Extract Work Items

Extract Work Items from Meta Documents

!accelerator config context --skill extract-work-items --fail-safe !accelerator config agents --fail-safe

If no "Agent Names" section appears above, use these defaults: accelerator:reviewer, accelerator:codebase-locator, accelerator:codebase-analyser, accelerator:codebase-pattern-finder, accelerator:documents-locator, accelerator:documents-analyser, accelerator:web-search-researcher.

Work items directory: !accelerator config path work --fail-safe Research directory: !accelerator config path research_codebase --fail-safe Plans directory: !accelerator config path plans --fail-safe

Work Item Template

The template below defines the sections and frontmatter fields that every work item must contain. Read it now — the valid work item kinds live in the kind field (not a hardcoded list elsewhere in this skill), and every written file must populate every frontmatter field.

!accelerator config template work-item --fail-safe

You are tasked with identifying requirements, work items, and actionable tasks within existing meta documents and helping the user capture them as formal work items. Source documents typically tell you what work items should exist but rarely give the full business context, testable acceptance criteria, dependencies, and assumptions a good work item needs. The model, the user, and web research fill those gaps.

Extraction therefore proceeds in two layers:

• Source-derived content stays faithful. Anything you draw from the source documents must reflect what they actually say — do not silently invent requirements that are not there.
• Business-context gaps are surfaced. The Assumptions, Open Questions and Drafting Notes sections serve different purposes when present, and all matter:
  • Assumptions are interpretations you made that affect the work item's meaning. Flag one only when using the wrong interpretation would lead someone to build something different. Example: "Interpreted 'users' as end users rather than internal staff — if wrong, scope changes."
  • Open Questions are genuine unknowns the source raises but leaves unanswered that a reader or implementer needs resolved before work can proceed. Example: "What does 'better results' mean — improved relevance, faster delivery, or both?"
  • Drafting Notes capture interpretations you made while filling out the work item — business-context calls, scope decisions, or technical choices that someone should review if they turn out to be wrong. Actively populate this section. If you inferred who the stakeholders are, what a vague term means, what the scope boundary is, or which technical approach the source implies, write it down. Examples: "Treated this as a spike because no acceptance criteria are defined — if implementation is already expected, kind and scope both change." Routine field selections (kind, priority, tags) don't need an entry unless the choice reflects a substantive scope or meaning interpretation that a reviewer should be aware of.

For each selected candidate, you offer the user the choice between enriching the work item interactively (with model knowledge, web research, and a few focused questions, similar to /create-work-item) or accepting the source-derived skeleton as-is and refining later. The enrichment loop is per candidate, not pre-generated, so the work the model invests matches the depth the user wants for each work item.

Initial Setup

When this command is invoked:

1. Check if parameters were provided:

• If one or more file paths were provided, note them as the target documents
• If no parameters provided, ask conversationally and helpfully which documents to work with. Give the user enough context to respond easily: briefly explain what kinds of sources work (specific files, planning docs, meeting notes, research, specs), mention that you can also scan all documents in the configured directories, and invite them to direct. Helpful examples matter more here than brevity — aim for something welcoming and informative rather than a single terse question.

Wait for user input.

Process Steps

Step 1: Identify Source Documents

1. If specific files were provided, read them FULLY. Before reading, verify each path exists. If any path does not exist, report which paths are missing and ask the user to correct or remove them — do not silently skip them or proceed with an empty set.
2. If scanning all meta documents:
   • Spawn a {documents locator agent} agent to find all documents in the configured research and plans directories (shown above)
   • Present the discovered documents and let the user select which to scan:

     I found the following documents:
     
     **Research:**
     - `{research directory}/2026-04-08-topic.md` — Topic research
     - ...
     
     **Plans:**
     - `{plans directory}/2026-04-19-feature.md` — Feature plan
     - ...
     
     Which documents should I scan for work items? (enter numbers, "all", or
     specific paths)
     

   • Wait for user selection.

Step 2: Analyse Documents for Work Items

1. Spawn {documents analyser agent} agents (one per document, in parallel) with instructions to identify requirements and actionable work items. Look for:

   • Explicit requirements ("The system must...", "Users need to...", "We need to implement...")
   • User stories ("As a..., I want..., so that...")
   • Feature descriptions and acceptance criteria
   • Bug reports with symptoms and expected behaviour
   • Open-ended investigations or unknowns requiring research
   • Multi-deliverable themes that span several stories
   • One-off tasks (migrations, infrastructure work, documentation)

2. Wait for all agents to complete.

3. Deduplicate: Where the same work item appears across multiple documents, merge the entries and record all source documents it came from.

