mulch-record-from-evidence

name mulch-record-from-evidence description Turn the evidence of a finished work session — git commits, changed files, recently-touched seeds issues — into well-formed `ml record` invocations. Use at session close, when an agent has made changes worth preserving as mulch expertise but hasn't yet recorded them. tools ["bun","git","ml"] inputs ["a finished (or nearly finished) unit of work in the mulch repo","optionally, a specific commit range or seeds issue id to scope the evidence"] outputs ["one or more validated records appended to `.mulch/expertise/<domain>.jsonl`","a committed `.mulch/` change via `ml sync`"] mulch-record-from-evidence Use this skill when you have just finished a chunk of work in the mulch repo and need to preserve the durable insights as mulch expertise . It converts concrete evidence — what you changed, why, and what broke along the way — into precise ml record calls, instead of inventing ritual filler records. Unrecorded learnings are lost; vague records are noise. The goal is a small number of high-signal records, each backed by real evidence. When NOT to record Skip recording entirely if the session produced no durable insight: a trivial typo fix, a dependency bump with no behavioral change, or a revert. A record that just restates the diff is noise. Only record a convention, pattern, decision, or failure that a future agent would benefit from knowing before touching the same area. Pre-flight Confirm you are at the mulch repo root and the store is healthy: ml status # per-domain health + record counts ml doctor # exits 0 when records are intact If ml doctor reports problems, fix the store first (see RUNBOOK.md §4) — do not record on top of a corrupt JSONL. Procedure 1. Gather the evidence Let mulch tell you what changed and which domains are implicated: ml learn # changed files + suggested domains git status # uncommitted work git diff -- stat HEAD~1 # what the last commit touched git log --oneline -5 # recent commit subjects If the work maps to a tracker, pull its context too: sd show <issue-id> # the seeds issue you were working Write down, for each insight candidate: what you learned, which file or subsystem it concerns, and what evidence supports it (a commit sha, a changed file, a failing test you fixed). 2. Classify each candidate For every insight worth keeping, decide: Domain — which .mulch/expertise/<domain>.jsonl it belongs to. Run ml status to see existing domains; match the subsystem you touched (e.g. CLI behavior → cli , test infra → testing , type conventions → typescript ). Respect the project's per-domain allowed_types rules printed at the top of ml prime — a domain may only accept certain types. Type — convention (a rule to follow), pattern (a reusable approach that worked), decision (a choice made and its rationale), failure (something that broke and how it was resolved), reference (an external fact/link), or guide (a procedure). Custom project types (e.g. flake_symptom , release_decision ) carry extra required fields — ml record will tell you which. Classification — foundational (permanent truth), tactical (relevant ~14 days), observational (relevant ~30 days). Default to the shortest shelf life that fits; only mark foundational when the insight is a lasting invariant. 3. Emit the ml record calls Run one ml record per insight. Evidence auto-populates from the current git commit and changed files; link explicitly when you can: ml record cli -- type convention \ --description "ml ready/prime/compact reject non-integer --limit/--budget with exit 1; each command inlines its own parseStrictPositiveInt rather than sharing a util" \ --evidence-seeds <issue-id> Useful evidence flags: --evidence-seeds <id> / --evidence-gh <id> — link a tracker. --evidence-commit <sha> — pin a specific commit. --relates-to <mx-id> — link a related mulch record. Naming a record (a stable identity) makes a re-record merge outcomes into the existing entry instead of appending a duplicate — prefer this when you are refining an insight you recorded before. If validation fails, mulch prints a copy-paste retry hint with the missing required fields pre-filled; fill them in and re-run. 4. Verify and commit ml validate # confirm every new record is well-formed ml prime <domain> # eyeball that the new record reads cleanly ml sync # validate, stage, and commit .mulch/ Do not git push unless the user asks — leave the commit local. Acceptance The skill is complete when all hold: Each durable insight from the session is captured by exactly one record (no duplicates, no filler). ml validate exits 0. ml prime <domain> shows the new record(s) with sensible domain/type/classification. ml sync has committed the .mulch/ change; git status is clean. Failure modes Symptom Likely cause Remedy ml record rejects --type for a domain The domain's allowed_types doesn't permit that type. Pick an allowed type (check the contract at the top of ml prime ), or record under a different domain. Validation error about a missing field A custom type requires extra fields. Re-run with the fields from the printed retry hint. Two near-identical records appear Recorded anonymously twice instead of naming the record. Name the record so re-records merge; remove the duplicate with ml delete <id> . ml sync reports an unknown type Config declaring the custom type hasn't merged yet. Wait for config to land, or re-run after merging; sync intentionally ignores --allow-unknown-types . Further reading AGENTS.md — repo-wide conventions and the agent workflow. CLAUDE.md — record types, classifications, and the registry layer. CONFIG.md — .mulch/mulch.config.yaml reference (domains, custom types, hooks). RUNBOOK.md — operational procedures, including debugging a broken store.