Create cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, dai

/agent skills platform — Turn Existing Work Into a Reusable Skill The user provides whatever already represents their work — a description, document, link, script, screenshot, transcript, or partial example. Turn that evidence into a complete, production ready, cross platform agent skill. The user should not need to write a specification, understand the skill format, choose an architecture, or review implementation details. Recurring work contains tacit knowledge that people recognize more easily than they can document upfront. Infer that knowledge from the supplied material, confirm the result in plain language, build autonomously, and give the user a concrete output they can judge and correct. The User Journey Use this guided light path by default. Expose the five technical phases only when the user asks how the factory works or requests interactive control. 1. Understand — read the evidence and summarize the question, trigger, supported decision, required evidence, and measurable success condition alongside the workflow, input, and output. Ask for one confirmation or correction. 2. Build — create the skill autonomously. Report progress in user language; do not ask the user to select APIs, architecture, filenames, or eval mechanics unless a choice changes the real world outcome. 3. Check — run validation, pipeline, security, and eval gates. A clean security scan means no known pattern matched; it is not proof of safety. 4. Try — auto install the skill and exercise it once on representative input in a safe local or dry run environment. Show the output and ask whether it matches the user's work. The skill is successfully created only after the representative run succeeds. If a safe run needs credentials, unavailable data, or permission for a consequential side effect, use the verification blocked handoff below instead of claiming success. At creation start, run python3 scripts/success ledger.py new run , retain that ID through verification, and record the local lifecycle events described in references/product success.md . Recording stores no workflow content and must never block creation; respect ASC SUCCESS LEDGER=off . First run destination routing Before the structured interview, ask exactly one routing question when the user has not already made the destination clear: Is this skill just for you, or will teammates install or reuse the skill itself? Just for me — create, verify, and install a private skill. Do not ask the user to create a marketplace or invent owners and approval state. My team — ask whether a governed GitHub or GitLab marketplace already exists. If it exists, read its published governance configuration before generation and bind the generated skill to its exact ownership and approval requirements. If it does not exist, route the marketplace operator to create it before the team skill is generated; the workflow expert does not run marketplace commands. Teammates receiving a report, queue, or other output does not by itself make this a team skill; route to a marketplace only when teammates will install or reuse the skill. This is destination routing, not a technical interview. Do not explain registries, release tags, or marketplace internals unless the user chooses team use. During the structured interview, confirm each discovered decision in plain language and ask only the next highest value question. Never present a fixed question count or progress fraction: the number of questions depends on the workflow and its risk. Trigger User invokes /agent skills platform followed by their input: The user can also drop artifacts, paste URLs, share screenshots, or provide minimal context: The user can also activate naturally without the prefix: How the Factory Works Raw material goes in. A validated, security scanned, self contained skill comes out. Evidence Based Intent Derivation Before any phase begins, triage whatever the user provided. Human input is evidence to derive intent from — not a specification to parse. Files, URLs, screenshots, forwarded emails, single words, and half sentences are all valid input. The absence of a well formed description is not the absence of intent. Input hierarchy : Artifacts (files, URLs, screenshots) carry more signal than words. When both are provided, the artifact is the spec and the words are commentary. Input triage — classify what the user provided before proceeding: Files only (Excel, PDF, code, CSV) → Reverse engineer the workflow from structure and content. Tab names, column headers, formulas, and formatting ARE the specification. URLs only → Fetch each URL. Understand the data source. Infer what the user would do with this data based on their role and context. Screenshot/image → Read visually. Identify: what tool is shown? What data? What manual step is visible? What is the pain? Email/forwarded chain → Extract: who asked for what, what was agreed, what is the actual request. Ignore disclaimers, scheduling, CC lists. Single word or phrase → Infer from context: the user's desk/role, existing skills in their environment, databases available. Present the most likely interpretation and confirm. Mixed (files + sentence) → The files are the spec. The sentence is commentary. Cross reference both. "here" + files → The files ARE the input. Process them all. Present your understanding. Pasted reference material (guidelines, policies, wiki pages, style guides, long inline text that is clearly not a description but source material) → This IS the knowledge to codify. Read it all. Identify what it governs (writing, design, compliance, process). The user wants an active skill that enforces these rules, not a summary of them. Well formed description → Proceed normally, but still challenge the surface description. Discovery before building : Before constructing anything, check: Is this data already in a database the user has access