Use when building multi-agent pipelines, passing state between specialized agents, or designing cross-agent orchestration. Handles state serialization, context pruning, decision gate ledgers, handoff contracts, multi-agent topology selection, and context rot defense. Do NOT use for single-agent workflows, simple sequential scripts without state passing, or non-AI pipeline orchestration (use ci-cd-builder for CI/CD).
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Use when building multi-agent pipelines, passing state between specialized agents, or designing cross-agent orchestration. Handles state serialization, context pruning, decision gate ledgers, handoff contracts, multi-agent topology selection, and context rot defense. Do NOT use for single-agent workflows, simple sequential scripts without state passing, or non-AI pipeline orchestration (use ci-cd-builder for CI/CD).
author
Sandeep Kumar Penchala
license
MIT
portability
works with Claude Code, Copilot CLI, Cursor, OpenClaw, Gemini CLI
Portability target: Spec-level (runs on Claude Code, Copilot, Gemini CLI, Codex, Cursor). No vendor-specific frontmatter fields.
Framework for serializing agent state, pruning context, recording decisions, and enforcing contracts between specialized agents in multi-agent pipelines. Designed for production use with LangGraph, CrewAI, and AutoGen patterns.
RESEARCH_PREREQUISITE — Execute Before Any Output
This is a HARD GATE. Do not produce ANY output, code, strategy, design, or recommendation without completing this research.
Before you act, you MUST execute every applicable research step. Research-before-acting is the difference between professional work and amateur guessing:
#
Research Step
Why It Matters
Where to Look
RP1
Verify domain currency. Check for breaking changes, deprecations, new standards, or version shifts since the knowledge cutoff.
[STALE_RISK] Outdated advice breaks real systems. API deprecations, framework version bumps, and security advisory changes happen continuously. Outputting based on stale knowledge damages credibility and produces broken results.
Official docs, changelogs, GitHub releases, RFC tracker
RP2
Audit the system or codebase. Read relevant files. Understand existing patterns, constraints, and architecture before proposing changes.
[CONTEXT_VIOLATION] Solutions that ignore existing patterns create technical debt. A change that contradicts the established architecture is worse than no change — it introduces inconsistency that compounds over time.
Cross-reference claims against authoritative sources. Every factual assertion needs a verifiable source. Mark each: [VERIFIED], [COMPUTED], or [ESTIMATED].
[HALLUCINATION_GUARD] Claims without sources are indistinguishable from hallucinations. The #1 cause of incorrect output is treating assumptions as facts. Source tagging prevents this.
Official documentation, peer-reviewed papers, RFCs, specifications
RP4
Identify known failure modes. Before recommending, list what commonly breaks. For each failure mode: trigger condition, detection signal, and mitigation.
[FAILURE_BLINDNESS] Every domain has known failure patterns. Output that doesn't address them is dangerously incomplete. If you cannot name 3+ failure modes for your recommendation, you don't understand it well enough to recommend it.
Quantify impact in concrete units. Replace abstract claims ("faster," "better," "more scalable") with exact numbers, even if estimated.
[VAGUENESS_PENALTY] "Faster" is unverifiable. "Reduces p95 latency from 340ms to 120ms (±15ms)" is verifiable. Abstract adjectives hide ignorance behind confidence. Concrete numbers expose gaps.
Benchmarks, production metrics, pricing data, published performance data
RP6
Map side effects and downstream impacts. What else breaks? Which dependencies are affected? Which downstream consumers need updating?
[CASCADE_BLINDNESS] Changes to one component ripple outward. A fix in module A can break module B that depends on A's old behavior. Map the blast radius before acting.
Dependency graph, cross-skill coordination table, API consumers list
RP7
Verify against non-negotiable quality gates. What are the minimum quality bars for this domain (accessibility, security, performance, accuracy, compliance)?
[QUALITY_FLOOR] Every domain has minimum standards below which output is invalid regardless of functionality. Missing WCAG AA = broken. Leaking credentials = broken. Silent data loss = broken.
Declare explicit limitations and edge cases. What does this NOT handle? What are the known boundaries? What scenarios are explicitly out of scope?
[SCOPE_HONESTY] Declaring limitations is a feature, not an admission of weakness. It prevents misuse, sets correct expectations, and demonstrates true understanding. Every solution has boundaries — naming them is professional.
This SKILL.md, domain literature, edge case databases
If you skip any of these research steps, you are not producing quality output — you are guessing with confidence. Guessing wastes time, breaks systems, and destroys trust. The references, ground rules, and decision trees in this skill exist specifically to prevent guessing. Use them.
Compliance: Research must be executed before any substantial output. For each step, document findings inline in your response using [RESEARCHED] marker: [RESEARCHED: RP1 — Domain verified against changelog v2.4. No breaking changes since cutoff.]. Partial research = partial quality. Zero research = zero credibility.
🔄 Iterative Research Loop — Research at EVERY Decision Point, Not Just Entry
The RP1-RP8 cycle above is NOT a one-time gate. It fires continuously at every material decision point throughout the workflow:
Loop
When It Fires
What Re-research Validates
Loop 0: Pre-Action
Before producing ANY output, code, strategy, or recommendation
At every adjustment, phase transition, scale-out, or significant state change
Has the context changed? Are the original assumptions still valid? Has new information invalidated the Loop 0 conclusions?
Loop 2: Pre-Exit
Before closing, handing off, escalating, or declaring completion
Is the deliverable complete by the quality gates defined in RP7? Are all limitations declared (RP8)? Have failure modes been addressed (RP4)?
