「auditing」の検索結果

Go-to-market engineering for the Yupcha portfolio (OmniVoice-Studio, the Clay-alternative in this repo, ResuBird). A closed set of executable verbs — research, audit, draft, measure, prepare, publish, escalate — each with a typed contract, required sources, and a risk tier. Use when: (1) researching ICP/competitors/channels/prior art, (2) auditing our own properties for what's actually broken (dead pricing pages, 404ing clone URLs, unfilled demos, licence detection, watermark coverage), (3) drafting docs/comparison pages/release notes/launch copy, (4) measuring real numbers against GOAL.md, (5) preparing a Show HN or a listing up to the send button, (6) any question about whether a GTM action is allowed. Triggers: 'gtm', 'go to market', 'launch', 'show hn', 'growth', 'marketing', 'positioning', 'icp', 'competitors', 'outreach', 'cold email', 'audit our', 'is it ok to', 'can we post', 'promote'.

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Repurpose existing content into a native Threads post or thread. Take a LinkedIn post, X thread, blog, YouTube script, or newsletter and rewrite it for Threads: warm the tone, de-corporate it, refit to a short post or light thread, rebuild the hook to clear the soft fold, strip off-platform artifacts, run the humanizer, publish via Publora on approval. Not for writing from scratch (use threads-post-writer), auditing a draft (use threads-humanizer), or a hook teardown (use threads-hook-extractor).

日本語の概要は準備中です。原文の説明を表示しています。

Draft a single Threads post or a multi-post thread using a 2026 Threads hook formula (warm contrarian, data-point, build-in-public, quote-post, mini-list, relatable, listicle, story, curiosity-gap, how-I teardown), picked by goal (replies, reposts, likes, quotes). Respects the 500-char limit and one-hashtag cap, runs the humanizer pass, and publishes via Publora, which auto-splits long content into a thread. Not for auditing a draft (use threads-humanizer) or hook teardown (use threads-hook-extractor).

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Use when auditing the imp codebase for structural debt, dead code, god-objects/files, duplication, flag sprawl, or deciding whether a cleanup is worth shipping - "structure audit", "tech debt", "is this still used", "dead code", "refactor for clarity", "should we split this file", "remove this flag", "file size gate red", "alloc sites gate red". Do NOT use for build/test mechanics (building-and-testing), perf/kernel work (benchmark-cuda / sm120-cuda-expert), or output quality (check-degeneration).

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Use when writing, moving or auditing any .md in imp - deciding which file a paragraph belongs in, adding a doc, fixing a stale claim, or when docs_lint.py fails in CI. Covers the four reader layers (L0 README / L1 operators / L2 kernel devs / L3 agents), the HTML-comment metadata header, [PROV:] provenance, the single-source-of-truth map, generated perf blocks, which numbers may appear in prose, plan-doc closure. Triggers on "which doc does this go in", "docs lint failed", "add a doc", "this claim is stale", "update the README", "PROV block", "layer", "STALE.md". Do NOT use for the CHANGELOG or a release body (shipping-prs), or for code-comment accuracy (codebase-audit).

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Count-based quantity takeoff and tag completeness auditing for construction drawings. Vision + OCR reconciliation, sheet markup, Excel QTO output. Triggers: 'tag audit', 'quantity takeoff', 'QTO', 'count fixtures'.

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Report ground-truth Claude token consumption and estimated cost by parsing JSONL session files directly. Use when checking API spend, auditing token usage by project/session/model, generating daily/weekly/monthly cost reports, or diagnosing ccusage undercounting. Includes subagent files that ccusage ignores.

