escalation, IDOR, missing authorization checks
Concurrency & State	Race conditions, TOCTOU, deadlocks, shared mutable state, atomicity violations
Memory & Resources	Buffer overflows, use-after-free, resource leaks, unbounded allocations
Error Handling & Recovery	Swallowed errors, info leakage in errors, incomplete cleanup, missing rollback
Cryptography & Secrets	Weak algorithms, hardcoded secrets, improper random, timing attacks
Business Logic	State machine violations, numeric overflow in prices, missing validation of business rules

Present selected aspects to user: "I'll focus review on these {N} aspects: ..."

Step 3b: Launch focused agents

For each aspect, launch ONE Agent with a FOCUSED prompt:

You are reviewing a plan document with a SINGLE focus: {ASPECT_NAME}.
Ignore everything outside your focus area — other reviewers handle those.

## Your focus: {ASPECT_NAME}
{ASPECT_DESCRIPTION — 2-3 sentences explaining what to look for}

## Plan to review
{reference the plan document path — the latest version with all prior fixes}

Read the ENTIRE plan but analyze ONLY through the lens of {ASPECT_NAME}.
Go deep: trace every {aspect-relevant thing} end-to-end. Check that every
scenario is complete, every interface is specified, every edge case is handled.

## Output format
FINDING: {one-line description}
SECTION: {which section}
SEVERITY: HIGH | MEDIUM | LOW
ASPECT: {ASPECT_NAME}
EVIDENCE: {quote, max 2 lines}
FIX: {concrete change}

If clean — output: "NO_FINDINGS — {ASPECT_NAME} review clean."

Launch ALL aspect agents in a SINGLE message (parallel).

After Round 3

Same dedup + synthesis. Present focused report.

Stop criteria: 0 high + <=2 medium → STOP. Otherwise ask about Round 4.

---

Step 4: ROUND 4 — Focused + Multisampling (optional, expensive)

Purpose: maximum depth. Only for critical plans where Round 3 still found high-severity issues.

Gate: ask user explicitly: "Round 3 still found {N} high-severity issues. Round 4 will launch {aspects x 2-3} agents (~{estimate} tokens). Continue?"

For each aspect from Round 3 that had findings, launch 2-3 agents with the same focused prompt. Same parallel launch pattern.

After Round 4

Final synthesis. At this depth, the plan should be clean. If still finding high-severity issues → the plan likely needs structural rework, not just polish. Tell the user.

---

Output Format (used after each round)

====================================================
  ROUND {N}: {BROAD | MULTISAMPLE | FOCUSED | FOCUSED+MULTISAMPLE}
  Agents: {count}  |  New findings: {count}  |  Dupes removed: {count}
====================================================

-- HIGH ({count}) ------------------------------------
  1. [{aspect}] {description}
     Section: {section reference}
     Evidence: "{quoted text}"
     Fix: {concrete change}
     Confidence: {HIGH if found by multiple agents, MEDIUM otherwise}

-- MEDIUM ({count}) ----------------------------------
  2. [{aspect}] {description}
     ...

-- LOW ({count}) -------------------------------------
  3. ...

====================================================
  CUMULATIVE: {total_high} high / {total_medium} medium / {total_low} low
  RECOMMENDATION: CONTINUE -> Round {N+1} | STOP - plan is clean
====================================================

---

Step 5: Final summary

After the last round (wherever the process stops):

====================================================
  PLAN SWARM REVIEW COMPLETE
====================================================
  Rounds executed: {N}
  Total agents launched: {count}
  Total findings: {count} ({fixed} fixed, {deferred} deferred)

  By severity:
    HIGH:   {count found} -> {count fixed}
    MEDIUM: {count found} -> {count fixed}
    LOW:    {count found} -> {count fixed}

  Round breakdown:
    R1 (broad):       {findings_count} findings
    R2 (multisample): {findings_count} findings
    R3 (focused):     {findings_count} findings
    R4 (focus+multi): {findings_count} findings

  VERDICT: {HARDENED | IMPROVED | NEEDS_REWORK}
====================================================

Verdicts:

• HARDENED — all high fixed, <=3 medium remaining → safe to implement
• IMPROVED — significant issues found and fixed, some medium deferred
• NEEDS_REWORK — structural issues remain, plan needs major revision

---

Gotchas

• Token cost: Round 4 with 7 aspects x 3 samples = 21 agent launches. Always confirm with user before expensive rounds.
• Plan mutations between rounds: after applying fixes, the plan changes. Each new round MUST read the UPDATED plan, not the original. Reference the file path, not inline text, so agents always read current version.
• Subagent depth: Agent tool subagents cannot launch sub-subagents. Each reviewer runs Read/Grep/Glob inline. This is fine for plan review (the plan is typically 1-3 files).
• Diminishing returns: Round 4 typically finds 1-3 medium issues. If Round 3 found 0 high, skip Round 4.
• False positives: multisampling creates duplicates. The dedup step (after each round) is critical — don't count the same issue from 3 agents as 3 issues.
• Not for code review: this skill reviews PLANS. For code review use /deep-review (competency-based parallel code review).

When to use this vs other review skills

Scenario	Use
Quick architecture check	/plan-eng-review
CEO-level scope challenge	/plan-ceo-review
Design/UX review	/plan-design-review
Code diff review (pre-merge)	/review or /deep-review
Thorough plan hardening before implementation	/plan-swarm-review (plan mode)
Plan with many interacting components	/plan-swarm-review (plan mode)
High-stakes plan (infra, security, payments)	/plan-swarm-review (plan mode)
Security audit of a module/codebase	/plan-swarm-review (code mode)
Pre-release vulnerability hunt	/plan-swarm-review (code mode)
Bug hunt when "something is wrong but tests pass"	/plan-swarm-review (code mode)