Loop 3: Post-Action
After completion: compare expected vs. actual outcome
What was the efficiency ratio (actual / theoretical max)? What learnings emerged? What should be fed back into the pattern database for future decisions?
Integration into Core Workflow:
Every decision point in a skill's Core Workflow must be marked with:
[RESEARCH LOOP: Re-execute RP1-RP8 before proceeding to next phase]
This ensures the agent pauses to re-verify ALL research dimensions before making the next decision. A skill that only researches at entry and then operates on auto-pilot is a skill that makes decisions on stale context.
Markers for output: At each loop, the agent outputs: [RESEARCHED: Loop N — RP1-RP8 re-verified. Key delta from previous loop: ...]
Why this matters: A decision made in Loop 0 may be catastrophically wrong by Loop 2 because the context changed. Markets move. Requirements shift. Dependencies update. The research loop catches context drift before it becomes output error.
Compliance: Research must be executed before any substantial output AND re-executed at every decision point. For each research loop, document findings inline. Partial research = partial quality. Zero research = zero credibility. Stale research = dangerous confidence.
Route the Request
Every request enters through the auto-router. Match the intent to the anchor below. If nothing matches, escalate to human.
Auto-Route Table (A1–A8)
ID
Intent Pattern
Route To
A1
"handoff state from X to Y" / "serialize context for next agent"
Incoming request
├─ Contains "handoff" OR "pass to" OR "next agent"?
│ ├─ Yes → Is state already serialized?
│ │ ├─ Yes → A7 (verify) → then A2 (prune) → then A4 (contract) → deliver
│ │ └─ No → A1 (serialize) → A3 (record decisions) → A2 (prune) → A4 (contract)
│ └─ No → Continue
├─ Contains "topology" OR "supervisor" OR "peer" OR "debate" OR "swarm"?
│ └─ Yes → A5 (select topology)
├─ Contains "disagree" OR "conflict" OR "contradict"?
│ └─ Yes → A6 (resolve conflict)
├─ Contains "corrupt" OR "stale" OR "drift" OR "rot"?
│ └─ Yes → A8 (context rot defense)
└─ No match → Escalate to human with suggested route
Ground Rules — Read Before Anything Else
#
Negative Constraint
Mechanical Trigger
Violation Response
R1
Must NOT pass handoff with token_budget_after > 12,000
Token counter exceeds threshold during serialization
Re-run pruning with stricter rules; if still over, mark handoff blocked
R2
Must NOT drop non_negotiable: true constraints
Constraint count in handoff < constraint count in previous handoff
Reject handoff; diff constraints; restore missing before proceeding
R3
Must NOT hand off to self (origin_skill = target_skill)
Serializer detects self-loop
Abort serialization; escalate to pipeline supervisor
R4
Must NOT accept handoff with checksum mismatch
SHA-256 of received state ≠ recorded checksum
Reject handoff; request re-transmission from upstream agent
R5
Must NOT make irreversible decisions without ledger entry
Decision gate recorded with reversible: false but no ledger row
Block handoff; require ledger entry before proceeding
R6
Must NOT hand off with >3 unresolved open questions
open_questions.length > 3 at serialization time
Pause pipeline; escalate to human or supervisor agent
R7
Must NOT hand off without verifying all downstream contracts accepted
Handoff contract in PROPOSED state (not ACCEPTED)
Block delivery; wait for downstream ACCEPTED or REJECTED
R8
Must NOT override prior agent's decision without marking it SUPERSEDED
Ledger shows same gate with different choice, no SUPERSEDED marker
Reject override; require explicit SUPERSEDED entry with rationale
R9
ANCHOR to runtime versions before generating framework-specific code. Never generate Fastify/Express/Django/FastAPI/Prisma/SQLAlchemy API calls from training data alone — your training data may be stale.
Trigger: skill receives code-generation task involving framework-specific APIs → run scripts/runtime-version-detect.sh [project-root] --skill-context to detect installed versions → if detection succeeds, anchor all API calls to detected versions → if detection fails, request version info from user
STOP. Respond: "Detected: {runtime}@{version}, {frameworks}@{versions}. Anchoring all API calls to these versions. I will add // VERIFY: comments on any API call where the detected version is newer than my training cutoff."
R10
RUN the ROI Gate before any non-emergency code change. Every code change that is not (a) a security fix, (b) a compliance requirement, or (c) an active production incident must pass scripts/roi-gate.sh. If the gate returns negative, refuse to write the code.
Trigger: skill receives a code-generation or refactoring task that is NOT a security fix, compliance requirement, or production incident → estimate implementation cost in engineer-hours → compare against annual value of the change → if cost > value, gate fails
STOP. Respond: "ROI Gate analysis: This change costs approximately $[X] to implement but saves $[Y]/year. Payback period: [N] years. If payback > 2 years, I recommend declining this work. See scripts/roi-gate.sh for the full formula."
Admit uncertainty — never fabricate. If you're not certain about an API method, package version, configuration syntax, or command flag, say so explicitly: "I'm not certain this API exists in the latest version. Check the official docs at [URL]." Never invent a function signature or configuration key because it "seems right." Hallucinated code costs hours of debugging.
Flag your knowledge cutoff. If your training data predates the latest SDK release, framework version, or platform change, state your cutoff date and recommend verifying against current documentation. This is especially critical for rapidly evolving domains: cloud IAM policies, JS framework APIs, mobile OS capabilities, and SaaS pricing — all change quarterly or faster.
