
Defines input/output schema validation for skills. Use when creating or validating skills, composing skill chains, or checking skill contracts. Agents load this skill to understand how to validate skill I/O.
Use when designing for graceful degradation, implementing retry or circuit breaker logic, handling partial failures, evaluating idempotency, setting up dead-letter queues, or when a system lacks resilience under dependency failure or network instability.
Track agent decisions, metrics, token costs, and generate reports. Use when logging task execution, tracking agent performance, or generating observability reports.
Track agent decisions, metrics, token costs, and generate reports. Use when logging task execution, tracking agent performance, or generating observability reports.
Unified ai-brain skill combining memory operations and KB query operations with promotion flow.
Use after completing major features, recovering from incidents, making architectural decisions that turned out wrong, or when repeated mistakes suggest stagnant mental models and missing feedback loops.
Use when designing for graceful degradation, implementing retry or circuit breaker logic, handling partial failures, evaluating idempotency, setting up dead-letter queues, or when a system lacks resilience under dependency failure or network instability.
Generate → execute → fail → analyze → fix → repeat cycle with failure pattern recognition. Use when implementing changes that need verification and automatic recovery.
Use when designing for graceful degradation, implementing retry or circuit breaker logic, handling partial failures, evaluating idempotency, setting up dead-letter queues, or when a system lacks resilience under dependency failure or network instability.
Standardizes how `n8n-builder` enters and runs Stage C (implementation) with worker fan-out, group checkpoints, and gated progression. Use when n8n-builder transitions from CRO `approved`/`degraded_skip` to execution.
Token-efficient query protocol for Knowledge Base. Provides tiered access to project documentation with staleness detection and budget-aware responses.
Token and prompt-cache discipline — lean always-on rules, stable prefixes, skill indirection, minimal Task payloads, bounded reads.
Post-merge critic pass — schema + policy-only feedback; no direct code edits.
Deterministic aggregation of shard results — lexical ordering + schema-checked bundle merges.
CTO/tech-lead helper — split work into disjoint shard briefs with caps (instance_cap, partition_basis, determinism keys).
Use when deciding whether to extract a shared utility, create a base class, build a framework layer, or generalize a pattern -- especially when the pattern has fewer than three concrete instances or the abstraction adds cognitive overhead without clear payoff.
Use when writing architecture decision records, documenting trade-offs, drafting design proposals, writing postmortems, summarizing technical reasoning, or when a decision lacks documented context and clear justification.
Use when designing authentication or authorization flows, handling sensitive data, mapping trust boundaries, reviewing secret management, evaluating dependency supply chain risks, or when a feature introduces new public exposure or privilege escalation paths.
Track agent decisions, metrics, token costs, and generate reports. Use when logging task execution, tracking agent performance, or generating observability reports.
Use when delegating tasks to AI agents, designing prompts for structured output, validating AI-generated code or reasoning, detecting hallucination, or when AI output needs constraint checking and human oversight before integration.
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full, ultra. Defaults: main chat = lite, subagents = ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Agent-based project identity derivation for Knowledge Base. Reads git config directly without shell commands. Worktree-safe — all worktrees of the same repo derive the same identity.
Declarative pipeline execution with stages, conditions, data passing, and rollback. Use when executing multi-stage workflows.
Programmatic rule enforcement with glob patterns, priority resolution, and pre/post validation. Use when checking compliance before or after agent actions.
Automated task classification, agent selection, and state tracking. Use when routing tasks to agents, selecting pipelines, or managing task state.
Use when writing architecture decision records, documenting trade-offs, drafting design proposals, writing postmortems, summarizing technical reasoning, or when a decision lacks documented context and clear justification.
Use when delegating tasks to AI agents, designing prompts for structured output, validating AI-generated code or reasoning, detecting hallucination, or when AI output needs constraint checking and human oversight before integration.
Bootstraps a Jira hierarchy snapshot before any multi-issue create / edit session. Disambiguates the parent input (issue key, spec URL, Confluence page URL, or `greenfield`), walks parents up (depth-cap 4), walks children down via JQL recursion (depth-cap 4, max 50 children per node), and returns a structured tree alongside the issue-type scheme and link-type metadata the caller needs to render a draft. Used primarily by `atlassian-pm`; secondary consumers (after the G2.5 retrofit) are the upstream Atlassian plugin skills `spec-to-backlog` and `capture-tasks-from-meeting-notes`. The skill **does not render**; rendering belongs to the caller (typically `atlassian-pm` rendering a markdown tree to chat).