4. Present discovered candidates as a numbered list:

I found the following actionable items across the scanned documents:

1. **[Short title]** — [one-line description]
   Source: `{research directory}/2026-04-08-topic.md`

2. **[Short title]** — [one-line description]
   Source: `{plans directory}/2026-04-19-feature.md`, `{research directory}/2026-04-08-topic.md`

3. ...

Which items would you like to create work items for? (enter numbers, "all",
or "none")

If no actionable items are found across all documents, inform the user: "No actionable items found in the provided documents." and exit cleanly.

Wait for user selection. If the user selects "none", exit cleanly without writing any files.

Step 3: Enrich and Approve (Per Candidate)

For each selected candidate, in original presented order, build a source-derived skeleton, present it, and let the user choose how much enrichment to invest. Do NOT pre-generate drafts for the entire batch in advance — enrichment can change a draft significantly, so generation happens per candidate inside this loop.

3.1 Build the source-derived skeleton

For the current candidate:

• Infer the work item kind from its content using kinds read from the work item template's kind field:
  • clear bug reports with symptoms and expected/actual behaviour → bug
  • open-ended investigations with specific questions → spike
  • broad multi-deliverable themes → epic
  • specific single deliverables → story
  • one-off technical or operational tasks → task
  • Default to story for items where the kind is genuinely ambiguous.
• Draft a complete work item from the source content alone, using XXXX as the placeholder work item number.
• Kind-specific content placement:
  • bug: reproduction steps, expected/actual behaviour → Requirements section
  • spike: research questions, time-box, exit criteria → Requirements section
  • epic: initial story decomposition → Requirements section as a list
  • Do not rename or add sections beyond those in the work item template.
• Surface business-context gaps using the right section: put your interpretation in Assumptions (when you made a call and the wrong call changes what gets built). Put unanswered questions in Open Questions when you genuinely cannot tell from the source. Populate Open Questions with anything that would materially change scope, approach, or acceptance criteria if resolved differently. Add a Drafting Notes entry for every meaningful interpretation you made (scope boundaries, who stakeholders are, what vague terms mean, which technical approach is implied).
• Include all source documents for this item in the References section.

3.2 Present the skeleton with options

Candidate #N of M: [title]
Kind (proposed): [kind]
Source: [paths]

[work item content with XXXX placeholder, including Drafting Notes section]

Use the AskUserQuestion tool with four options:

1. Enrich — interactive Q&A, web research where useful, then approve
2. Accept as-is — write this skeleton as a thin draft for later refinement
3. Skip — exclude from this batch
4. Accept all remaining — fast-path every remaining candidate as a thin draft

Wait for the user's choice. If the user enters free-form text via the "Other" input, treat it as Enrich seeded with those instructions: enter the enrichment loop in 3.3 using the supplied text as the first round of revision guidance, skipping the question phase if the instructions are already substantive.

3.3 enrich — interactive enrichment

Treat this as a focused, per-candidate version of the /create-work-item flow, seeded with the skeleton above:

1. Ask 1–3 focused business-context questions tailored to this candidate. Fewer than /create-work-item's 3–5 because the source already provides some context. Cover whichever of the following are not already clear from the source:

   • What pain point or problem does this address, and who experiences it?
   • What is the desired outcome — what changes for people once this is done?
   • Are there constraints, deadlines, or dependencies worth knowing?
   • For bugs: what is the impact, and is it a blocker?
   • Anything you are uncertain about, or that should be researched?

2. Spawn {web-search-researcher agent} when there is uncertainty about any aspect of this candidate the model lacks confidence on — business rules, domain concepts, competitive landscape, industry standards, external technology. Skip only when the candidate is self-contained and well-understood from the source plus user answers. When in doubt, prefer to spawn research — over-asking is cheaper than producing a vague enriched draft.

3. Update the draft combining source content, user answers, model knowledge, and research findings. Re-present it as a structured proposal that:

   • Confirms or revises the kind
   • Lists requirements drawn from source + enrichment
   • Proposes specific, testable acceptance criteria — prefer Given/When/Then for story/task; draw on domain knowledge and research to make them thorough
   • Lists dependencies (blocking and blocked) where known
   • Keeps a Drafting Notes section for any interpretations still unresolved so the user can challenge them
   • Lists remaining open questions in the Open Questions section

   Then use the AskUserQuestion tool with four options:

   1. Approve — accept this draft and move on to the next candidate
   2. Revise — update the draft with additional instructions (prompted after via plain-text — do NOT use AskUserQuestion for the instructions)
   3. Skip — discard this candidate entirely
   4. Accept as-is — discard enrichment and use the original source-only skeleton

4. Iterate on the same candidate. Challenge weak or untestable acceptance criteria — when a criterion is not measurable, ask what a passing test would look like and reformulate together. Do not accept vague criteria into the draft. The candidate position (e.g. "Candidate #N of M") does NOT advance during iteration: re-present the updated draft for the same candidate and accept further free-form revision instructions, looping as many times as needed. Only approve (or one of the interrupts below) advances to candidate N+1. Once the user explicitly approves, mark the candidate approved (enriched) and advance to the next candidate.