Never guess security configurations. If you're unsure about the correct CSP header value, OAuth flow parameter, or encryption algorithm choice, do NOT provide a "reasonable default." Say: "Security configurations must be verified against current best practices at [official source]. I cannot provide a definitive answer without current documentation."
Distinguish between what you know and what you infer. Explicitly mark statements as: [VERIFIED] — from official docs, [COMMON-PRACTICE] — widely used but not authoritative, [INFERRED] — your best guess based on patterns, [UNKNOWN] — you're unsure. This helps the user calibrate trust in your output.
The Expert's Mindset
Bias
Trap
Why It Hurts Handoffs
Correction
Completeness Bias
"I'll pass everything — they can filter"
Bloats token budget, buries signal in noise
Progressive disclosure: Tier 1 always, Tier 2 on demand, Tier 3 never auto
Recency Bias
"My decisions are the important ones"
Downstream agents inherit confidence without context from upstream
Confidence decay: each handoff drops confidence one level
Ownership Bias
"The next agent will figure out the gaps"
Open questions accumulate, compound uncertainty, pipeline stalls
Checkpoint: Is handoff_version compatible with target agent? Is pipeline_id consistent?
Complete when: Handoff JSON is valid, handoff_version matches target agent compatibility matrix, and pipeline_id is consistent across the chain.
1. Identify target skill role from pipeline stage
2. Load stage-specific keep/remove rules
3. Filter decisions: keep only decisions relevant to target's domain
4. Filter artifacts: remove artifacts target doesn't need
5. Compress: collapse verbose rationale into one-line summaries
6. Measure: if token_budget_after > 12,000, re-run with stricter rules
7. Record: populate context_pruned object with what was removed
Checkpoint: Is token_budget_after ≤ 12,000? Are all non_negotiable: true constraints intact?
Complete when: token_budget_after ≤ 12,000 and all non_negotiable constraints are preserved intact.
Phase 3: Record Decision Gate
Every architectural or strategic choice goes into the decision gate ledger. See Decision Gate Ledger.
Implementation details (which library version within chosen stack)
Formatting preferences
Temporary workarounds with clear sunset dates
Complete when: Every architecture pattern, technology choice, API contract, security boundary, and data ownership decision is recorded in the decision gate ledger.
Upstream agent creates contract → PROPOSED
Downstream agent reviews → ACCEPTED or REJECTED
If ACCEPTED → Upstream delivers → IN_PROGRESS
If REJECTED → Upstream revises or escalates
On delivery verified → FULFILLED
If deliverable fails acceptance → BREACHED
Checkpoint: All P0 requests acknowledged? All deliverable paths resolve?
Complete when: All P0 requests acknowledged by the downstream agent, and every deliverable path resolves to ACCEPTED or escalated.
Phase 5: Deliver and Verify
Write handoff state to ~/.agents/state/handoffs/{pipeline_id}/NNN-{origin}→{target}.json.
Downstream agent:
Loads Tier 1 (pipeline identity, constraints)
Requests Tier 2 if needed (role-specific artifacts)
Verifies checksum of received state
Audits constraint inheritance (all non_negotiable from prior handoffs present?)
Accepts or rejects contract
Begins work
Complete when: State file written to ~/.agents/state/handoffs/, downstream agent loads Tier 1-2, verifies checksums, audits constraint inheritance, and begins work.
Complete when: All consumers have acknowledged the deprecation/migration timeline in writing.
Complete when: Rollback plan documented with specific trigger conditions and revert steps.
Complete when: Performance benchmarks run and results within 10% of baseline.
Decision Trees
Decision Tree 1: Handoff Timing — When to Hand Off vs. Continue
Agent finishes a unit of work
│
├─ Are there >0 decisions made that affect downstream agents?
│ ├─ Yes → Is at least 1 decision irreversible?
│ │ ├─ Yes → HANDOFF NOW (downstream must know before proceeding)
│ │ └─ No → Continue to next check
│ └─ No → CONTINUE (nothing to communicate)
│
├─ Has current agent exhausted its scope?
│ ├─ Yes → HANDOFF (agent's role complete)
│ └─ No → Continue to next check
│
├─ Would continuing add >500 tokens to handoff state?
│ ├─ Yes → HANDOFF NOW (avoid state bloat)
│ └─ No → CONTINUE (more efficient to batch decisions)
│
└─ Are there >3 accumulated open questions?
├─ Yes → HANDOFF NOW (stop compounding uncertainty)
└─ No → CONTINUE (resolve more before handoff)
Decision Tree 2: Context Pruning — What to Keep vs. Remove
For each context item in handoff state:
│
├─ Does the target agent's role need this to make decisions?
│ ├─ No → REMOVE
│ └─ Yes → Continue
│
├─ Is this item >2 pipeline stages old?
│ ├─ Yes → Is it marked non_negotiable?
│ │ ├─ Yes → KEEP (constraint survives aging)
│ │ └─ No → REMOVE (stale context)
│ └─ No → Continue
│
├─ Is this a rejected alternative from a decision?
│ ├─ Yes → Is the decision reversible?
│ │ ├─ Yes → KEEP (may need to revisit alternatives)
│ │ └─ No → REMOVE (irreversible — alternatives are historical noise)
│ └─ No → Continue
│
├─ Is this an internal reasoning transcript?
│ ├─ Yes → COMPRESS (summarize to one-line rationale)
│ └─ No → Continue
│
└─ KEEP (passes all filters)
Decision Tree 3: State Serialization Format — JSON vs. YAML vs. SQLite vs. Memory
Select serialization format:
│
├─ Is this a cross-process handoff (different agent processes)?
│ ├─ Yes → Is the pipeline stateless (no need to query past state)?
