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Use when you need Liberty91 platform intelligence — what actually happened (deduplicated Threat Events with every source, Admiralty reliability/credibility and verification stage), whether an IOC is already known to your account, the canonical threat library (actors, malware, vulnerabilities, clusters, ATT&CK TTPs), alert matches, or which of your customer organizations an occurrence affects — or when you need to push intelligence back: ingesting your own report, generating an intelligence package, or uploading an organization document. Also supplies event provenance fields, when the platform returns them, and Threat Library alias resolution to /source-provenance and /quality-of-information-check. Two-way, first-party integration. Commonly invoked by /ip-investigation and friends, /ioc-enrichment-workflow, /threat-actor-profiling, /campaign-tracking and /vulnerability-intelligence. Reads $LIBERTY91_API_KEY.

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Validate at every layer data passes through to make bugs impossible

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。

Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.

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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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。

Work autonomously without waiting for hand-holding — complete tasks end-to-end without asking for permission at every step.

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。

Deep review — random exploration + cross-agent review, alternating. Finds integration bugs that self-review misses. Run every 30–60 min during active implementation.

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。

Translate a polished markdown plan into self-contained beads in .beads/beads.jsonl. Every bead must be executable without reopening the plan. Use after the plan has converged.

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。

Deep code review after beads have landed — random exploration + cross-agent review, alternating. Use every 30–60 min during active implementation, or after any epic lands.

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。

Founder Operating Context. Use to create or update the canonical founder/startup context document so every fundraising, growth, product, and recruiting workflow runs on concrete company facts instead of generic assumptions.

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。

Discover how a codebase already handles a specific concern — search broadly, find every instance, and assess consistency. The "how does this app do X?" tool.

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 whenever you state a fact. Add a short "Sources:" list at the end of your answer with where each claim came from (file name, URL, or "reasoning"). Shared by every agent on the team.

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 for every answer. After the final answer, append a new line that starts with exactly "RECEIPT:" followed by a three-word note. This proves the skill was loaded by the runtime.

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 agenda-setting theory (McCombs & Shaw) to analyze how media salience transfers to public perception. Use this skill when the user needs to study media influence on public opinion priorities, evaluate issue salience transfer across media and public agendas, or design communication strategies that leverage agenda-setting effects — even if they say 'why is everyone talking about this topic', 'how does media shape public priorities', or 'which issues get attention and why'.".

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 whether a machine-readable regelrecht law model is faithful to the LETTER of the wettekst — and strictly separates the wettekst (leading) from the toelichting (Nota/Memorie van Toelichting; explanatory, not a norm). Runs in critical passes per article/lid and detects four deviation classes: toelichting-bleed (a modeled criterion that the letter only states as an open norm, or not at all), missing verbatim elements (a word/condition such as a "tenzij" or a qualifier dropped from the model), wrong legal_basis anchoring (an endpoint hung on a "kapstok"-article while the operative norm lives elsewhere), and verbatim-drift in text: (an ingetrokken/older redaction). Also detects positief-lid vs uitsluitings-lid conflicts and checks the chapeau for an explicit derogation rule ("zo nodig in afwijking van het eerste lid") before judging precedence. Classifies every finding as modelfout (fix), wettekst-gevolg (report, do not fix), or letter-vs-toelichting-question (jurist decides what is leading + revision signal). Use when the user wants to know if a model is "dicht bij de letter", mentions letter vs toelichting/bedoeling, fidelity, "natuurgetrouw", or before trusting feature outcomes.

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。

Performs a hallucination check on machine_readable sections by verifying every element traces back to the original legal text. Use this skill proactively when: machine_readable sections have been generated or modified, after /law-generate completes, when reviewing corpus YAML files for legal accuracy, or when user mentions "validate", "verify", or "hallucination check" for law YAML files. Activate automatically after editing machine_readable sections in corpus regulation YAML files.

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。

Detects version drift between machine-readable law YAML files and the binding geldende wettekst on wetten.overheid.nl at the YAML's claimed valid_from. Catches the silent failure where authored content lags behind ingevoerde wijzigingen — a YAML dated 2025-01-01 still carrying pre-Stb.-X text. Activate as Step 0 of every regelrecht-stelselanalyse micro-cycle, before any YAML edit. Strict by design: no bypass, no normalization beyond whitespace within a single paragraph; the geldende wettekst is leading and is mirrored verbatim, including errata. Use proactively when starting a stelselanalyse cycle, before harvest/migrate/extend, or when user mentions "drift", "versie", "geldende tekst", or worries that a YAML may be stale relative to wetten.overheid.nl.

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Facilitate and document Sprint Retrospectives per Scrum Guide 2020. Choose format by team maturity, structure the session, capture action items, and track improvement across sprints. Use after every sprint. Do not use for individual performance reviews, blame sessions, or annual assessments.

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Facilitate SAFe 6.0 PI Planning events. Structure the 2-day cadence, generate PI Objectives with Business Value scores, build the Program Board for dependency visualization, and run the Confidence Vote. Use before every Program Increment when the organization follows SAFe.

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Generates specific, implementable fixes for every finding in findings.json. Produces code patches (unified diff), configuration changes, dependency updates, and IaC fixes — not generic advice but actual before/after code. Uses the reproduction command from each finding as the verification step: "run this after the fix — it should now fail." Stores remediation data in findings.json so the dashboard shows a Fix button and /gh-export includes the remediation in GitHub issues. Chains from /pentester, /codebase, or any scan skill after findings are produced. Chains into /gh-export for export with remediation included.

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Deep OAuth 2.0 / OpenID Connect security assessment. Covers the full attack surface: redirect_uri validation bypass (path traversal, open redirect chains, subdomain confusion, URL parsing tricks, parameter pollution, response mode switching), missing/broken state parameter CSRF, PKCE downgrade and absent-challenge attacks, implicit grant token leakage (Referer, browser history, XSS fragment theft), authorization code injection, scope escalation, client confusion attacks, mutable-claims account takeover (iss+sub confusion, email-keyed identity merging), pre-account takeover via unverified registration, OpenID Connect dynamic client registration SSRF (logo_uri, jwks_uri, sector_identifier_uri, request_uri), nonce replay, ID token validation failures, Device Code phishing flow, mobile custom URI scheme hijacking, refresh token persistence, token introspection enumeration, consent screen clickjacking, host header injection, and cross-IdP mix-up attacks. Uses KOAuth, jwt_tool, nuclei oauth templates, and manual http(action="request", ...) payloads. Every technique includes actual request payloads and verification logic. Chains from /pentester or /api-security when OAuth endpoints are discovered. Chains into /web-exploit for open redirect, XSS, and SSRF chaining. Chains into /post-exploit when full account takeover is achieved. Chains into /credential-audit when access tokens or refresh tokens are recovered.

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques

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。