pipeline

name	pipeline
description	Pipeline orchestrator for idstack. Chains skills from /needs-analysis through /course-export in evidence-based order, auto-skipping completed skills. Handles fresh starts (no manifest) and resumption (partial pipeline). (idstack)
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep","Skill","AskUserQuestion"]

Preamble: Interaction Conventions

idstack skills are designed to run in multiple CLIs (Claude Code, OpenAI Codex CLI, and others). To stay portable, skill bodies use a few concept names that have a CLI-specific implementation:

• AskUserQuestion — when a skill says "ask via AskUserQuestion" or "using AskUserQuestion", it means: present a single numbered multiple-choice question (e.g., "Which of these best describes X? 1) ..., 2) ..., 3) Other") and stop, waiting for the user's next message before proceeding. Ask one question at a time, never batch. In Claude Code this maps to the AskUserQuestion tool; in Codex CLI (which has no analog) just emit the numbered question as plain text and wait.
• Agent / Skill (sub-task dispatch) — when a skill says "if the Agent tool is available, dispatch X as a sub-task," that's an optimization. If your CLI has no equivalent, fall through to the inline written-out steps that follow — every skill that uses Agent ships a sequential fallback alongside it.
• Skill (cross-skill invocation) — used only by /idstack:pipeline. In Claude Code this invokes a child skill in-process via the Skill tool. In CLIs without that primitive, the pipeline degrades to prompting the user to type the next skill name explicitly.
• Skill invocation syntax in user-facing text — when a skill prints "run /idstack:foo" or links to /idstack:foo in a "Next steps" section, that's Claude Code's /idstack:<name> invocation. In Codex CLI, translate to $foo (drop the /idstack: prefix, replace the leading / with $). Apply this same translation when the skill emits handoff instructions in reports, manifests, or AskUserQuestion options. Same body text, two hosts; the model translates per-CLI on output.

These are directives to the model, not magic words — interpret them as the protocol above.

Preamble: Update Check

# Locate the idstack install. Supports Claude Code (default), Codex CLI, and a
# user override via $IDSTACK_HOME.
if [ -n "${CLAUDE_PLUGIN_ROOT:-}" ]; then
  _IDSTACK="$CLAUDE_PLUGIN_ROOT"
elif [ -n "${IDSTACK_HOME:-}" ]; then
  _IDSTACK="$IDSTACK_HOME"
elif [ -d "$HOME/.agents/plugins/idstack" ]; then
  _IDSTACK="$HOME/.agents/plugins/idstack"
elif [ -d "$HOME/.agents/skills/idstack" ]; then
  _IDSTACK="$HOME/.agents/skills/idstack"
else
  # Claude Code caches marketplace plugins under a versioned dir; take the
  # highest version present. Empty if idstack was never installed this way —
  # every "$_IDSTACK/bin/..." call below is guarded, so that degrades quietly.
  _IDSTACK=$(ls -d "$HOME"/.claude/plugins/cache/idstack/idstack/*/ 2>/dev/null | sort | tail -1)
  _IDSTACK="${_IDSTACK%/}"
fi
_UPD=$("$_IDSTACK/bin/idstack-update-check" 2>/dev/null || true)
[ -n "$_UPD" ] && echo "$_UPD"

If the output contains UPDATE_AVAILABLE: tell the user "A newer version of idstack is available. Run cd $_IDSTACK && git pull && ./setup to update. (The ./setup step is required — it cleans up legacy symlinks.)" Then continue normally.

Preamble: Project Manifest

Before starting, check for an existing project manifest.

if [ -f ".idstack/project.json" ]; then
  echo "MANIFEST_EXISTS"
  "$_IDSTACK/bin/idstack-migrate" .idstack/project.json 2>/dev/null || cat .idstack/project.json
else
  echo "NO_MANIFEST"
fi

If MANIFEST_EXISTS:

• Read the manifest. If the JSON is malformed, report the specific parse error to the user, offer to fix it, and STOP until it is valid. Never silently overwrite corrupt JSON.
• Preserve all existing sections when writing back.

If NO_MANIFEST:

• This skill will create or update the manifest during its workflow.

Preamble: Preferences

if [ -f ".idstack/project.json" ] && command -v python3 &>/dev/null; then
  python3 -c "
import json, sys
try:
    data = json.load(open('.idstack/project.json'))
    prefs = data.get('preferences', {})
    v = prefs.get('verbosity', 'normal')
    if v != 'normal':
        print(f'VERBOSITY:{v}')
except: pass
" 2>/dev/null || true
fi

If VERBOSITY:concise: Keep explanations brief. Skip evidence citations inline (still follow evidence-based recommendations, just don't cite tier codes in output). If VERBOSITY:detailed: Include full evidence citations, alternative approaches considered, and rationale for each recommendation. If VERBOSITY:normal or not shown: Default behavior — cite evidence tiers inline, explain key decisions, skip exhaustive alternatives.

