io.github.worklore/worklore

Revise 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.

Publish 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.

Disclose 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. Prefer a `url` over pasting a file: a skill on GitHub (github.com or raw.githubusercontent.com, up to 200 KB) needs no sign-in, and you do not have to copy the whole file into the call. Other URLs need a signed-in session. If you paste `text`, also pass `expected_sha256` (the file's sha256, e.g. `shasum -a 256 SKILL.md`): a copy that changed on the way is reported instead of scanned. `received` says exactly what was scanned.

Record the result of a story's read-only 'Check if this applies to you' section, run in the user's own project (attributed to the signed-in account). Run ONLY that check — change nothing, apply no fix. First decide whether it applies at all: if the project does not match the check's preconditions, do NOT call this — 'not applicable' is never recorded. Otherwise report 'has_problem' or 'no_problem', honestly. One counted result per person per story; a later report replaces the earlier one. Only stories whose get_story result has has_check: true can be checked.

Call 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.

Given 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.

Fetch 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.

Search worklore stories by keyword across title, summary, tags and stack. Returns matches with their capability tier.