check_capability
ChatGPTDisclose what an agent Skill or worklore story CAN DO before you run it: a capability tier T0-T4 bound to a content sha256, with findings that carry file:line evidence, PLUS a separate list of behavioral red flags (prompt-injection / deception / exfiltration-intent / safety-bypass phrasings) for a human to review. This is blast-radius disclosure and triage, never a 'safe' verdict. Pass the raw text, or a url to fetch and scan (e.g. a worklore story .md). text needs no sign-in; a url makes the server fetch it for you, so it needs a signed-in session.
edit_story
ChatGPTRevise a worklore story the signed-in account PUBLISHED — only its author can edit it. Call get_story first, then show the human a before/after diff of your change and get their EXPLICIT approval before calling this; never edit on your own initiative. Send the COMPLETE revised story as markdown (frontmatter + '# title' + narrative + '## Reproduce this'), not a fragment. Keep the frontmatter date exactly as it is: it records when the work happened, and a revision never moves it. Say what kind of edit this is: 'rephrase' (same claims, better wording), 'addition' (new detail, nothing was wrong), or 'correction' (something in the story was WRONG — a step, a claim, a result). If anything was wrong, it is a correction: never hide a correction as a rephrase. A correction requires a one-line note saying what was wrong, and notifies everyone who reported reproducing the story. source optionally credits what prompted the revision (an https URL). The new text REPLACES the old (worklore keeps no copy of earlier text), while a record of this revision — when, what kind, the note — is appended to the story's public history.
get_story
ChatGPTFetch one worklore story by slug — its full markdown (narrative + the 'Reproduce this' contract your agent would run) WITH its capability disclosure attached (tier + findings), so you see what it can do before applying it. Read the capability first; run check_capability on the live artifacts before executing. After you apply the story to the user's task, call report_reproduction with an honest result to record it.
publish_story
ChatGPTPublish a NEW worklore story as the signed-in account. worklore stories are PUBLIC and honest, so before you call this you MUST show the human the complete draft — the title, the first-person narrative, and the 'Reproduce this' contract — and get their EXPLICIT approval. Never publish on your own initiative and never invent details. Provide the full story as markdown (frontmatter with title/date/tags/type/reproducible, then '# title', the narrative, then '## Reproduce this' with prerequisites/inputs/steps/verify) — call get_story on an existing story first to match the format — OR provide the structured fields title + narrative (+ reproduce, tags, type, stack) and the server assembles it. SANITIZE: no secrets, employer internals, client names, or private URLs. Returns the live URL and capability tier.
report_reproduction
ChatGPTCall this right after you apply a worklore story's steps to the user's own task, to record the reproduction (attributed to the signed-in account). Use an honest result: 'worked', 'partial' (you produced the output but it isn't verified yet), or 'failed' — all are useful. One counted report per person per story; repeats are acknowledged, not doubled. Never report a story you did not apply.
search_stories
ChatGPTSearch worklore stories by keyword across title, summary, tags and stack. Returns matches with their capability tier.
suggest_for_project
ChatGPTGiven a short description of the current project (stack, what you're building, recent problems), suggest up to 3 worklore stories worth reproducing here, with why each fits and its tier.