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full, ultra. Defaults: main chat = lite, subagents = ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Structured feedback propagation between pipeline stages. Use when review stages produce findings that should trigger re-implementation.
Dev–Reviewer–QA closed-loop orchestration for implementation tasks — sequence, escalation, retries, merged-diff fan-in, loop state tracking. Invoke when coordinating tech-lead or code-reviewer workflows that chain dev-, reviewer-, and qa-* agents or senior-dev fallback.
Use when estimating infrastructure costs, comparing architectural approaches by cost, evaluating managed service trade-offs, reviewing cloud pricing models, or when a design decision has unclear cost implications or vendor lock-in risk.
Agent-based project identity derivation for Knowledge Base. Reads git config directly without shell commands. Worktree-safe — all worktrees of the same repo derive the same identity.
Use after completing major features, recovering from incidents, making architectural decisions that turned out wrong, or when repeated mistakes suggest stagnant mental models and missing feedback loops.
Standardizes how `n8n-builder` enters and runs Stage C (implementation) with worker fan-out, group checkpoints, and gated progression. Use when n8n-builder transitions from CRO `approved`/`degraded_skip` to execution.
Standardizes how `n8n-builder` performs planning triage, specialist consultation, and produces plan v0 plus post–v0 edit round; mandatory `cro-loop` runs at execution intent before Stage C. Use when n8n-builder is in Stage A (planning).
Use when defining metrics before implementation, designing structured logging, setting SLO targets, instrumenting tracing boundaries, building health checks, or when an outage reveals missing signals and blind spots in operational visibility.
Use when debugging latency issues, evaluating concurrency models, reviewing connection pooling, investigating memory growth, analyzing queue backpressure, or when services degrade under load and the root cause is unclear.
Declarative pipeline execution with stages, conditions, data passing, and rollback. Use when executing multi-stage workflows.
Validates generated output before writing to disk. Use when implementing changes that need pre-write verification to catch errors early.
Standardizes how `remotion-builder` performs planning triage, specialist consultation, and produces plan v0; mandatory tech `cro-loop` at execution intent before Stage C. Use when remotion-builder is in Stage A (planning).
Programmatic rule enforcement with glob patterns, priority resolution, and pre/post validation. Use when checking compliance before or after agent actions.
Use when designing authentication or authorization flows, handling sensitive data, mapping trust boundaries, reviewing secret management, evaluating dependency supply chain risks, or when a feature introduces new public exposure or privilege escalation paths.
Defines input/output schema validation for skills. Use when creating or validating skills, composing skill chains, or checking skill contracts. Agents load this skill to understand how to validate skill I/O.
Post-merge critic pass — schema + policy-only feedback; no direct code edits.
Deterministic aggregation of shard results — lexical ordering + schema-checked bundle merges.
CTO/tech-lead helper — split work into disjoint shard briefs with caps (instance_cap, partition_basis, determinism keys).
Discovery + naming convention reference for typed dev/SME/QA/devops team members in any workspace folder. Primary consumer: `tech-lead` (org-tier).
Three-level parallelism (phase group → phase-task → intra-role fan-out), parallelize-by-default stance, disjoint-touches pre-flight, merge/rollback for horizontal fan-out, failure semantics, and dynamic concurrency caps for orchestrators consuming a cto plan.
Use when debugging latency issues, evaluating concurrency models, reviewing connection pooling, investigating memory growth, analyzing queue backpressure, or when services degrade under load and the root cause is unclear.
Use when designing scalable systems, evaluating consistency models, planning state management, making architectural decisions, or when trade-offs around coupling, failure isolation, and reversibility need explicit reasoning before implementation.
Standardizes how `remotion-builder` runs Stage C (implementation) after tech CRO approval — remotion CLI, browser ensure, Skia smoke, ffmpeg from approved recipes, group checkpoints.