The user may switch out of enrichment at any point by saying:

• skip — exclude this candidate entirely. Discard any partial enrichment work — questions answered, drafts re-proposed — and never approve it later, even if accept remaining as-is is invoked on a subsequent candidate.
• accept as-is — replace the in-flight enriched draft with the original 3.1 source-derived skeleton (NOT the partially enriched state) and apply the 3.4 thin-draft assumptions note. The user is saying "stop enriching this one; take the source-only version".

Honour these interrupts immediately on the turn they are received.

3.4 accept as-is — thin draft

Take the source-derived skeleton from 3.1 as the final draft for this candidate. Append (or extend) the Drafting Notes section with the following note verbatim (so future tooling like /refine-work-item and human reviewers can detect thin drafts deterministically):

Extracted from source documents without interactive enrichment. Acceptance criteria, dependencies, and kind may need refinement before promoting from draft to ready.

If the candidate already has source-derived drafting notes, keep them and add the verbatim note as a separate paragraph beneath. Keep any Open Questions from the 3.1 skeleton — these are genuine business unknowns that need resolution regardless of whether enrichment happened. Mark the candidate approved (thin) and advance to the next.

3.5 skip

Exclude this candidate from the batch and advance to the next. Skipped candidates never become approved later, even if a subsequent accept remaining as-is is used.

3.6 accept remaining as-is — fast-path

Mark every remaining unreviewed candidate as approved (thin), applying the same skeleton + assumptions note as 3.4. Do not ask further questions. Already-skipped candidates stay skipped. Jump to Step 4.

3.7 No accelerator work next-number calls in Step 3

accelerator work next-number is not called at any point during Step 3, regardless of which option the user picks. Writing happens exclusively in Step 4 after all approvals — enriched and thin — are collected.

Step 4: Write Work Items

1. Count approved (non-skipped) items: N.

2. If N is 0: print "No work items approved — nothing written." and exit cleanly. Do NOT call accelerator work next-number.

3. Otherwise:

   a. Compute target slugs — for each approved draft, derive a meaningful kebab-case slug from its title.

   b. Read configuration. Invoke each script by its bare path and use the command's stdout as the named value — do not wrap the call in a VAR=$(…) assignment, as the assignment (not the path) would become the command and escape the rule:

   • Run accelerator config work id_pattern and use its stdout as PATTERN.
   • Run accelerator config work default_project_code and use its stdout as DEFAULT_PROJECT.

   Run the bare path directly as an executable; never prefix it with bash/sh/env (a wrapper prefix escapes the skill's allowed-tools permission and forces an unnecessary prompt).

   c. Suggest projected IDs: if PATTERN contains {project}, the default project for each row is DEFAULT_PROJECT (warn and require user amendment if DEFAULT_PROJECT is empty). If PATTERN lacks {project}, no project column is shown.

   Compute display-only projected IDs by calling, per distinct project code:

   accelerator work next-number --project <code> --count <count-for-that-project>
   

   These calls do not commit numbers; the same call is re-issued after every amendment to keep the table accurate.

   d. Present an amendment table:

   | # | Slug       | Project | Projected ID |
   | 1 | add-foo    | PROJ    | PROJ-0001    |
   | 2 | fix-bar    | PROJ    | PROJ-0002    |
   | 3 | update-baz | PROJ    | PROJ-0003    |
   
   Amend any rows? (`<rows> <PROJECT>` to set, `<rows> -` to revert to
   default, `?` for help, `q` to cancel, blank to confirm.)
   

   When PATTERN lacks {project}, omit the Project column and render only | # | Slug | Projected ID |. The amendment prompt is not shown in that case — proceed directly to confirmation.

   Amendment grammar (canonical — same wording in every state):

   • <rows>: one row number (2) or comma-separated list (2,3,7). Whitespace around commas is permitted (2, 3, 7) and trimmed.
   • <PROJECT>: a project code matching [A-Za-z][A-Za-z0-9]*.
   • <rows> -: revert the named rows to the default project code (or to "no project" when no default is set).
   • ?: re-display the amendment grammar reference plus the unchanged table; no state change.
   • q: cancel the entire flow with no files written and no numbers allocated.
   • Blank input: confirms the current table state.