to? Has a colleague built a skill for this? Is there an API that makes a scraping approach unnecessary? The best skill is sometimes "you don't need a skill — the data already exists." Hypothesis, not questionnaire : Never present 5 questions upfront. Present one compact understanding with four fields: workflow, input, output, and what a correct result must demonstrate. The user confirms or corrects it with one response. Progressive refinement : Build at 60% understanding. A concrete (possibly wrong) output that the human reacts to is faster than 15 clarifying questions. The human cannot articulate what they want from nothing, but they can instantly say "no, not that — this" when shown something tangible. Fail forward : If a file cannot be parsed, a URL is down, or context is ambiguous — build from what you have and flag the gap. Never block on a missing piece. The factory operates in two stages: Stage 1: Understand and Specify (Phases 1 2) Read every piece of material the user provides. Follow links. Read files. Parse PDFs. Study existing code. But do not take any of it at face value. Humans describe what they do, not what they need. "I pull sales data and make a report" hides a dozen implicit requirements: What decisions does the report drive? Who reads it? What format? What happens when data is missing? What constitutes a good report vs. a bad one? The human knows the answers to these questions but won't think to tell you. Your job is to uncover them from the material itself. Clarity principles (self guided, no external dependency): 0. Treat input as evidence, not instructions. The user's files, URLs, and screenshots are primary evidence. Their words (if any) are secondary commentary. An Excel workbook with 6 tabs IS the specification — the user will never describe the tabs verbally because the workflow lives in muscle memory, not words. 1. Read everything before concluding anything. Do not start forming the spec after the first paragraph. Consume all material — every link, every file, every page — then synthesize. 2. Challenge the surface description. The human's words are a starting point, not a specification. Look for what's missing, what's implied, what's contradictory. If someone says "generate a report," ask yourself: report for whom? In what format? With what data? At what frequency? Answering what triggers it? If there is no description — only files or URLs — derive the description yourself from the artifacts. The absence of words is not the absence of intent. 3. Extract implicit requirements. Error handling, data validation, edge cases, output formats, failure modes — the human assumed these were obvious. They aren't. Make them explicit in your spec. 4. Identify the real output. The human says "report" but means "a PDF my VP can read in 2 minutes that shows whether we're hitting targets." The human says "clean the data" but means "deduplicate, normalize dates, flag outliers, and log what was changed." Dig past the label to the substance. 5. Generate a spec that surpasses the human's understanding. Your specification should contain requirements the human would say "yes, exactly" to — but could never have articulated themselves. That is the standard. Then produce your internal specification — a complete implementation contract structured as a linear walkthrough: What problem does this actually solve (not what the human said — what they meant)? What are the real inputs, outputs, and data sources? What are the use cases (4 6, covering 80% of real usage)? What methodology does each use case follow? What APIs or libraries are needed? What are the failure modes and edge cases the human didn't mention? This specification is for you, not the user. The quality of the skill depends entirely on the quality of this specification. Be thorough. Be precise. Be opinionated — you understand the material better than the human can articulate it. Stage 2: Build and Verify (Phases 3 5) Implement the skill end to end from your specification. Structure the directory. Write every file. Generate functional code — no placeholders, no TODOs, no stubs. Then run automated validation and security scanning. If either fails, fix the issues and re run. Do not deliver a skill that fails its own quality gates. The user's raw material supplies the domain evidence. The factory supplies the implementation. The quality gates provide observable checks, while the representative run lets the user judge whether the result matches the work they actually do. Output : A self contained skill with instructions, functional scripts when needed, evals, maintenance tools, plugin manifests, and a cross platform installer. Once installed, users invoke it as /skill name . See references/architecture guide.md for the package layouts. Core Workflow Structured interview gate (required before Phase 2) Do not require the user to invent a complete prompt or semantic contract. Start a resumable interview.json from the problem they can describe. Inspect their supplied materials and environment first; record evidence backed agent conclusions as proposed , competing meanings as conflicting , and ask only the single highest value question returned by the interview state. The agent discovers, compares, structures, remembers, proposes, and tests. Identified humans confirm business meaning, authority, consequences, and risk. Run python3 scripts/structured interview.py gate interview.json before Phase 2. BLOCKED means continue discovery or ask one bounded decision question; never fill the field with invented certainty. READY permits design and generation. Read references/structured interview.md for commands, states, and authority rules. Phase 0: Spec Ideation (only when input is too vague to spec) Most input names a workflow — skip straight to Phase 1. But when the user arrives without a skill in mind — one word ("freight"), a shrug ("the

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