Track agent decisions, metrics, token costs, and generate reports. Use when logging task execution, tracking agent performance, or generating observability reports.
Git pull, commit (~/.gitmessage), push for content-repo automation. SSH pre-provisioned; fail-closed on conflicts. Emits stages for agent-observability.
Token and prompt-cache discipline — lean always-on rules, stable prefixes, skill indirection, minimal Task payloads, bounded reads.
Structured handoff from content org to tech remotion-builder for programmatic video; Task payload, workspace_root, Remotion paths, composition id, output basename, aspect ratio, audio policy, handoff_schema_version 2 synthetic audio fields, ffmpeg recipe id.
Automated task classification, agent selection, and state tracking. Use when routing tasks to agents, selecting pipelines, or managing task state.
Standardizes how `remotion-builder` performs planning triage, specialist consultation, and produces plan v0; mandatory tech `cro-loop` at execution intent before Stage C. Use when remotion-builder is in Stage A (planning).
Use when delegating tasks to AI agents, designing prompts for structured output, validating AI-generated code or reasoning, detecting hallucination, or when AI output needs constraint checking and human oversight before integration.
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full, ultra. Defaults: main chat = lite, subagents = ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Structured handoff to org singleton chief-visual-officer for corpus image generation; Task payload, workspace_root, paths.
Git pull, commit (~/.gitmessage), push for content-repo automation. SSH pre-provisioned; fail-closed on conflicts. Emits stages for agent-observability.
Token / cache discipline for Gemini content pack — pointer to canonical tech skill + Gemini dispatch semantics.
Structured feedback propagation between pipeline stages. Use when review stages produce findings that should trigger re-implementation.
Agent-based project identity derivation for Knowledge Base. Reads git config directly without shell commands. Worktree-safe — all worktrees of the same repo derive the same identity.
Declarative pipeline execution with stages, conditions, data passing, and rollback. Use when executing multi-stage workflows.
Validates generated output before writing to disk. Use when implementing changes that need pre-write verification to catch errors early.
Defines input/output schema validation for skills. Use when creating or validating skills, composing skill chains, or checking skill contracts. Agents load this skill to understand how to validate skill I/O.
Automated task classification, agent selection, and state tracking. Use when routing tasks to agents, selecting pipelines, or managing task state.
Discovery + naming for project agents under each workspace root. Primary consumer: `content-lead` (content org). Content lane: no org `qa-*` assumption — patterns may still list dev-*, sme-*, editor-* per project.
Declarative stages for content automation — optional git_sync, plan, generate_content, git_commit_push; n8n correction branch with target_paths[].
Unified ai-brain skill combining memory operations and KB query operations with promotion flow.
Automated task classification, agent selection, and state tracking. Use when routing tasks to agents, selecting pipelines, or managing task state.
Standardizes how `n8n-builder` performs planning triage, specialist consultation, and produces plan v0 plus post–v0 edit round; mandatory `cro-loop` runs at execution intent before Stage C. Use when n8n-builder is in Stage A (planning).
Token / prompt-cache discipline for content org — points to canonical tech-pack skill + JSON subagent envelope reminder.
Unified ai-brain skill combining memory operations and KB query operations with promotion flow.
Track agent decisions, metrics, token costs, and generate reports. Use when logging task execution, tracking agent performance, or generating observability reports.
Declarative stages for content automation — optional git_sync, plan, generate_content, git_commit_push; n8n correction branch with target_paths[].
Content org: subagent → parent is one JSON object (no YAML). Validate against contracts/schemas/subagent-response.schema.json. Applies to every Task child.
Programmatic rule enforcement with glob patterns, priority resolution, and pre/post validation. Use when checking compliance before or after agent actions.
Two-pass CCO plan-critic loop owned by editorial-cro; ledger schema, editorial bounce rubric, caps, observability stages. Content org only — no Atlassian.
Mandatory planner intake — brain-memory-kb + kb-query, repo/workdir corpus scan, merge into brief for CCO plans.
Discovery + naming for project agents under each workspace root. Primary consumer: `content-lead` (content org). Content lane: no org `qa-*` assumption — patterns may still list dev-*, sme-*, editor-* per project.
