token-budget-advisor
openbooklet.com/s/token-budget-advisoropenbooklet.com/s/[email protected]GET /api/v1/skills/token-budget-advisorEnforces Law 2 (Plan Is Sacred) of the 7 Laws of AI Agent Discipline by making token-budget tradeoffs explicit before the response is composed. Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: "token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", or clear variants where the user is explicitly asking to control answer size or depth. DO NOT TRIGGER when: user has already specified a level in the current session (maintain it), the request is clearly a one-word answer, or "token" refers to auth/session/payment tokens rather than response size.
Law 4 says verify before reporting. A merged commit with green CI is a claim, not a proof. This skill runs the audit-driven loop: take a window of recent commits, hunt for real defects, prove each one before touching code, fix on a branch, and verify every surface the fix touches before declaring th
Install structured self-improvement loops with instinct-based learning into Claude Code â research, plan, execute, verify, reflect, learn, iterate. On-demand or weekly analysis to save tokens. Supports multi-agent parallel analysis.
Enforces Law 2 (Plan Is Sacred) of the 7 Laws of AI Agent Discipline. Detects when a session has drifted away from its stated goal by scoring recent tool activity against the '## Goal' section of task_plan.md, so drift is caught mid-session instead of at end-of-session reflection.
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. After any failure in the verification ladder or auto-loop, classify the failure class before retrying â provider, tool-schema, deterministic-policy, git, worktree, runtime â so retry-vs-pause-vs-self-heal-vs-stop is an intentional decision, not a generic 'try again'.
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. Pre-dispatch invariant: reconcile DB-vs-disk-vs-memory state before any unit runs, so a stale flag, missing artifact, or out-of-sync row never re-dispatches a unit that already completed or never started.
Initialize a multi-agent swarm with anti-drift configuration
Enforces Law 2 (Plan Is Sacred) of the 7 Laws of AI Agent Discipline. Decision-framing lens that pairs WILD generation with RISA execution when emitting recommendation lists. Not a runtime hook.
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. Pre-dispatch invariant: validate worktree root before any source-writing tool call. Catches missing .git, fallback path-only creation, stale leases, foreign-session ownership, and non-worktree git operations before they corrupt history.
Auto-indexed from naimkatiman/continuous-improvement
Are you the author? Claim this skill to take ownership and manage it.
Related Skills
graceful-error-recovery
Use this skill when a tool call, command, or API request fails. Diagnose the root cause systematically before retrying or changing approach. Do not retry the same failing call without first understanding why it failed.
audience-aware-communication
Use this skill when writing any explanation, documentation, or response that will be read by someone else. Match vocabulary, depth, and format to the audience's expertise level before writing.
Refactoring Expert
Expert in systematic code refactoring, code smell detection, and structural optimization. Use PROACTIVELY when encountering duplicated code, long methods, complex conditionals, or any code quality issues. Detects code smells and applies proven refactoring techniques without changing external behavior.
Research Expert
Specialized research expert for parallel information gathering. Use for focused research tasks with clear objectives and structured output requirements.
clarify-ambiguous-requests
Use this skill when the user's request is ambiguous, under-specified, or could be interpreted in multiple ways. If proceeding with a wrong assumption would waste significant work, always ask exactly one focused clarifying question before doing anything.
structured-step-by-step-reasoning
Use this skill for any problem that involves multiple steps, tradeoffs, or non-trivial logic. Think out loud before answering to improve accuracy and transparency. Apply whenever the answer is not immediately obvious.