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Builds complete, optimized Claude prompts and Project scaffolds from task descriptions. Use when building, designing, creating, or structuring a Claude prompt or system prompt. Trigger on: "build me a prompt," "compile a prompt for," "create a system prompt," "write a Claude prompt," "design a prompt for," "help me write a prompt," "scaffold a Claude Project," "design a Project for." Uses a four-stage methodology (Parse, Select, Construct, Validate) and a tested 5-layer architecture (Identity, Objective, Context, Reasoning, Output + Quality Control). In Project Mode, the Compiler is global-layer-aware — checks User Preferences, installed Skills, and MCP Connectors to avoid redundancy and flag gaps. Do NOT use for auditing existing prompts (use rootnode-prompt-validation if available) or diagnosing existing Projects (use rootnode-project-audit if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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Diagnoses and fixes Claude behavioral issues in system prompts and Projects using tested countermeasure templates. Use when "Claude is too verbose," "keeps hedging," "agrees with everything," "claims it did something it didn't," "won't use a tool," "fix Claude's output," "tune Claude's behavior," "Claude ignores my preferences," "adds unsolicited disclaimers," "uses too many lists." Covers ten tendencies: agreeableness (output-content + persistent-preference), hedging, verbosity, list overuse, fabricated precision, over-exploration, tool miscalibration (over- and under-triggering), LaTeX defaulting, editorial drift, self-referential fabrication. Also use when auditing a system prompt for behavioral calibration or recalibrating a pre-4.7 prompt, and when users describe recurring output problems without naming a tendency. Do NOT use for scoring a prompt's overall quality — use rootnode-prompt-validation if available.

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Specialized business strategy prompt methodology for Claude. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output formats) for consulting, M&A, corporate strategy, and strategic planning tasks. Use when building prompts for deal evaluation, due diligence, portfolio strategy, business model analysis, board narratives, investment cases, market entry plans, or strategic options assessments. Trigger on: "build a prompt for M&A analysis," "due diligence prompt," "board narrative," "investment case," "market entry strategy," "strategic options," "portfolio strategy prompt," "business model analysis prompt," "consulting-style analysis." Do NOT use for evaluating or scoring existing prompts (use rootnode-prompt-validation if available) or for auditing Claude Projects (use rootnode-project-audit if available).

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Builds, validates, packages, tests, and optimizes deployment-ready Skill files (SKILL.md + references/ + scripts/ + agents/) from design specifications. Three pre-build gates + 9-dimension quality gate (with D9a/b/c empirical evidence tiers) + behavioral iteration loop + automated description optimizer + blind version comparator. Use for building, reviewing, revising, testing, or optimizing Skills. Trigger on: "build this Skill," "review this Skill," "test my Skill," "optimize the description," "compare these Skill versions," "should this be a hook instead," "build a template first." Also: "my Skill doesn't activate," "my description truncates," "my SKILL.md is too long," "my Skill is overtriggering." Do NOT use when designing Skill methodology — that's design work, not packaging. Do NOT use for prompt compilation, project auditing, or prompt scoring (use rootnode-prompt-compilation, rootnode-project-audit, or rootnode-prompt-validation if available).

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Comprehensive audit of a user's entire Claude environment — combines Project audit (six-dimension Project Scorecard, seven anti-patterns) with Global audit (six-dimension Global Layer Scorecard) plus Cross-Layer Alignment Check across all nine layers and Evolutionary Recommendations across four pathways. Produces a unified action plan. Use when user says "full audit of everything," "audit my entire Claude setup," "full stack audit," "comprehensive review of my project and preferences," "check everything," or wants the complete health check of both a specific Project AND their global configuration. Also use when a project audit reveals cross-layer issues that require full-stack visibility. Do NOT use for auditing only a Project (use rootnode-project-audit if available) or only global layers (use rootnode-global-audit if available). Do NOT use for single prompt evaluation (use rootnode-prompt-validation if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

日本語の概要は準備中です。原文の説明を表示しています。

Specialized research and analysis prompt methodology for Claude. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output) for data analysis, policy research, systematic evidence review, investigative research, and research-specific deliverables. Use when building prompts for quantitative data interpretation, evidence-based policy recommendations, literature reviews, causal analysis, hypothesis-driven investigation, or briefing documents. Trigger on: "prompt for data analysis," "research prompt," "policy brief prompt," "literature review prompt," "prompt for investigating," "systematic review prompt," "build a prompt for evidence synthesis," "causal analysis prompt," "briefing document prompt." Also use when user describes a research or analytical task and needs a structured Claude prompt for it. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or auditing Claude Projects (use rootnode-project-audit if available).