Two-pass editorial plan-critic loop (`editorial-cro`); ledger schema, adversarial rubric, bounce rubric, caps, observability. Content org only — no Atlassian.
Structured handoff to Cursor remotion-builder for programmatic video; payload contract with Remotion paths, composition id, output basename, aspect ratio, audio; Gemini prepares briefs — execution in Cursor when remotion-builder is available.
Standardizes how `remotion-builder` runs Stage C (implementation) after tech CRO approval — remotion CLI, browser ensure, Skia smoke, ffmpeg from approved recipes, group checkpoints.
Track agent decisions, metrics, token costs, and generate reports. Use when logging task execution, tracking agent performance, or generating observability reports.
Three-level parallelism (phase group → phase-task → intra-role fan-out), parallelize-by-default stance, disjoint-touches pre-flight, merge/rollback for horizontal fan-out, failure semantics, and dynamic concurrency caps for orchestrators consuming a cto plan.
Use when designing for graceful degradation, implementing retry or circuit breaker logic, handling partial failures, evaluating idempotency, setting up dead-letter queues, or when a system lacks resilience under dependency failure or network instability.
Generate → execute → fail → analyze → fix → repeat cycle with failure pattern recognition. Use when implementing changes that need verification and automatic recovery.
Use when designing for graceful degradation, implementing retry or circuit breaker logic, handling partial failures, evaluating idempotency, setting up dead-letter queues, or when a system lacks resilience under dependency failure or network instability.
Generate → execute → fail → analyze → fix → repeat cycle with failure pattern recognition. Use when implementing changes that need verification and automatic recovery.
Automated task classification, agent selection, and state tracking. Use when routing tasks to agents, selecting pipelines, or managing task state.
Token and prompt-cache discipline — lean always-on rules, stable prefixes, skill indirection, minimal Task payloads, bounded reads.
Use when estimating infrastructure costs, comparing architectural approaches by cost, evaluating managed service trade-offs, reviewing cloud pricing models, or when a design decision has unclear cost implications or vendor lock-in risk.
File-based Markdown memory at ~/.cursor/memory/. Any agent can read/write directly using this skill. No external dependencies.
Core generation protocol for Knowledge Base documents. Analyzes project structure, generates Obsidian-compatible markdown with mermaid diagrams, builds relationship graphs, and tracks manifests for incremental updates.
Structured feedback propagation between pipeline stages. Use when review stages produce findings that should trigger re-implementation.
Validates generated output before writing to disk. Use when implementing changes that need pre-write verification to catch errors early.
Two-pass plan-critic loop (`cro`); ledger schema, adversarial rubric, bounce rubric, caps, observability stages.
Heuristic context budget checks using file-size and count proxies — integrates with context-cache-discipline; no tokenizer introspection claims.
Protocol for subagent → parent responses: single fenced YAML envelope, strict caveman-ultra for compressed fields, verbatim for paths/errors/code. Applies to every `Task` invocation. Use when spawning subagents or parsing their output.
Agent-based project identity derivation for Knowledge Base. Reads git config directly without shell commands. Worktree-safe — all worktrees of the same repo derive the same identity.
Structured feedback propagation between pipeline stages. Use when review stages produce findings that should trigger re-implementation.
Use when delegating tasks to AI agents, designing prompts for structured output, validating AI-generated code or reasoning, detecting hallucination, or when AI output needs constraint checking and human oversight before integration.
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full, ultra. Defaults: main chat = lite, subagents = ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Validates generated output before writing to disk. Use when implementing changes that need pre-write verification to catch errors early.
Programmatic rule enforcement with glob patterns, priority resolution, and pre/post validation. Use when checking compliance before or after agent actions.
Use when delegating tasks to AI agents, designing prompts for structured output, validating AI-generated code or reasoning, detecting hallucination, or when AI output needs constraint checking and human oversight before integration.
Unified ai-brain skill combining memory operations and KB query operations with promotion flow.
Use when writing architecture decision records, documenting trade-offs, drafting design proposals, writing postmortems, summarizing technical reasoning, or when a decision lacks documented context and clear justification.
Structured feedback propagation between pipeline stages. Use when review stages produce findings that should trigger re-implementation.