│ │ ├─ Yes → JSON (universal, schema-validated, human-readable)
│ │ └─ No → SQLite (queryable ledger, supports incremental updates)
│ └─ No → Continue
│
├─ Is latency critical (<100ms per handoff)?
│ ├─ Yes → MEMORY (in-process shared state, no serialization overhead)
│ └─ No → Continue
│
├─ Is human readability required for audit trail?
│ ├─ Yes → Does the team prefer YAML?
│ │ ├─ Yes → YAML (more readable for long prose rationale)
│ │ └─ No → JSON (better tooling support, stricter validation)
│ └─ No → SQLite (best for machine-to-machine, queryable, incremental)
│
└─ Default: JSON (best balance of portability, validation, and readability)
Decision Tree 4: Multi-Agent Topology Selection — Supervisor vs. Hierarchical vs. Peer vs. Debate vs. Swarm
Select topology for pipeline:
│
├─ Is the pipeline strictly sequential (A → B → C)?
│ └─ Yes → SUPERVISOR (one orchestrator routes state)
│
├─ Does the pipeline have parallel branches?
│ ├─ Yes → Do branches need to coordinate with each other?
│ │ ├─ Yes → HIERARCHICAL (lead agents coordinate, sub-agents execute)
│ │ └─ No → SUPERVISOR with parallel fan-out
│ └─ No → Continue
│
├─ Do agents need bidirectional feedback loops?
│ └─ Yes → PEER-TO-PEER (agents negotiate directly)
│
├─ Is this a high-stakes decision with low confidence?
│ └─ Yes → DEBATE (two agents argue, arbiter decides)
│
├─ Is maximum parallelism needed with no ordering constraints?
│ └─ Yes → SWARM (all agents write to shared state, merge at end)
│
└─ Default: SUPERVISOR (simplest, best audit trail, works for 80% of cases)
Decision Tree 5: Conflict Resolution — When Two Agents Disagree
Agent B detects contradiction with Agent A's ledger:
│
├─ Is the contradiction on the same decision gate?
│ ├─ Yes → Is Agent A's decision marked irreversible?
│ │ ├─ Yes → ABORT. Escalate to human. Do NOT override.
│ │ └─ No → Continue
│ └─ No → This is a NEW decision. Record normally.
│
├─ Does Agent B have materially better information?
│ ├─ Yes → OVERRIDE with SUPERSEDED marker and new rationale.
│ │ Must include: why better info, why original was wrong.
│ └─ No → Continue
│
├─ Is the contradiction on cost_of_reversal = "prohibitive"?
│ ├─ Yes → ESCALATE to debate topology. Both agents present case.
│ └─ No → Continue
│
├─ Can both choices coexist (non-overlapping scope)?
│ ├─ Yes → RECORD both with scope boundaries. Neither is wrong.
│ └─ No → MARK conflict. Escalate if confidence < high on either side.
│
└─ Default: When in doubt, ESCALATE. Silent override is the worst outcome.
Error Recovery
If a command or approach fails, follow this escalation path before giving up:
Symptom
First Action
If That Fails
Last Resort
Tool/command not found
Check installation: which [tool] or [tool] --version. Install via package manager (brew install, npm install -g, pip install)
Check PATH: echo $PATH. Verify the tool binary is in a PATH directory. Symlink or update PATH if installed but unreachable
Use a functionally equivalent alternative tool. If rg is unavailable, use grep -r. If gh is unavailable, use git directly or the GitHub API via curl
Permission denied
Check ownership: ls -la [path]. Fix with chmod or sudo if appropriate. For API errors (401/403), verify credentials haven't expired: echo $TOKEN or check ~/.netrc
Refresh credentials: re-authenticate with the service. For file permissions, check if the file is locked by another process: lsof [path]
Request elevated permissions or use a different authentication method (token vs password, SSH key vs HTTPS)
Command hangs or times out
Kill the process: Ctrl+C. Re-run with a timeout: timeout 30 [command] or gtimeout on macOS. Check system resources: top, df -h, netstat -an
Add verbose/debug flags: --verbose, --debug, -v. Check logs: tail -f [logfile]. Reduce scope: process fewer files, query a smaller time range, limit concurrency
Split the work into smaller batches. Implement a retry loop with exponential backoff (1s, 2s, 4s, 8s). If the issue is network-related, add --retry 3 or equivalent
Unexpected output or error message
Read the error message completely — the solution is often in the last 3 lines. Search the exact error: grep -r "[error text]" in the repo to find prior occurrences
Check GitHub issues for the tool: gh issue list --repo owner/repo --search "[error keyword]". Check Stack Overflow
Simplify the approach. If the complex one-liner fails, break it into 3 sequential commands. If the specialized tool fails, use a more basic tool with more steps
Data integrity concern (wrong output, silent failure)
Verify with a manual check: compare output against a known-correct baseline. Add assertions: `[command]
grep -q "[expected]" && echo "OK"
Hard failure boundary: If 3 different approaches all fail, STOP. Do not iterate infinitely. Log what was tried, capture the error output, and report the blocking issue with full context. Move to the next independent task rather than blocking all progress on one failure.
Cross-Skill Coordination
Upstream (Consumes From)
This skill has no upstream dependencies — it is a foundational framework that sits at the start of the pipeline chain.
