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Found 1,305 Skills
This skill should be used when the user wants to create a new agent skill, scaffold a SKILL.md, validate an existing skill against repo rules, or refactor a skill to match this monorepo's conventions. Common triggers include "build a skill for X", "create a new skill", "scaffold a skill", "add a skill that does Y", "make me a skill", "audit this skill against our rules", and "refactor this skill to match repo conventions". Enforces kebab-case naming, verbatim trigger phrases, selective XML for example boundaries, and a RED→GREEN→REFACTOR cycle. Skip when modifying source code, debugging an existing skill, or writing non-skill markdown.
Expert guide for writing comprehensive API documentation including OpenAPI specs, endpoint references, authentication guides, and code examples. Use when documenting APIs, creating developer portals, or improving API discoverability.
Captures and organizes chaotic brain dumps into a structured, actionable system with zero information loss. Use this skill whenever the user says 'capture this', 'brain dump', 'let me dump some ideas', 'I've got a bunch of thoughts', 'here's everything on my mind', 'idea dump', 'let me get this out of my head', 'I need to organize my thoughts', 'here's what I'm thinking', or any variation where someone is unloading a messy stream of ideas, tasks, thoughts, and plans wanting them turned into something coherent. Also trigger when the user pastes or dictates a long, unstructured block of mixed ideas — even without the exact phrase — the intent is the same. Fast-to-action by design: no upfront intake. Output is four sections (Projects/Ideas, Tasks, Connections, How I Can Help) ending with a directive question. Asks at most one mid-organization clarifying question when a single item is genuinely ambiguous between task and project.
Check whether the installed securecoder is current. Reports the installed version + the latest release on GitHub + days since release + release notes URL + the exact install command to upgrade. Read-only — never modifies anything; upgrade is always an explicit user action.
Analyze binaries using the Domain API for IDA Pro. Use when examining program structure, functions, disassembly, cross-references, or strings.
Extracts exact, behaviour-first specifications from an existing codebase. Defines domain concepts, use cases, and business rules with precision — zero implementation details. Use when reverse-engineering a legacy project into precise specs or preparing an AI-friendly spec set for a rewrite.
Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.
ELI5-style explanations with analogies and multiple examples. Explains concepts at different levels (ELI5, high school, undergraduate, graduate). Uses real-world analogies and visual metaphors. Use when explaining difficult concepts, clarifying confusing topics, or learning new subjects. Triggers - explain concept, ELI5, explain like I'm 5, what is, how does, why does, analogy for, simple explanation.
Examine file system slack space, MFT entries, USN journal, and alternate data streams to recover hidden data and reconstruct file activity on NTFS volumes.
Generate a pull request subject line and a concise description by analyzing the commits and diff on the current local git branch. Use this whenever the user is preparing a PR and wants help writing its title or body — phrases like "write a PR description", "summarize my changes for a PR", "what should the PR title be", "draft the PR for this branch", or "describe these commits". Trigger even if the user doesn't say the exact words "pull request" but is clearly wrapping up branch work and wants it summarized for review. This skill only reads git locally and prints the result for the user to copy — it never pushes or edits anything on GitHub.
Produce a comprehensive, evidence-grounded prioritized action plan from any PM input (notes, transcripts, drafts, executive asks, Slack threads, or a raw situation). Outputs one saveable document with an executive summary, input mirror, situation classification (Cynefin), the binding constraint (Theory of Constraints), prioritized questions and open decisions, a ranked action plan with the critical effort plus follow-ons, risks and pre-mortem, copy/paste prompts for downstream pm-skills, and an evidence map. Builds a source ledger and cites exact input quotes; refuses High-confidence plans for Complex or Chaotic situations. Use when you want the critical next effort and how to execute it.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**