   Validation: out-of-range row numbers re-prompt with error: row N — out of range (valid: 1-M) without applying any other amendments in the same input. Invalid project codes re-prompt with error: row N — project value "<value>" must match [A-Za-z][A-Za-z0-9]* and discard the entire input (no partial application). Unrecognised commands re-prompt with error: unrecognised input. Type ? for help. On any rejection the table reverts to its last valid state.

   After every accepted amendment, recompute projected IDs by re-issuing the per-project allocator calls (display only).

   e. Project-aware slug-collision check before any allocation. For each row, glob:

   • When the pattern has {project}: {work_dir}/<project>-*-<slug>.md for the row's project — a same-slug file under the same project is a real collision.
   • Always (legacy fallback): {work_dir}/[0-9][0-9][0-9][0-9]-<slug>.md — a same-slug legacy file shadows the new file regardless of project.

   Within the same batch, two amendments to the same project with the same slug are also a collision. Same slug under different projects (PROJ-0001-add-foo.md and OTHER-0001-add-foo.md) is legitimate and not a collision.

   If any collision is detected, report which slugs collide and which existing files they match, abort without calling the allocator, and ask the user to resolve the collision before re-running.

   f. Allocate per distinct project code, in original presentation order:

   accelerator work next-number --project <code> --count <count>
   

   One call per distinct project code; --project is omitted when the pattern lacks {project}. If any allocator call exits non-zero, abort immediately and surface the error verbatim — do not write any files. The whole batch fails atomically.

   g. Substitute the allocated full IDs into approved drafts in their original presented order. Within a single project, the first row in presentation order takes the first allocated number; multiple projects each preserve their own ordering. The id frontmatter is always quoted ("PROJ-0001").

   h. Populate frontmatter for every approved draft. Before writing each file, capture metadata and substitute the unified base fields into the template's frontmatter block:

   1. Invoke accelerator corpus metadata derive once for the batch to obtain Current Date/Time (UTC):, Current Revision:, and Repository Name:.

   2. For each approved draft, substitute every field below with the indicated value:

      • type: ← work-item
      • id: ← the allocated full ID, always quoted as a YAML string (e.g. id: "PROJ-0001")
      • title: ← the draft's H1 title
      • date: ← the Current Date/Time (UTC): value
      • author: ← the author value resolved per the rules in create-work-item/SKILL.md > author (config → VCS user → prompt)
      • producer: ← extract-work-items
      • status: ← draft
      • last_updated: ← the same Current Date/Time (UTC): value
      • last_updated_by: ← the same value resolved for author
      • schema_version: ← 1 (bare integer, not quoted)

      Optional linkage/foreign-ref keys are omit-by-default: the template shows each as ""/[], but write a key into the artifact only when it has a value, and omit it entirely otherwise (do not carry the empty placeholder through). By default a freshly extracted draft names none of them.

      • parent: ← the parent work item's ID as a typed-linkage ref ("work-item:NNNN"). Fill when the source names a parent; otherwise omit the key entirely.
      • blocks: ← list of typed-linkage refs to work items this item blocks (["work-item:NNNN", ...]). Fill when blocking edges are explicit in the source; otherwise omit the key.
      • blocked_by: ← list of typed-linkage refs to work items that block this one. Prefer writing the canonical blocks: on the other side; emit blocked_by: only when the canonical side cannot be written, and omit it otherwise.
      • derived_from: ← list of typed-linkage refs to artifacts this item is derived from (["plan:NNNN", ...]). Fill when derivation is explicit; otherwise omit the key.
      • relates_to: ← list of typed-linkage refs to related artifacts. Fill when relationships are explicit; otherwise omit the key.
      • source: ← typed-linkage ref to the originating source artifact ("issue-research:NNNN"). Fill when the source is a meta artifact with an id; otherwise omit the key.
      • external_id: ← cross-system pointer (e.g. a Jira/Linear key). Fill when the item is linked to an external tracker; otherwise omit the key.

   i. Write all N work item files. Each work item's References section must include all source document paths the item was extracted from. For deduplicated items that appeared in multiple documents, list every contributing source under References, one per line.

   j. If a write error occurs mid-batch: report which numbers were allocated, which files were written successfully, and which were not — so the user can manually write the missing files with their pre-assigned IDs. Do not retry writes silently and do not call the allocator again to re-allocate; the original allocation stands. The user needs to know the exact state.