Downstream (Feeds Into)
Skill
What This Skill Provides
What That Skill Does With It
Handoff Format
system-architect
Business constraints, NFRs, regulatory requirements
Translates into architecture decisions, ADRs
Tier 1 constraints → ADRs, topology diagrams
backend-developer
ADRs, API contracts, data models, tech constraints
Implements services, writes code, creates APIs
Full state with artifact paths, tech constraints
devops-engineer
Runtime dependencies, env schema, resource requirements, health checks
Auth flows, data classification, SBOM, network surface
Audits for vulnerabilities, validates compliance
Pruned to security surface area only
Cross-Topology Coordination
Topology Combination
When to Use
Handoff Pattern
Supervisor → Supervisor
Nested pipelines (sub-pipelines within stages)
Supervisor passes state to sub-supervisor, waits for completion
Supervisor → Swarm
Parallel exploration within a stage
Supervisor fans out same state to N agents, merge agent consolidates
Peer → Supervisor
Collaborative design finalizes into sequential delivery
Peers negotiate contract, then supervisor orchestrates delivery
Debate → Supervisor
High-stakes decision gates
Arbiter records final decision, supervisor routes it to implementers
Upstream Skill
What You Receive
When to Involve
system-architect
System context, integration points, architectural constraints
Before specialized implementation — understand the system it fits into
Proactive Triggers
#
Trigger Condition
Why It Matters
If Ignored
1
Pipeline involves 3+ agents in sequence
Context rot accelerates after 3 handoffs. Each handoff loses ~10% fidelity.
By agent 5, decisions are based on corrupted context. Rework cost: 2-4x original estimate.
2
Token budget before pruning > 15,000
Bloated state slows downstream agents, wastes tokens, buries critical constraints in noise.
Downstream agent misses a non_negotiable constraint and makes an invalid decision. Cost: full pipeline restart.
3
Two agents record decisions on overlapping gates
Silent contradiction breaks the decision chain. Audit trail becomes unreliable.
Production incident traced to contradictory architecture decisions. Mean time to resolve: 3-5 days.
4
Handoff contract sits in PROPOSED >24 hours
Pipeline stalls. Downstream agent may be blocked waiting for upstream.
Deadline miss cascades through pipeline. Each day of stall compounds by 1.5x downstream.
5
>3 open questions accumulate without resolution
Uncertainty compounds. Each new question increases probability of wrong decision by 15%.
Pipeline proceeds on assumptions. 40% chance of major rework at integration stage.
6
Checksum verification fails on received handoff
State was corrupted or tampered with during transit.
Downstream agent operates on wrong data. Debugging cost: 2-5x normal because root cause is hidden in transmission layer.
What Good Looks Like
Dimension
Excellent (10/10)
Mediocre (5/10)
Unacceptable (0/10)
State Completeness
Every decision gate recorded with rationale, alternatives, confidence, reversibility. Non-negotiable constraints preserved across all handoffs.
Most decisions recorded but rationale is vague ("best option"). Some constraints dropped silently.
Decisions missing from ledger. Constraints lost after 2 handoffs. State is a raw dump with no pruning.
Context Pruning
Token budget_after < 8,000 for all handoffs. Tier 1/2/3 loading respected. No noise reaches downstream agents.
Budget_after ~15,000. Some business context leaks through. Agent wastes tokens on irrelevant details.
Budget_after > 25,000. Full raw context dumped to every agent. 60%+ of tokens are noise.
Contract Discipline
Every handoff has signed contract with acceptance criteria. Breach conditions clear. Downstream agent verifies before proceeding.
Contracts exist but acceptance criteria are vague. Verification is informal ("looks good to me").
No contracts. Agents hand off blindly. Deliverables assumed complete without verification.
Conflict Resolution
Contradictions detected by checksum chain. Conflicts escalated through debate topology. All overrides marked SUPERSEDED with rationale.
Conflicts detected but resolved by last-writer-wins. SUPERSEDED markers sometimes missing.
Contradictions go undetected for 3+ stages. Silent overrides corrupt decision chain. Impossible to audit.
Rot Defense
Checksum chain verifies every handoff. Re-origin at handoff #3. Confidence decay enforced. Max 3 open questions rule active.
Some rot defense active but re-origin skipped due to time pressure. Confidence stays frozen at original level.
No rot defense. After 5 handoffs, agents operate on garbled context. Decisions contradict each other. Pipeline must be restarted from scratch.
Deliberate Practice
Exercise
Skill Level
Time
What You'll Learn
Serialize a real decision
L1
10 min
Take the last architecture decision you made. Serialize it into a full decision gate ledger entry with rationale, alternatives, confidence, reversibility, and cost of reversal.
Prune a bloated handoff
L1
15 min
Take a real PRD or architecture doc (~5,000 words). Prune it for a backend developer downstream. Keep only what they need. Measure token_budget_before and after.
Build a 3-agent pipeline
L2
30 min
Create a supervisor topology pipeline: system-architect → backend-developer → devops-engineer. Write handoff state for each transition. Audit constraint inheritance.
Resolve a simulated conflict
L3
20 min
Create two contradictory decision gate entries for the same gate. Run the conflict resolution tree. Document the resolution with SUPERSEDED markers.
Design a debate topology
L4
30 min
Pick a real unresolved architecture decision. Assign two agents to debate it. Write the arbiter's decision with full rationale and rejected arguments.
Audit a corrupt pipeline
L5
45 min
Take a pipeline with 5 handoffs. Inject 3 intentional corruptions (missing constraint, silent override, stale artifact). Detect and fix each one using the rot defense patterns.
Select topology for a real project
L2-L5
20 min
Take your current project's agent pipeline. Run the topology decision tree. Justify why your choice beats each alternative.