Use when estimating infrastructure costs, comparing architectural approaches by cost, evaluating managed service trade-offs, reviewing cloud pricing models, or when a design decision has unclear cost implications or vendor lock-in risk.
Use after completing major features, recovering from incidents, making architectural decisions that turned out wrong, or when repeated mistakes suggest stagnant mental models and missing feedback loops.
Use when defining metrics before implementation, designing structured logging, setting SLO targets, instrumenting tracing boundaries, building health checks, or when an outage reveals missing signals and blind spots in operational visibility.
Use when debugging latency issues, evaluating concurrency models, reviewing connection pooling, investigating memory growth, analyzing queue backpressure, or when services degrade under load and the root cause is unclear.
Validates generated output before writing to disk. Use when implementing changes that need pre-write verification to catch errors early.
Programmatic rule enforcement with glob patterns, priority resolution, and pre/post validation. Use when checking compliance before or after agent actions.
Use when designing authentication or authorization flows, handling sensitive data, mapping trust boundaries, reviewing secret management, evaluating dependency supply chain risks, or when a feature introduces new public exposure or privilege escalation paths.
Protocol for subagent → parent responses: single fenced YAML envelope, strict caveman-ultra for compressed fields, verbatim for paths/errors/code. Applies to every `Task` invocation. Use when spawning subagents or parsing their output.
Use when designing scalable systems, evaluating consistency models, planning state management, making architectural decisions, or when trade-offs around coupling, failure isolation, and reversibility need explicit reasoning before implementation.
Discovery + naming convention reference for typed dev/SME/QA/devops team members in any workspace folder. Primary consumer: `tech-lead` (org-tier).
Executable checklist for one cco pipeline run (parallel groups, METRICS_READ, audit NDJSON, n8n gates).
Derive ai-brain project name and KB path from git remote or folder name — same algorithm as Cursor kb-identity (worktree-safe). Use before resolving content-brain paths.
Format one NDJSON audit line per inter-persona event (hashes, paths — no raw secrets or full draft bodies).
Generate → execute → fail → analyze → fix → repeat cycle with failure pattern recognition. Use when implementing changes that need verification and automatic recovery.
Defines input/output schema validation for skills. Use when creating or validating skills, composing skill chains, or checking skill contracts. Agents load this skill to understand how to validate skill I/O.
Three-level parallelism (phase group → phase-task → intra-role fan-out), parallelize-by-default stance, disjoint-touches pre-flight, merge/rollback for horizontal fan-out, failure semantics, and dynamic concurrency caps for orchestrators consuming a cto plan.
Content org: subagent → parent is one JSON object (no YAML). Validate against contracts/schemas/subagent-response.schema.json. Applies to every Task child.
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full, ultra. Defaults: main chat = lite, subagents = ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Declarative pipeline execution with stages, conditions, data passing, and rollback. Use when executing multi-stage workflows.
Structured handoff to org singleton chief-visual-officer for corpus image generation; Task payload, workspace_root, paths.
Three-level parallelism (phase group → phase-task → intra-role fan-out), parallelize-by-default stance, disjoint-touches pre-flight, merge/rollback for horizontal fan-out, failure semantics, and dynamic concurrency caps for orchestrators consuming a cto plan.
Declarative pipeline execution with stages, conditions, data passing, and rollback. Use when executing multi-stage workflows.
Defines input/output schema validation for skills. Use when creating or validating skills, composing skill chains, or checking skill contracts. Agents load this skill to understand how to validate skill I/O.
Mandatory planner intake — brain-memory-kb + kb-query, repo/workdir corpus scan, merge into brief for CCO plans.
Use when deciding whether to extract a shared utility, create a base class, build a framework layer, or generalize a pattern -- especially when the pattern has fewer than three concrete instances or the abstraction adds cognitive overhead without clear payoff.
Bootstraps a Jira hierarchy snapshot before any multi-issue create / edit session. Disambiguates the parent input (issue key, spec URL, Confluence page URL, or `greenfield`), walks parents up (depth-cap 4), walks children down via JQL recursion (depth-cap 4, max 50 children per node), and returns a structured tree alongside the issue-type scheme and link-type metadata the caller needs to render a draft. Used primarily by `atlassian-pm`; secondary consumers (after the G2.5 retrofit) are the upstream Atlassian plugin skills `spec-to-backlog` and `capture-tasks-from-meeting-notes`. The skill **does not render**; rendering belongs to the caller (typically `atlassian-pm` rendering a markdown tree to chat).