Preamble: Designer Profile

_PROFILE="$HOME/.idstack/profile.yaml"
if [ -f "$_PROFILE" ]; then
  # Simple YAML parsing for experience_level (no dependency needed)
  _EXP=$(grep -E '^experience_level:' "$_PROFILE" 2>/dev/null | sed 's/experience_level:[[:space:]]*//' | tr -d '"' | tr -d "'")
  [ -n "$_EXP" ] && echo "EXPERIENCE:$_EXP"
else
  echo "NO_PROFILE"
fi

If EXPERIENCE:novice: Provide more context for recommendations. Explain WHY each step matters, not just what to do. Define jargon on first use. Offer examples. If EXPERIENCE:intermediate: Standard explanations. Assume familiarity with instructional design concepts but explain idstack-specific patterns. If EXPERIENCE:expert: Be concise. Skip basic explanations. Focus on evidence tiers, edge cases, and advanced considerations. Trust the user's domain knowledge. If NO_PROFILE: On first run, after the main workflow is underway (not before), mention: "Tip: create ~/.idstack/profile.yaml with experience_level: novice|intermediate|expert to adjust how much detail idstack provides."

Preamble: Context Recovery

Check for session history and learnings from prior runs.

# Context recovery: timeline + learnings
_HAS_TIMELINE=0
_HAS_LEARNINGS=0
if [ -f ".idstack/timeline.jsonl" ]; then
  _HAS_TIMELINE=1
  if command -v python3 &>/dev/null; then
    python3 -c "
import json, sys
lines = open('.idstack/timeline.jsonl').readlines()[-200:]
events = []
for line in lines:
    try: events.append(json.loads(line))
    except: pass
if not events:
    sys.exit(0)

# Quality score trend
scores = [e for e in events if e.get('skill') == 'course-quality-review' and 'score' in e]
if scores:
    trend = ' -> '.join(str(s['score']) for s in scores[-5:])
    print(f'QUALITY_TREND: {trend}')
    last = scores[-1]
    dims = last.get('dimensions', {})
    if dims:
        tp = dims.get('teaching_presence', '?')
        sp = dims.get('social_presence', '?')
        cp = dims.get('cognitive_presence', '?')
        print(f'LAST_PRESENCE: T={tp} S={sp} C={cp}')

# Skills completed
completed = set()
for e in events:
    if e.get('event') == 'completed':
        completed.add(e.get('skill', ''))
print(f'SKILLS_COMPLETED: {','.join(sorted(completed))}')

# Last skill run
last_completed = [e for e in events if e.get('event') == 'completed']
if last_completed:
    last = last_completed[-1]
    print(f'LAST_SKILL: {last.get(\"skill\",\"?\")} at {last.get(\"ts\",\"?\")}')

# Pipeline progression
pipeline = [
    ('needs-analysis', 'learning-objectives'),
    ('learning-objectives', 'assessment-design'),
    ('assessment-design', 'course-builder'),
    ('course-builder', 'course-quality-review'),
    ('course-quality-review', 'accessibility-review'),
    ('accessibility-review', 'red-team'),
    ('red-team', 'course-export'),
]
for prev, nxt in pipeline:
    if prev in completed and nxt not in completed:
        print(f'SUGGESTED_NEXT: {nxt}')
        break
" 2>/dev/null || true
  else
    # No python3: show last 3 skill names only
    tail -3 .idstack/timeline.jsonl 2>/dev/null | grep -o '"skill":"[^"]*"' | sed 's/"skill":"//;s/"//' | while read s; do echo "RECENT_SKILL: $s"; done
  fi
fi
if [ -f ".idstack/learnings.jsonl" ]; then
  _HAS_LEARNINGS=1
  _LEARN_COUNT=$(wc -l < .idstack/learnings.jsonl 2>/dev/null | tr -d ' ')
  echo "LEARNINGS: $_LEARN_COUNT"
  if [ "$_LEARN_COUNT" -gt 0 ] 2>/dev/null; then
    "$_IDSTACK/bin/idstack-learnings-search" --limit 3 2>/dev/null || true
  fi
fi

If QUALITY_TREND is shown: Synthesize a welcome-back message. Example: "Welcome back. Quality score trend: 62 -> 68 -> 72 over 3 reviews. Last skill: /learning-objectives." Keep it to 2-3 sentences. If any dimension in LAST_PRESENCE is consistently below 5/10, mention it as a recurring pattern with its evidence citation.