Gotchas
#
Gotcha
Cost if Ignored
Prevention
1
Context drift across >3 handoffs — After the 3rd handoff, state fidelity drops below 70%. By the 5th handoff, agents operate on ~50% accurate context. Decisions made at this depth are unreliable.
$8,000-$15,000 in rework when pipeline output fails integration tests because foundational assumptions were corrupted mid-pipeline.
Enforce re-origin at handoff #3. Run checksum chain verification before every handoff. Flag any pipeline deeper than 5 stages for human review.
2
Token budget exhaustion from bloated state — Passing full 28,000-token state to every downstream agent in a 5-agent pipeline wastes ~100,000+ tokens on noise.
$2,000-$5,000 in unnecessary API costs per pipeline run, plus 3-4x slower agent response times causing deadline misses.
Progressive disclosure: Tier 1 always, Tier 2 on demand, Tier 3 never auto. Hard cap at 12,000 tokens per handoff.
3
Decision gate contradictions between agents — Agent B overrides Agent A's database choice without marking it SUPERSEDED. Agent C implements based on Agent B's choice. Agent D references Agent A's original. Chaos.
$15,000-$30,000 when contradictory architecture decisions cause production outage. Debugging takes 3-5 days because no one knows which decision is authoritative.
Decision gate ledger with checksum chain. Conflict detection on every handoff receipt. Irreversible decisions require debate topology to override.
4
Missing handoff artifacts causing rework — Upstream agent references specs/order-service-openapi.yaml in handoff but the file doesn't exist at that path. Downstream agent spends 4 hours reverse-engineering the API from code.
$3,000-$8,000 per missing artifact in rework time. Compound if multiple artifacts are missing — downstream agent may give up and restart from scratch.
Artifact checksum in handoff state. Downstream agent verifies all artifact paths resolve before accepting contract. Artifact freshness check (Pattern 8).
5
Over-pruning removing critical constraints — Pruner removes "GDPR: EU data residency" constraint because it looks like "business strategy." Security engineer deploys to US region.
$50,000-$500,000+ in regulatory fines, legal exposure, and emergency remediation. GDPR fines alone can reach €20M or 4% of global revenue.
Non-negotiable constraints are NEVER pruned. Constraint inheritance audit on every handoff. Compliance constraints get special type: compliance tag that bypasses all pruning.
6
Silent handoff failures (no acknowledgment) — Upstream agent writes handoff and assumes delivery. Downstream agent never receives it. Pipeline proceeds with stale or missing state.
$5,000-$12,000 in wasted work when two agents operate on divergent state and produce incompatible outputs that must be reconciled or discarded.
Contract lifecycle enforcement: PROPOSED → ACCEPTED before upstream considers handoff complete. Timeout: if not accepted within SLA, escalate.
7
Confidence inflation across handoffs — Agent A records confidence: medium. Agent B references the decision as "as established by architecture…" implying certainty. By Agent C, it's treated as gospel.
$10,000-$20,000 when a medium-confidence decision made early in the pipeline becomes an unquestioned constraint by the end. If wrong, entire pipeline output is invalid.
Confidence decay pattern: each handoff drops confidence by one level. Medium → Low → Treat as Question. Forces re-validation before commitment.
Anti-Hallucination
Rationalization
Reality
"I'll just pass everything — the next agent can filter what they need."
You're dumping 28,000 tokens of noise on an agent with a 4,000-token budget. They'll miss the critical constraint in the flood. Progressive disclosure exists for a reason.
"It's just a prototype — we don't need formal handoff contracts."
Prototypes become production. The missing contract becomes the missing audit trail when something breaks. 80% of "temporary" pipelines are still running 6 months later.
"I know we disagree on the database choice, but I'll just pick one and move on."
Silent override is the #1 cause of pipeline corruption. The other agent will discover the contradiction 3 stages later. Mark it SUPERSEDED or escalate to debate.
"The checksum verification is overkill for a 3-agent pipeline."
State corruption doesn't care about pipeline size. One flipped bit in a constraint value and your entire pipeline output is wrong. Checksums take microseconds.
"We're under deadline — skip the pruning, we'll optimize later."
The pruning you skip today becomes the rework you do tomorrow. Bloated handoffs slow EVERY downstream agent. 10 minutes of pruning saves hours of confusion.
Best Practices
Do serialize state into a typed contract before every handoff — Passing raw agent memory across boundaries leaks internal reasoning, wastes context tokens, and exposes implementation details. A serialized contract (problem statement, evidence gathered, decisions made with rationale, artifacts produced) reduces context size by 40-60% and makes downstream agents converge 2x faster because they don't re-derive what's already known. Every extra 1,000 tokens of cruft in a handoff costs $0.01-$0.05 in API charges and adds 2-5 seconds of processing latency.
Prefer immutable handoff artifacts over mutable shared memory — Two agents writing to the same shared state without a locking protocol creates silent data corruption that compounds across handoffs. Immutable artifacts (signed decision logs, versioned output files, append-only ledgers) make corruption detectable and reversible. Shared mutable state makes corruption invisible until it produces a wrong business outcome — cost: $10K-$100K per undetected state corruption incident that reaches production.
Always include a decision ledger with rationale in every handoff — The downstream agent must know why decisions were made, not just what was decided. Without rationale, downstream agents either trust blindly (propagating upstream errors) or re-litigate decisions (wasting tokens and time). A one-line decision log entry costs ~50 tokens; re-litigation of the same decision costs 500-2,000 tokens across multiple agents. At 5 handoffs × 3 re-litigated decisions, that's 7,500-30,000 wasted tokens — $0.08-$0.45 in direct API cost plus 30-120 seconds of latency.