Dev–Reviewer–QA closed-loop orchestration for implementation tasks — sequence, escalation, retries, merged-diff fan-in, loop state tracking. Invoke when coordinating tech-lead or code-reviewer workflows that chain dev-, reviewer-, and qa-* agents or senior-dev fallback.
Two-pass plan-critic loop (`cro`); ledger schema, adversarial rubric, bounce rubric, caps, observability stages.
Agent-based project identity derivation for Knowledge Base. Reads git config directly without shell commands. Worktree-safe — all worktrees of the same repo derive the same identity.
CTO/tech-lead helper — split work into disjoint shard briefs with caps (instance_cap, partition_basis, determinism keys).
Post-merge critic pass — schema + policy-only feedback; no direct code edits.
Deterministic aggregation of shard results — lexical ordering + schema-checked bundle merges.
Use when deciding whether to extract a shared utility, create a base class, build a framework layer, or generalize a pattern -- especially when the pattern has fewer than three concrete instances or the abstraction adds cognitive overhead without clear payoff.
# Entrypoint clarification Use when an **entrypoint** session needs requirements before delegating or mutating product repos. ## Triggers - Vague scope, missing success criteria, or unclear in/out boundaries - Ambiguous workspace root(s) (multi-root) - Conflicting mode signals (plan vs execute, builder mode, broker vs interactive) - Pre-delegate / pre-implementation phase (not mid-approved-plan execution) ## Router vs entrypoint | Actor | Policy | |-------|--------| | Generic parent/router
Standardizes how `remotion-builder` runs Stage C (implementation) after tech CRO approval — remotion CLI, browser ensure, Skia smoke, ffmpeg from approved recipes, group checkpoints.
Programmatic rule enforcement with glob patterns, priority resolution, and pre/post validation. Use when checking compliance before or after agent actions.
Use when deciding whether to extract a shared utility, create a base class, build a framework layer, or generalize a pattern -- especially when the pattern has fewer than three concrete instances or the abstraction adds cognitive overhead without clear payoff.
Heuristic context budget checks using file-size and count proxies — integrates with context-cache-discipline; no tokenizer introspection claims.
Two-pass plan-critic loop (`cro`); ledger schema, adversarial rubric, bounce rubric, caps, observability stages.
Standardizes how `n8n-builder` performs planning triage, specialist consultation, and produces plan v0 plus post–v0 edit round; mandatory `cro-loop` runs at execution intent before Stage C. Use when n8n-builder is in Stage A (planning).
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full, ultra. Defaults: main chat = lite, subagents = ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Use when delegating tasks to AI agents, designing prompts for structured output, validating AI-generated code or reasoning, detecting hallucination, or when AI output needs constraint checking and human oversight before integration.
Use when writing architecture decision records, documenting trade-offs, drafting design proposals, writing postmortems, summarizing technical reasoning, or when a decision lacks documented context and clear justification.
Structured feedback propagation between pipeline stages. Use when review stages produce findings that should trigger re-implementation.
Dev–Reviewer–QA closed-loop orchestration for implementation tasks — sequence, escalation, retries, merged-diff fan-in, loop state tracking. Invoke when coordinating tech-lead or code-reviewer workflows that chain dev-, reviewer-, and qa-* agents or senior-dev fallback.
Agent-based project identity derivation for Knowledge Base. Reads git config directly without shell commands. Worktree-safe — all worktrees of the same repo derive the same identity.
Use when estimating infrastructure costs, comparing architectural approaches by cost, evaluating managed service trade-offs, reviewing cloud pricing models, or when a design decision has unclear cost implications or vendor lock-in risk.
Use after completing major features, recovering from incidents, making architectural decisions that turned out wrong, or when repeated mistakes suggest stagnant mental models and missing feedback loops.
Use when defining metrics before implementation, designing structured logging, setting SLO targets, instrumenting tracing boundaries, building health checks, or when an outage reveals missing signals and blind spots in operational visibility.
