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Drive a whole tdmcp feature BACKLOG to completion as resumable, wave-by-wave releases — the campaign layer ABOVE tdmcp-pipeline/tdmcp-feature-lead. Use whenever the user wants to implement an ENTIRE backlog or discovery file (e.g. _workspace/discovery*/FEATURE_BACKLOG*.md), 'all the features', many features across multiple releases, or a long autonomous build campaign, AND for every follow-up: continue/resume the campaign, run the next wave, re-run a failed wave, fold in QA results, or check campaign status. Backed by a ledger.json so re-running is idempotent (skips shipped work, resumes interrupted work) and resilient (retry-once then quarantine-and-continue). For a SINGLE feature or one small batch, use tdmcp-pipeline instead; this skill is for the whole ledger.

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。

Automatically optimize AI agent credit usage by routing tasks to the most cost-efficient execution path. Use when you want to reduce AI API costs by 30-75% without quality loss, classify task complexity before execution, route simple tasks to free or low-cost models, split complex tasks into optimized sub-tasks, or detect vague prompts before wasting credits.

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。

Linux local privilege escalation from a low-privilege shell to root. Covers quiet vs loud enumeration, sudo/GTFOBins, CVE-2025-32463 chwoot and CVE-2025-32462 host bypass, polkit/udisks CVE-2025-6018/6019, SUID/capabilities, systemd timers/units, cron/PATH/LD_PRELOAD, dangerous groups and docker.sock, host-local container escape, and version-gated kernel LPE including Copy Fail CVE-2026-31431. Use when the operator has a Linux foothold and needs root. Kubernetes RBAC stays in /k8s. Endpoint blocks hand off to /edr-bypass then return here.

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。

Render `.claude/diamonds/active.yml` as the decisions each diamond has recorded, in order, grouped by mode (discovery, delivery). Read-only. Default format Mermaid stateDiagram-v2, drawn by a script. Recommended at the end of `/mycelium:diamond-assess` so every assessment closes with a visual state-of-play. See `${CLAUDE_PLUGIN_ROOT}/engine/render-conventions.md` for shared render fleet conventions.

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。

Apply MCAF human-review-planning guidance for a large AI-generated code drop by reading the target area, tracing the natural user and system flows, identifying the riskiest boundaries, and prioritizing the files a human should inspect first. Use when the codebase is too large to review line-by-line and you need a practical review sequence plus a prioritized file list.

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。

Use on any core for first-run planning intake — Phase 0 runs the vendored BMAD-METHOD planning shelf, shared by every core, and Phase 1 (mining `04-prd.md` into intent records plus the Add-ons/sync-and-remote-entities decision) is full-stack-app's and pwa-app's shared procedure — identical mechanics, a different decision at step 5/8. Phase 0 also defines compressed intake, the path a user's explicit "just build it" choice takes on full-stack-app, pwa-app, authored, and copywriting cores: one batched round of questions in place of the shelf, writing a thinned archive at the same paths so Phase 1 (or, on copywriting, that core's own mining step), `ux-planner`, and the Re-entry pass all keep their documented source. Also use for the Re-entry pass, which mines new scope into additional intents without re-running the shelf, on any core with a module axis to add an intent to (full-stack-app, pwa-app, authored) — landing-page has none, so its own new-scope path runs through `hedgehog-landing-loop`'s Correction Protocol instead. Invoked by the `planner` agent, which decides the path; don't run standalone. landing-page runs this skill's Phase 0 on first run, then mines the same archive through `hedgehog-landing-loop`'s own planning-intake section, that core's counterpart to this skill's Phase 1. copywriting runs this skill's Phase 0 the same way (full or compressed), then mines the same archive through `hedgehog-copywriting-loop`'s own planning-intake section into a what/audience/register brief — no module axis, so its own new-scope path runs through that loop's Correction Protocol rather than the Re-entry pass below. An authored core runs this skill's Phase 0, then `hedgehog-core-design`, then this skill's Phase 1 mining against the designed layer sequence. A brownfield adoption (`hedgehog-adopt`) never runs this skill's shelf at all — the drivers BMAD elicits are already settled facts of a repo that already exists.

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。

Documents the hook enforcement system, bypass mechanisms, and common failure patterns to help agents avoid getting stuck on protected path writes

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。

Ultra-granular code analysis for deep architectural context building. Line-by-line and block-by-block analysis using First Principles, 5 Whys, 5 Hows methodology at micro scale. Builds mental models, tracks invariants and assumptions, and maps cross-function call flows for security audit preparation.

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。

Hunt authentication and authorization bypasses — forced browsing, path traversal in auth, header injection, JWT downgrade, OAuth state confusion, role manipulation. Use when an endpoint returns 401/403 but appears reachable.