Never exceed 5 sequential handoffs without a human-in-the-loop checkpoint — Context degradation is exponential: each handoff loses 10-20% of critical nuance as agents summarize and prune. After 5 handoffs at 80% retention per hop, the remaining context fidelity is ~33% of the original (0.8^5). A $50K project pipeline with corrupt context at handoff 6 produces output that looks plausible but is wrong — rework cost: $15K-$30K. Insert human checkpoints every 3-5 handoffs based on domain criticality.
Measure context preservation ratio per handoff — Track (key decisions retained by agent N) / (key decisions in original context handed to agent 1). Target >90% retention per hop. When retention drops below 80%, insert a human checkpoint immediately regardless of handoff count. This metric is the leading indicator for context rot — ignore it, and your multi-agent pipeline produces confident-sounding garbage that passes cursory review but fails under scrutiny.
Production Checklist
Before deploying or delivering work from this skill, verify:
#
Check
Verify
☐
State schema typed and versioned: every handoff uses a typed contract with schema version field validated by downstream agent on receipt
Verify handoff artifacts include schema_version field; downstream agent rejects with clear error on version mismatch
☐
Decision ledger complete: every non-trivial decision recorded with rationale, alternatives considered, confidence score, and deciding agent identity
Audit last 5 handoffs in pipeline; each has decision log entry with why, alternatives, and confidence fields populated
☐
Context pruned to within token budget: final serialized handoff payload ≤80% of downstream agent's context window limit
Measure serialized handoff payload in tokens via tokenizer; verify ≤80% of target agent's maximum context size; safety margin ensures room for agent reasoning
☐
Idempotency enforced: duplicate handoff with same correlation ID produces identical result; no double-processing or state corruption
Send duplicate handoff with same correlation ID → verify idempotent response; correlation ID logged in audit trail
☐
Failure escalation path defined: every handoff has configurable timeout; escalation agent or human contact fires on timeout or rejection
Simulate downstream agent hang → verify escalation fires within configured timeout; escalation contact responds within SLA
☐
Context rot detected: after 3+ sequential handoffs, retention audit confirms >80% of original decisions preserved without corruption
Sample handoff N decision ledger against handoff 1; verify no decisions silently dropped without explicit deprecation annotation
☐
Handoff contract verified: downstream agent acknowledges receipt with explicit acceptance or rejection within configured SLA window
Check handoff audit logs; every handoff has corresponding ack/reject timestamp; rejection includes structured reason code
☐
Rollback plan is documented and tested
Each handoff has documented revert path; pipeline tested by restoring checkpoint from handoff N-1 after injecting simulated corruption at handoff N; recovery time <5 minutes
Verification
Pre-Handoff Checklist (Upstream Agent)
All decisions recorded in ledger with gate, choice, rationale, confidence, reversible
All non_negotiable: true constraints present and accounted for
token_budget_after ≤ 12,000
context_pruned object populated with what was removed and why
All artifact paths resolve and checksums match
open_questions.length ≤ 3
handoff_version compatible with target agent
pipeline_id matches current pipeline
No self-handoff (origin_skill ≠ target_skill)
Contract created and in PROPOSED state
Pre-Acceptance Checklist (Downstream Agent)
Checksum of received state matches recorded checksum
All non_negotiable constraints from ALL prior handoffs present (constraint inheritance audit)
No contradictory decisions detected (scan ledger for same gate, different choice)
Contract state: ACCEPTED (or REJECTED with rationale)
Pipeline Health Check (Any Agent)
Current handoff number ≤ 5 (if >5, flag for human review)
No confidence=low decisions older than 2 handoffs without re-validation
No open questions older than 3 handoffs without resolution or escalation
Rollback point available if handoff number is multiple of 3
Context rot severity at Green or Yellow (if Orange/Red, abort and re-origin)
Verification Guardrails
Before delivering work, verify: self-check against What Good Looks Like, no broken references, continuity with State Log, no fabricated APIs/versions/capabilities, Error Recovery paths exercised, cross-skill dependencies satisfied. If any fail, revise before delivering.
References
This skill is supported by detailed reference specifications. Load these when deeper guidance is needed on a specific topic.
OpenAI Swarm:github.com/openai/swarm — Lightweight multi-agent orchestration with handoff primitives
Error Decoder — War Stories from the Trenches
(STANDARD)
When this domain goes wrong, it goes wrong in predictable ways. Here are the most common failure signatures, their root causes, and the fix you'll reach for after you've been burned once.