日本語の概要は準備中です。原文の説明を表示しています。

Audits Claude Code repositories. Two-phase workflow — Phase 1 sweeps 14 hygiene categories (permissions, hooks, CLAUDE.md bloat, Skills hygiene, process-abstraction, plus structural) and an optional 7-layer leak check. Phase 2 executes [APPROVED] markers from the report file. Use for repo cleanup, pre-distribution sanity, post-feature hygiene, environment audit, process-abstraction discovery. Trigger on "audit my CC repo," "sweep my repository for hygiene," "check this CC environment," "find process abstraction candidates," "review my CLAUDE.md," "clean up my .claude directory," "pre-distribution hygiene check," "find layer leaks in my CC setup." Routes structural findings to rootnode-cc-design REMEDIATE. Composes with rootnode-critic-gate. CC-only — does not scan Claude Projects. Do NOT use for building a Skill (use rootnode-skill-builder), compiling a prompt (use rootnode-prompt-compilation), scoring a single prompt (use rootnode-prompt-validation), or auditing a Project (use rootnode-project-audit).

日本語の概要は準備中です。原文の説明を表示しています。

SEO & GEO content linter — validates Markdown/MDX files for AI search visibility using 92 deterministic rules (35 GEO, 32 SEO, 14 content quality, 8 technical, 3 i18n). Runs an autonomous lint-fix loop: scan content, read structured violations, fix them, re-lint until clean. Use when optimizing content for AI citations, auditing SEO compliance, checking GEO readiness, or running pre-publish content validation. Triggers on: "geo-lint", "lint content", "SEO audit", "GEO", "content optimization", "AI search", "citation readiness".

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Direct a claim-driven research experiment project from post-Idea intake through method realization, prioritized evidence collection, Human Gates, and Evidence Freeze. Use for scientific planning, state transitions, queues, budgets, or recovery; not for running code or independently auditing results.

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Use when a B2B SaaS product needs upstream positioning or messaging clarity — defining who it's for, the category, competitive alternatives, differentiation, value props, strategic narrative, or a messaging hierarchy. Triggers include "my messaging isn't working," "we sound like everyone else," repositioning, or auditing an existing positioning doc. Use before homepage, deck, or sales-narrative work. Not for writing the final copy (use copywriter-skill) or running the sales process (use sales-skill).

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Use when planning, auditing, or speccing a video's motion and visual craft: shot lists, storyboards, color scripts, transitions, easing, kinetic typography, pacing, retention, or animation principles. Triggers: 'video plan', 'shot list', 'storyboard', 'color script', 'motion spec', 'fix the pacing', 'explainer/demo/brand video'. For the spoken words themselves, use script-writer-skill; for in-code UI motion, use frontend-design.

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Register trained models in MLflow Model Registry with version control, implement stage transitions (Staging, Production, Archived) with approval workflows, and manage model lineage with comprehensive metadata and deployment tracking. Use when promoting a trained model from experimentation to production, managing multiple model versions across development stages, implementing approval workflows for governance, rolling back to previous versions, or auditing model changes for compliance.

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Probe the runtime state of a named feature flag in a CLI binary. Covers the four-pronged evidence protocol (binary strings, live invocation, on-disk state, platform cache), the four-state classification (LIVE / DARK / INDETERMINATE / UNKNOWN), gate-vs-event disambiguation, conjunction-gate handling, and skill-substitution scenarios where a flag appears DARK but the capability is delivered by other means. Use when verifying whether a documented or inferred capability has rolled out, when auditing dark-launched features, or when a prior probe's conclusions need refreshing against a new binary version.

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Review a data analysis for quality, correctness, and reproducibility. Covers data quality assessment, assumption checking, model validation, data leakage detection, and reproducibility verification. Use when reviewing a colleague's analysis before publication, validating an ML pipeline before production deployment, auditing a report for regulatory or business decision-making, or performing a second-analyst review in a regulated environment.

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Review a pull request end-to-end using GitHub CLI. Covers diff analysis, commit history review, CI/CD check verification, severity-leveled feedback (blocking/suggestion/nit/praise), and gh pr review submission. Use when a pull request is assigned for review, performing a self-review before requesting others' input, conducting a second review after feedback is addressed, or auditing a merged PR for post-merge quality assessment.

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Monitor, cap, and recover from context accumulation in agentic systems. Covers per-cycle cost tracking, context window auditing, budget caps with enforcement policies, emergency pruning when approaching limits, and progressive disclosure integration to minimize token spend on routing. Use when running long-lived agent loops (heartbeats, polling, autonomous workflows), when context windows are growing unpredictably between cycles, when API costs spike beyond expected baselines, when designing new agentic workflows that need cost guardrails from the start, or when post-mortem analysis reveals a cost incident caused by context accumulation.

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Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.

日本語の概要は準備中です。原文の説明を表示しています。