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。

Hunt business logic flaws — race conditions, workflow state bypass, parameter pollution affecting logic, coupon/discount abuse, refund logic, multi-step process bypass, integer overflow, negative quantities, double-spend. Use when looking for non-CVE bugs that require understanding the app's intended workflow.

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。

Hunt file upload vulnerabilities — bypass content-type/extension/magic-byte/size filters, achieve XSS via SVG/HTML upload, achieve RCE via .phtml/.jsp/.htaccess, exploit path traversal in filenames, and use polyglot files. Use when an endpoint accepts file uploads.

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。

Executes a QA specification (specs/qa/QA-<N>-<slug>.md) written by /plan-qa: drives every step with the plan's drivers, verifies each Expected line mechanically ([assert]) or by evidence-backed judgment against the plan's written criterion ([judge]), pauses at named operator handoffs, and writes the results artifact QA-RESULTS-<N>-<slug>.md. Use only when the user asks to run or execute a QA plan — never trigger automatically.

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。

Use when the user wants to search Turkish academic journals on DergiPark (keyword or advanced field search by title/author/abstract/DOI/ORCID/year/etc.), read a DergiPark article PDF as text, or extract an article's references — drives the user's own Chrome (no CAPTCHA solving needed) by injecting JavaScript.

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。

Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the user asks to "score this paper draft", "evaluate against the benchmark", "compare two papers", or "run the autoraters".

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。

Filter and summarise the cross-library KB audit log. Reports confidentiality events (attribution drops, synthesis aborts, dispatcher failures, no-evidence markers, cross-library promotions) over a date range, by event type, and by source handle.

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。

Scrape pages locally with anti-bot bypass, TLS impersonation, and adaptive element tracking — no API keys, no cloud. Handles Cloudflare protection, CSS/XPath element extraction, and survives site redesigns. Complements firecrawl (cloud) with 100% local execution. Triggers on Cloudflare bypass, anti-bot scraping, stealth fetch, local scraping, Scrapling.

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 websites for AI search visibility (GEO/SEO) and generates deployable artifacts (llms.txt, schema JSON-LD, robots.txt). 7-dimension scorecard tailored by site type and size, grounded in Princeton KDD 2024, Toronto 2025, and ICLR 2026 research. This skill should be used when optimizing for ChatGPT, Perplexity, Google AI Overviews, or other AI search engines. Also triggered by queries about AI search optimization, generative engine optimization, llms.txt, AI crawler configuration, schema markup for AI, citation optimization, or Google AI Mode.

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。

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.

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。

Channel Tom di Mino's daimonic voice for writing. Invoke when Tom asks to write prose, essays, tweets, poetry, or technical docs in his own voice—possessed by the daimon, not assisted by it. Triggers on: 'write as me', 'in my voice', 'channel', 'daimon', 'tamarru', 'waltz', or any request for Tom's authentic writing voice.

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。

Navigate make-on-demand catalogues — ZINC-22 through CartBlanche and Enamine REAL Space — to find compounds that can actually be ordered. Use this skill to look substances up by ZINC identifier or structure, understand tranche partitioning by heavy-atom count and logP, and choose between screening an enumerated subset and searching a combinatorial synthon space with a fragment-growing method such as V-SYNTHES. Also trigger on ZINC22, CartBlanche, Enamine REAL, make-on-demand, tangible library, synthon, tranche, giga-scale enumeration, or ultra-large virtual screening.

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, expand, revise, structure, and maintain a full Islamic University of Technology BSc thesis from source documents using the local iutbscthesis LaTeX template. Use when writing or editing thesis metadata, front matter, abstract, chapters, related works, methodology, results, discussion, conclusion, citations, appendices, figures, tables, code listings, or compile guidance for main.tex. Also use when the user expects an AI agent to turn papers, notes, PDFs, LaTeX sources, bib files, data tables, and assets into a substantial thesis manuscript chapter by chapter without requiring repeated babysitting.

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。

The TaskBarHero game updated (new version) and the tbh-meter reader needs re-calibration — symptoms are gold showing 0, stage mode "?", or the live party/XP/stats dropping out (party empty / no live levels / runs degraded) after a patch, or you're prepping a meter release for a new game build. Runs the diagnose→dump→diff→reseed→bump→verify playbook (the fix is usually a reseed, but a build can also change an Obscured encoding with zero offset movement — caught only by the live gate, fixed by a read-strategy change). Use when the game version changed, after a TBH patch, when a player reports gold/stage/party/XP broken following an update, or when asked to re-seed / re-calibrate the reader for a new build.

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。

Phase 2: Generate and discover tests, validate against real library. Only invoke when explicitly requested by the user or by the yoink orchestrator.

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。

Phase 3: Dependency decomposition. Only invoke when explicitly requested by the user or by the yoink orchestrator.

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。