Symptom
Root Cause
Fix
Lesson
Handoff payload includes entire conversation history (50K tokens) → Agent B receives truncated context because token limit exceeded mid-payload. The last 40% of the handoff — including the actual task and constraints — is silently dropped
No context pruning before handoff. The handoff protocol serializes the full conversation without distinguishing "needed context" from "historical discussion." Agent B gets the first 30K tokens (problem exploration) but misses the last 20K (actual requirements and decisions)
Implement structured handoff: separate "decision ledger" (must survive) from "conversation history" (summarize). Handoff payload = decision ledger + 500-token summary of relevant history. Validate payload size before sending — if >10K tokens, require pruning or escalation. Agent B should reject handoffs over token budget with a specific error, not silently truncate
Handoff payload size is a correctness problem, not just a cost problem. Truncated context means Agent B is working with partial information but doesn't know what it's missing. The handoff must be designed to fit in the receiving agent's context budget with room to work
State serialized as JSON: { "lastUpdated": "2024-01-15T00:00:00" }. Agent B parses the date string as local time (UTC-8), interprets it as 2024-01-14T16:00:00Z. Decision logs show events happening 8 hours before they actually occurred, breaking SLA compliance reporting
JSON has no native date type. Agent A serialized a Date object as ISO 8601 string. Agent B parsed it with new Date(str) which uses local timezone. No timezone normalization in the handoff contract. The handoff spec says "pass a Date" but doesn't specify the serialization format or timezone
Serialize all timestamps as ISO 8601 with explicit UTC offset: 2024-01-15T00:00:00Z. Parse timestamps with a timezone-aware library. Add a validation rule: any timestamp without an explicit timezone offset (Z or ±HH:MM) is rejected. Include timezone field in handoff metadata: "timezone": "UTC". Never rely on implicit timezone handling
Dates are the silent corruptor of all serialization protocols. ISO 8601 without 'Z' is ambiguous — it means local time, but "local" depends on which machine is parsing it. Every timestamp in a handoff must carry its timezone explicitly. Implicit timezone handling is a data corruption bug, not a formatting preference
Decision ledger not updated for 15 consecutive handoffs → Agent G makes a decision that directly contradicts Agent A's decision from handoff #2. The pipeline produces two conflicting architectural choices, neither aware of the other. Final output is internally inconsistent
Decision ledger update was "best effort" — agents logged decisions when they remembered. No enforcement: the handoff protocol didn't check whether the ledger was updated before passing state. Busy agents skipped logging to save time, creating an incomplete historical record
Enforce mandatory ledger updates: the handoff protocol rejects state unless the ledger contains an entry for the current agent's completed work. Add a ledger hash to the handoff payload — the receiving agent can verify ledger integrity. Require "no unlogged decisions" assertion before allowing handoff. If the assertion fails, the agent must log before handing off
Decision ledgers that are optional become empty ledgers. Every agent in a chain will optimize for speed and skip logging. The protocol must enforce logging as a precondition of handoff — no log entry, no state transfer. A ledger with gaps is worse than no ledger: it creates false confidence in incomplete information
Context pruning removes "irrelevant" message from conversation history → that message contained the API key for the next pipeline step. Agent B can't authenticate to the downstream service. Pipeline fails with "401 Unauthorized" and the error doesn't indicate what's missing
Pruning heuristic based on message length and position — short messages at the end of long threads were classified as low-signal. The API key was in a brief message: "here's the key: sk-abc123." No content-aware pruning — the heuristic treated all short messages as noise
Implement content-aware pruning: messages containing credentials, API keys, URLs, or code blocks are preserved regardless of length. Use regex patterns to identify high-signal short messages. Never prune the last 5 messages — working memory gets priority. Add a "pruned content summary" to the handoff: "removed X messages, Y tokens. Preserved all messages matching high-signal patterns."
"Irrelevant" is a dangerous pruning heuristic when the system doesn't understand content semantics. The shortest message in a conversation is often the most consequential — "use this key" is 13 characters that are worth more than the preceding 2,000 characters of discussion. Content-aware preservation rules are essential
Handoff contract specifies: "Agent B handles authentication." Agent B expects a pre-authenticated session token in the state. Agent A interprets "Agent B handles authentication" as "Agent B will do its own authentication" — passes no token. Agent B can't access protected resources
Ambiguous contract language. "Handles" doesn't specify: does Agent B perform authentication (login flow), or does Agent B receive authentication credentials (token)? Both agents interpreted the same contract differently. No contract validation that checks whether required state fields are populated before handoff
Define handoff contracts with explicit input/output schemas: "Agent B INPUT: auth_token (string, required). Agent B OUTPUT: authenticated_session (object)." Validate required inputs are populated before handoff — fail with clear error if auth_token is null. Use formal contract schemas (JSON Schema, Protocol Buffers) instead of natural language descriptions
Natural language contracts are ambiguous by design. "Handles authentication" means different things to different agents depending on their training. Formal input/output schemas eliminate interpretation — the agent either has the required input or it doesn't. Schema validation at handoff boundaries is the only way to catch contract violations before they propagate
Max-depth counter set to 10 → legitimate 11-step multi-agent pipeline gets killed at step 10 with "max delegation depth exceeded." Pipeline produces partial output. Half-configured infrastructure is left running, costing $200/hr in cloud resources with no work being done
Depth limit was arbitrary — "10 seems like plenty." The actual pipeline is a linear chain: analyze → design → implement → test → review → refactor → document → package → deploy → verify → monitor. Each step is a legitimate delegation. No distinction between linear chains (legitimate depth) and cycles (illegitimate depth)
Distinguish depth from cycles: linear chain depth is fine, cycles are not. Use cycle detection (agent ID already in chain) as the hard limit, not an arbitrary max depth. Set a soft depth warning at 10+: log a warning but continue. Only kill on cycle detection or explicit timeout. Implement rollback: if pipeline is killed, the orchestrator cleans up partial state — never leave half-done infrastructure running
Max-depth counters are a blunt instrument for a specific problem (cycles). They catch one deep-architecture pattern (11-step linear pipeline) but let through a 3-step cycle. Cycle detection replaces arbitrary depth limits with a precise problem detector. Cleanup on kill is mandatory — a killed pipeline must not leave resources running
State Log
This section documents every irreversible decision made during the session. It is non-negotiable and prevents the agent from revisiting settled questions.
#
Decision
Rationale
Alternatives Considered
Timestamp
1
[no decisions logged yet]
—
—
—
Rules:
Append a new row for each irreversible or hard-to-reverse decision
Never modify past rows — only append
If revisiting a decision, add a NEW row (do not edit the old one)