Standardizes how `n8n-builder` enters and runs Stage C (implementation) with worker fan-out, group checkpoints, and gated progression. Use when n8n-builder transitions from CRO `approved`/`degraded_skip` to execution.
Use when debugging latency issues, evaluating concurrency models, reviewing connection pooling, investigating memory growth, analyzing queue backpressure, or when services degrade under load and the root cause is unclear.
Declarative pipeline execution with stages, conditions, data passing, and rollback. Use when executing multi-stage workflows.
Standardizes how `remotion-builder` performs planning triage, specialist consultation, and produces plan v0; mandatory tech `cro-loop` at execution intent before Stage C. Use when remotion-builder is in Stage A (planning).
Use when designing authentication or authorization flows, handling sensitive data, mapping trust boundaries, reviewing secret management, evaluating dependency supply chain risks, or when a feature introduces new public exposure or privilege escalation paths.
Defines input/output schema validation for skills. Use when creating or validating skills, composing skill chains, or checking skill contracts. Agents load this skill to understand how to validate skill I/O.
Use when designing scalable systems, evaluating consistency models, planning state management, making architectural decisions, or when trade-offs around coupling, failure isolation, and reversibility need explicit reasoning before implementation.
Automated task classification, agent selection, and state tracking. Use when routing tasks to agents, selecting pipelines, or managing task state.
Discovery + naming convention reference for typed dev/SME/QA/devops team members in any workspace folder. Primary consumer: `tech-lead` (org-tier).
Three-level parallelism (phase group → phase-task → intra-role fan-out), parallelize-by-default stance, disjoint-touches pre-flight, merge/rollback for horizontal fan-out, failure semantics, and dynamic concurrency caps for orchestrators consuming a cto plan.
Bootstraps a Jira hierarchy snapshot before any multi-issue create / edit session. Disambiguates the parent input (issue key, spec URL, Confluence page URL, or `greenfield`), walks parents up (depth-cap 4), walks children down via JQL recursion (depth-cap 4, max 50 children per node), and returns a structured tree alongside the issue-type scheme and link-type metadata the caller needs to render a draft. Used primarily by `atlassian-pm`; secondary consumers (after the G2.5 retrofit) are the upstream Atlassian plugin skills `spec-to-backlog` and `capture-tasks-from-meeting-notes`. The skill **does not render**; rendering belongs to the caller (typically `atlassian-pm` rendering a markdown tree to chat).
Generate → execute → fail → analyze → fix → repeat cycle with failure pattern recognition. Use when implementing changes that need verification and automatic recovery.
Validates generated output before writing to disk. Use when implementing changes that need pre-write verification to catch errors early.
# Entrypoint clarification Use when an **entrypoint** session needs requirements before delegating or mutating product repos. ## Triggers - Vague scope, missing success criteria, or unclear in/out boundaries - Ambiguous workspace root(s) (multi-root) - Conflicting mode signals (plan vs execute, builder mode, broker vs interactive) - Pre-delegate / pre-implementation phase (not mid-approved-plan execution) ## Router vs entrypoint | Actor | Policy | |-------|--------| | Generic parent/router
Unified ai-brain skill combining memory operations and KB query operations with promotion flow.
Protocol for subagent → parent responses: single fenced YAML envelope, strict caveman-ultra for compressed fields, verbatim for paths/errors/code. Applies to every `Task` invocation. Use when spawning subagents or parsing their output.
Use when designing for graceful degradation, implementing retry or circuit breaker logic, handling partial failures, evaluating idempotency, setting up dead-letter queues, or when a system lacks resilience under dependency failure or network instability.
Use when defining metrics before implementation, designing structured logging, setting SLO targets, instrumenting tracing boundaries, building health checks, or when an outage reveals missing signals and blind spots in operational visibility.
Checklist for tech-lead and project agents on discovering and using typed dev/SME/QA team members with low-token, minimal-context behavior.
Use when designing scalable systems, evaluating consistency models, planning state management, making architectural decisions, or when trade-offs around coupling, failure isolation, and reversibility need explicit reasoning before implementation.
Generate → execute → fail → analyze → fix → repeat cycle with failure pattern recognition. Use when implementing changes that need verification and automatic recovery.