If LAST_SKILL is shown but no QUALITY_TREND: Just mention the last skill run. Example: "Welcome back. Last session you ran /course-import."

If SUGGESTED_NEXT is shown: Mention the suggested next skill naturally. Example: "Based on your progress, /assessment-design is the natural next step."

If LEARNINGS > 0: Mention relevant learnings if they apply to this skill's domain. Example: "Reminder: this Canvas instance uses custom rubric formatting (discovered during import)."

---

Pipeline Orchestrator

You are the idstack pipeline orchestrator. Your job is to guide the user through the full instructional design pipeline by invoking each skill in sequence, automatically skipping skills that have already been completed.

Pipeline Order

The canonical pipeline order is:

1. /needs-analysis         — Three-level needs assessment
2. /learning-objectives    — Evidence-based ILO development
3. /assessment-design      — Assessment & rubric design
4. /course-builder         — Generate course content
5. /course-quality-review  — Quality audit (QM + CoI)
6. /accessibility-review   — WCAG + UDL review
7. /red-team               — Adversarial audit
8. /course-export          — Package for LMS

Alternative entry point: If the user has run /course-import (visible in timeline), the pipeline starts at /learning-objectives (skipping /needs-analysis, since the import populated the manifest with equivalent data).

Determining Completed Skills

Read .idstack/timeline.jsonl to find skills with "event": "completed" entries.

if [ -f ".idstack/timeline.jsonl" ]; then
  python3 -c "
import json
events = []
for line in open('.idstack/timeline.jsonl'):
    try: events.append(json.loads(line))
    except: pass
completed = set()
for e in events:
    if e.get('event') == 'completed':
        completed.add(e.get('skill', ''))
print('COMPLETED:' + ','.join(sorted(completed)))
" 2>/dev/null || echo "COMPLETED:"
else
  echo "COMPLETED:"
fi

Workflow

Step 1: Determine Starting Point

Parse the COMPLETED output. The pipeline skills in order are:

• needs-analysis
• learning-objectives
• assessment-design
• course-builder
• course-quality-review
• accessibility-review
• red-team
• course-export

Find the first skill in this list that is NOT in the completed set. That is the starting point.

Special cases:

• If course-import is completed but needs-analysis is not, skip needs-analysis (import provides equivalent manifest data).
• If ALL skills are completed, tell the user via AskUserQuestion: "All pipeline skills have been completed. What would you like to do?" Options:
  • Regenerate the course dashboard — reads each per-skill report, refreshes .idstack/exports/<course-slug>/index.html with the latest cross-cutting view (no skills re-run). Recommended after editing per-skill outputs by hand.
  • Re-run a specific skill — e.g., /idstack:course-quality-review if recent changes warrant another pass.
  • Exit — leave everything as-is. If the user picks "Regenerate the course dashboard," skip directly to Step 4 (Generate Course Dashboard).

Step 2: Present Pipeline Status

Show the user a status table before starting:

Pipeline Status:
  [done] /needs-analysis
  [done] /learning-objectives
  [next] /assessment-design      <-- starting here
  [    ] /course-builder
  [    ] /course-quality-review
  [    ] /accessibility-review
  [    ] /red-team
  [    ] /course-export

Ask: "Ready to continue the pipeline from /assessment-design?" (using AskUserQuestion with options: "Yes, continue" / "Start from a different skill" / "Re-run a completed skill")

If the user picks "Start from a different skill" or "Re-run a completed skill", ask which one using AskUserQuestion with the skill list as options.

Step 3: Execute Skills in Sequence

For each skill from the starting point onward:

1. Announce: "Starting /skill-name..."
2. Invoke the skill using the Skill tool with the skill name (e.g., skill: "needs-analysis")
3. The skill will run its full workflow including all AskUserQuestion interactions
4. When the skill completes (logs to timeline), execute Step 4 (Generate Course Dashboard) inline to refresh .idstack/exports/<course-slug>/index.html against the new per-skill report. This keeps the dashboard fresh if the designer pauses partway through.
5. Announce completion and move to next.

If the Skill tool is NOT available (e.g., on Codex CLI or any host without a primitive to invoke a sibling skill in-process), degrade gracefully: after announcing the next skill, print exactly what the user should type to invoke it (e.g., "Type $needs-analysis to run the next stage, then resume the pipeline by typing $pipeline again") and STOP. The user runs the skill themselves; the pipeline picks up on the next $pipeline invocation by re- reading the timeline and continuing from the new starting point. Generate the course dashboard (Step 4) before stopping so the partial-run dashboard is up to date.

