git:20261026.c5c88ff to git:20261028.108bdc4

46 added, 6 removed. Audit A to A.

---

name: skillify

- description: Turn a recurring "clone-the-last-ticket" workflow into a parameterized skill of its own — phased pipeline, QC checkpoints, golden-replay test, hard-halt before external posts.

+ description: Turn a workflow you repeat by cloning the last ticket into a parameterized skill of its own, with a phased pipeline, QC checkpoints, a golden replay test, and a hard stop before external posts.

---

-

+

# /skillify

The meta-skill (`skillify_everything`). When a task recurs (quarterly sale, monthly report,

periodic reconciliation), this stamps out a new **folder skill** with the generated-skill skeleton:

a phased pipeline with hard halts, parameterized SQL/steps, a QC checkpoint, a golden-replay

- regression test, and a hard halt before any external post. `/refresh index` + the `--recurring`

- flag of `bin/build_ticket_index.py` surface candidates ("this object shows up in 7 tickets").

+ regression test, and a hard halt before any external post. Candidates come from two places: the

+ repeat check in [repeats.md](repeats.md) (`recall.py --repeats`: this ticket reads the same tables

+ as an earlier one), which `/ticket`, `/ship`'s retro and a session's closing message run at the

+ second instance, and the `--recurring` flag of `bin/build_ticket_index.py` ("this object shows up

+ in 7 tickets").

+ **Where it came from.** When the run starts from a repeat suggestion, it arrives with the tickets

+ the pattern came from. Use them as the golden fixture's known-good run (Phase 1) and write them,

+ always as `<owner>/<id>`, into the new skill's `built_from:`, with the tables they read in

+ `read_objects:`. That is how the next repeat check finds this skill instead of suggesting a second

+ one. A bare id that more than one owner has is not written: ask which ticket it meant.

+ ## How this skill asks the person something

+

+ The kit's one question rule, the same in every skill that asks:

+ - Read `capabilities` from this skill's `kit_paths.py --json` probe the first time a question

+ comes up, and keep the result.

+ - `capabilities.structured_questions` is `yes` and `capabilities.question_tool` is not `none` →

+ **ask with that tool**, within `capabilities.question_limits` (when that says `unknown`: one

+ question per call, at most four options). Group up to the limit only questions that do not

+ depend on each other's answers; ask a dependent question after the one it depends on.

+ - Otherwise (`no` / `unknown`, or the probe failed) → ask in chat as a short numbered list, one

+ question at a time unless this skill says a set goes in one round, and wait for an explicit

+ answer.

+ - The question content is the same either way; only how it is shown changes. No skill names a

+ runtime's tool: the runtime's own adapter (`adapters/runtime/<name>.md`) does. A question tool

+ is not available inside a subagent, so the main session asks.

+ - **Never invent an option.** Options come from what this run found or the fixed choices a step

+ names. A recommended option goes first, marked recommended, with where it came from; the person

+ still picks. Each option says what choosing it changes, and free text is always possible (the

+ tool's own "Other", or "or tell me something else" in prose).

+ - Whatever the person needs to answer is already in chat before the question, so a picker never

+ hides it.

+ - A reply the kit asks for word for word (`ship unreviewed`, `go ahead`, the `yes` before

+ destructive SQL) stays typed: an unreviewed ship, an external send or an irreversible write is

+ never one click.

+

+ In this skill the questions are Phase 1's interview about the workflow.

## Phase 1 — Interview the workflow (reduce_assumptions)

- Ask, with the runtime's own question tool when it has one (`bin/tw kit_paths.py --json` → `capabilities.question_tool`), otherwise as a numbered list one question at a time, for:

+ Ask by the question rule in "How this skill asks the person something" above (its probe is

+ `bash "$(git rev-parse --show-toplevel 2>/dev/null || echo .)/bin/tw" kit_paths.py --json`), one question at a time

+ in prose, for:

- **Name & cadence** (→ kebab skill name) and the **parameters** that change each run (e.g.

`--period`, `--as-of-date`, `--ticket`, an input file) — with formats + validation rules.

- **Steps**: the ordered phases. For each, what it reads, what it writes, and whether it has

external side effects or DB writes (those become hard-halt / approval gates).

- **QC checks**: per check — what it tests, and its tier (hard-halt / warn / info) + remediation hint.

- **Determinism anchor**: the known-good prior run to use as the **golden fixture** (counts/totals

to assert; the byte-identical output file to diff against).

## Phase 2 — Stamp from the template (HARD HALT → confirm before writing a new skill)

`/skillify` is model-invocable: the model may reach for it when it notices a workflow recurring.

Because this phase writes a **brand-new skill** into the repo — self-modifying tooling — it stops

first. **Print the plan, then stop and wait for the user** before any file is created: the skill

name, its path (`.claude/skills/<name>/`), and the file list about to be written (`SKILL.md`,

`sql/`, `templates/`, `bin/`, `golden/`). Only on explicit confirmation, stamp the files.

1. `KIT="$(bash "$(git rev-parse --show-toplevel 2>/dev/null || echo .)/bin/tw" --kit)"`, then copy

`"$KIT"/templates/generated-skill/` → `.claude/skills/<name>/` (SKILL.md + `sql/ templates/

bin/ golden/`).

- 2. Render `SKILL.md.tmpl` with the interview answers into the canonical phase shape:

+ 2. Render `SKILL.md.tmpl` with the interview answers (`built_from` and `read_objects` are

+ comma-separated, and empty when the run did not start from a repeat) into the canonical phase shape:

**Phase 0** pre-flight (validate params + `verify_stack` for touched seams + an object drift

check → halt-on-fail) → **render & run** parameterized steps → **QC** atomic checkpoint

(hard-halt tiers) → **export** (deterministic `ORDER BY`) → **render docs** (README +

tracker-comment draft) → **HARD HALT for human review** → **post-review** (the external posts,

in a separate invocation).

3. Write the `{{token}}` step files into `sql/` (or `bin/` for non-SQL), the output templates into

`templates/`, and seed `golden/<run>.json` with the determinism anchor. **Follow the two SQL

authoring rules in [authoring.md](authoring.md)** — a render gate enforces both.

4. Scaffold `bin/drift_check.sh` (confirms every object the workflow reads is still reachable —

the Phase-0 catch for relocations).

## Phase 3 — Wire to the stack & self-test

5. The stamped skill reads `.claude/config/stack.yaml` and resolves all I/O through adapters (no

hardcoded tools in the orchestration). Confirm the SKILL.md prose is tool-neutral:

`!grep -REn "acli|snow |slack|gh " .claude/skills/<name>/SKILL.md || echo OK`. (The leaf

`bin/drift_check.sh` is allowed to name your warehouse CLI — it's the tool-bound probe.)

6. Wire the **render gate** (`bin/render_and_validate.sh`) and **export helpers**

(`bin/split_and_export.sh`) into the stamped phases so authoring defects fail *closed*, not

silently — details and rationale in [authoring.md](authoring.md).

7. Document the **golden test** + **failure-mode tests** in the new SKILL.md (how to replay the

known run and assert the fixture; how each Phase-0/QC/render-gate halt is expected to trigger).

8. **Report** the new skill path, its parameters, and how to run its golden test.

## Output shape (what every stamped skill guarantees)

Parameterized · phased · QC-gated · golden-tested · hard-halts before external posts · idempotent

where possible (re-run converges) · records a rollback key for any DB write. Heavy pulls run in the

**background**, not the foreground — mark slow phases at stamp time so the operator launches them

detached from the start.