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Token and prompt-cache discipline — lean always-on rules, stable prefixes, skill indirection, minimal Task payloads, bounded reads.
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Based on SOC occupation classification
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.
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.
Generate → execute → fail → analyze → fix → repeat cycle with failure pattern recognition. Use when implementing changes that need verification and automatic recovery.
Heuristic context budget checks using file-size and count proxies — integrates with context-cache-discipline; no tokenizer introspection claims.
| name | context-cache-discipline |
| description | Token and prompt-cache discipline — lean always-on rules, stable prefixes, skill indirection, minimal Task payloads, bounded reads. |
| version | 1 |
Use this skill when orchestrating or editing the pack so context stays small and early prompt prefixes stay stable (better cache hit odds on providers; always fewer input tokens).
| Goal | Practice |
|---|---|
| Stable prefix | Prefer one canonical copy of long text in skills; rules point — avoid churn in alwaysApply: true bodies. |
| Lean always-on | Rarely needed norms → alwaysApply: false + globs, or load skill on demand. |
| Indirection | Agents/rules cite skill path; do not paste full playbooks into rules or agent bodies. |
| Task payloads | Parent Task prompts: goal, constraints, approved_plan_path, phase/shard id, touches[], correlation ids — not full rules or transcripts. |
| Reads | Batch parallel Read where independent; one read per file per turn unless content changed; lookup-first for brain-memory-kb. |
| Subagent traffic | Children: caveman-ultra + single YAML envelope per templates/subagent-response.yml.tmpl; parents: do not paste raw child YAML to user. |
Non-claim: Exact cache eligibility is vendor-defined; token reduction is always a win.
Load ~/ai-brain/org/global/config/memory-demotion.yml (via brain-memory-kb) for query ladder L0–L3 caps and demotion thresholds — do not duplicate full policy text here.
task-orchestrationbrain-memory-kbvp-research.md (single broker); never duplicate broker bullets elsewheresubagent-response-protocol + templates/subagent-response.yml.tmpltoken_stats: agent-observabilityalwaysApply rule bodies into chat to “teach” a subagentTask (anti-dup hook is fail-closed)