4. Print a summary table:

Created the following work items:
| ID         | Title   | File                                 |
|------------|---------|--------------------------------------|
| PROJ-0001  | [title] | `{work_dir}/PROJ-0001-slug.md`       |
| OTHER-0001 | [title] | `{work_dir}/OTHER-0001-slug.md`      |
...

Under the default {number:04d} pattern the ID column shows 0001, 0002, etc., and no project amendment table appears.

Validate the frontmatter: after writing, run the validator on each work item written this batch

accelerator corpus frontmatter validate --file <each written work item path>

If any invocation exits non-zero, the document violates the canonical frontmatter standard; report the emitted violation and fix the frontmatter before completing.

Quality Guidelines

• Never call accelerator work next-number before all approvals are collected. The number space is shared and finite; consuming numbers for drafts the user might still skip creates gaps that are impossible to clean up later.
• Never call accelerator work next-number when N=0. An all-skipped session must exit cleanly with no side effects.
• If accelerator work next-number exits non-zero, abort immediately and surface the script's error output verbatim — even if it emitted some numbers on stdout before failing, treat the entire batch as failed.
• Verify all target slugs are free BEFORE calling accelerator work next-number — collision checks happen before number allocation, by slug pattern, since numbers are not yet known. Under a {project} pattern the collision check is project-aware: the same slug under two different project codes (PROJ-0001-add-foo.md and OTHER-0001-add-foo.md) is legitimate. Same-slug legacy NNNN-{slug}.md files always count as a collision.
• Under a {project} pattern, the amendment table prompts the user to assign or override project codes per row before allocation. The display-only projected IDs are recomputed after every amendment. No numbers are committed until the user confirms with blank input.
• Numbers are assigned to approved drafts in their original presented order, not in approval timestamp order. This makes outputs deterministic and matches the order the user reviewed.
• Every written work item MUST include all source document paths in its References section. For deduplicated items that appeared in multiple documents, list every contributing source.
• Do not extract structural or navigational content (table of contents entries, section headings with no requirements content, agenda items with no actionable outcome) as candidate work items. If a heading just organises content rather than describing work, skip it.
• Work item kind inference must use kinds read from the work item template frontmatter (loaded at the top of this skill), not a hardcoded list. Default to story for items where the kind is genuinely ambiguous.
• All frontmatter fields defined in the work item template must be populated in every written work item — type (work-item), id matching the assigned full ID, title matching the work item's title, date, author, producer (extract-work-items), status (draft), kind, priority (medium unless the source implies otherwise), parent (empty string unless the source establishes a parent), external_id (empty unless set), tags (a YAML array, possibly empty), last_updated, last_updated_by, and schema_version: 1. No field may contain unfilled placeholder text like [author] or NNNN. The body H1 format is # <full-id>: <title> — kept in sync with the frontmatter title: field.
• date must use the work item template's YYYY-MM-DDTHH:MM:SS+00:00 format in UTC (e.g. obtained via date -u +%Y-%m-%dT%H:%M:%S+00:00).
• author is sourced in this order: configuration if present, then the source document's author/owner field if named, then the current git/jj user identity, then — only if all of those fail — ask the user once before writing the batch. Never write [author] or any placeholder.
• "Accept remaining as-is" only marks unreviewed candidates as approved (thin) — it does not resurrect skipped candidates, and writing still happens exclusively in Step 4 after the single accelerator work next-number call.
• Source-derived content stays faithful to what the source documents say. Do not silently invent requirements. When you make an interpretation while filling out the work item — about scope, stakeholders, terminology, or implied approach — capture it in Drafting Notes. Use Open Questions for genuine unknowns the source leaves unanswered. A Drafting Note is worth writing whenever the wrong interpretation would send someone in a meaningfully different direction.
• The enrichment loop (3.3) is per candidate. Do not pre-generate enriched drafts for the whole batch — enrichment depends on the user's answers and the model's research, both of which differ per candidate. Build the source-derived skeleton (3.1), present it (3.2), then enrich one candidate at a time.
• Web research ({web-search-researcher agent}) is a first-class step inside enrichment. Spawn it whenever there is uncertainty about domain, business, competitive, or technical aspects of the candidate. Skip only when the candidate is self-contained and well-understood.
• Thin drafts (accepted as-is) and enriched drafts coexist in the same work items directory. Thin drafts must carry the verbatim Drafting Notes entry recording non-enrichment so a future /refine-work-item invocation (or manual review) can identify them as needing follow-up before promotion from draft to ready.
• Acceptance criteria in enriched drafts must be specific and testable; prefer Given/When/Then for story/task. Challenge any criterion that is not measurable before accepting it into the draft.

!accelerator config instructions extract-work-items --fail-safe