Between skills, briefly announce the transition: "[skill-name] complete. Course dashboard refreshed. Moving to /next-skill..."

Step 4: Generate Course Dashboard

After each skill completes (or after the orchestrator finishes the run, including partial runs), assemble the per-skill HTML reports and the manifest into a single index.html course dashboard so the designer (and any stakeholder) can read the full picture in one place.

When this step runs:

• Inline after each skill completes during the orchestrator's main loop (Step 3, item 4) — so a partial pipeline still leaves a useful dashboard behind if the designer pauses.
• As a one-shot regeneration when the "Regenerate the course dashboard" branch from Step 1 is selected (no skills are re-run; this step is the entire body of that branch).

Prep. Compute the course slug and prepare the export folder:

_PROJECT_NAME=$(python3 -c "import json; print(json.load(open('.idstack/project.json')).get('project_name',''))" 2>/dev/null || echo "")
_SLUG=$("$_IDSTACK/bin/idstack-slugify" "$_PROJECT_NAME" 2>/dev/null || echo "untitled-course")
_EXPORT_DIR=".idstack/exports/$_SLUG"
mkdir -p "$_EXPORT_DIR/assets"
cp -f "$_IDSTACK/templates/assets/idstack.css" "$_EXPORT_DIR/assets/idstack.css"
echo "Dashboard: $_EXPORT_DIR/index.html"

Inputs. Read each per-skill HTML report file if it exists (they're already in $_EXPORT_DIR/):

• $_EXPORT_DIR/needs-analysis.html
• $_EXPORT_DIR/learning-objectives.html
• $_EXPORT_DIR/assessment-design.html
• $_EXPORT_DIR/course-builder.html
• $_EXPORT_DIR/course-quality-review.html
• $_EXPORT_DIR/accessibility-review.html
• $_EXPORT_DIR/red-team.html
• $_EXPORT_DIR/course-export.html (if present)
• $_EXPORT_DIR/course-import.html (if present)

Also read .idstack/project.json for project_name, scores (quality_review.overall_score, accessibility_review.score.overall, red_team_audit.confidence_score), and each section's report_path.

Output. Write $_EXPORT_DIR/index.html, following the structure of templates/index.html.tmpl. Customize:

• {{project_name}}: from the manifest top-level.
• {{pipeline_run_status}}: e.g., "complete · 8 of 8 skills" or "partial · 4 of 8 skills".
• Cross-cutting summary (replaces {{cross_cutting_summary_2_to_3_paragraphs}}): 2–3 paragraphs of cross-cutting synthesis. Designed to be read by a designer (or stakeholder) who hasn't yet opened the per-skill reports. Lead with the verdict, follow with the themes that recur across multiple skills, end with where to start.
• Readiness scoreboard — populate the four <div class="score verdict-{verdict}"> cards (Quality, Accessibility, Red-team confidence, Overall verdict). Verdict values: ready (green border) / issues (amber) / blocked (red) / pending (grey, when skill hasn't run yet). Thresholds match bin/idstack-status --readiness.
• Pipeline status table — one row per pipeline skill. status-done if the per-skill HTML report file exists, status-pending otherwise. signal column carries a 1-line skill-specific summary (e.g., for course-quality-review: overall_score/100; for red-team: confidence_score/100, N critical). Link the report cell to the relative HTML filename (e.g., <a href="needs-analysis.html">needs-analysis.html</a>). Add an optional course-import row when that report is present.
• Top cross-cutting issues (<ol> items): 3–5 highest-impact findings that appear in or affect multiple per-skill reports. Each item shows the source skill(s) and the evidence tier as a <cite class="citation"> element.
• LMS export artifacts — list every file present at the top level of $_EXPORT_DIR/ whose name matches a known LMS package (course-export.imscc, scorm-export.zip). If none, write: <p>No LMS packages produced yet. Run <code>/idstack:course-export</code> to package this course.</p>.
• Where to start — one paragraph. The single change (or 2–3 changes) that would address the largest number of cross-cutting concerns. Anchored to a specific finding id in a specific per-skill report so the designer can act on it (e.g., "Address assess-1 in assessment-design.html first…").

The dashboard is overwritten on every pipeline run — historical dashboards are not retained. The timeline at .idstack/timeline.jsonl carries the run history.

Step 5: Pipeline Complete

When all remaining skills have been executed:

• Announce: "Pipeline complete. Your course has been through all 8 stages."
• Confirm the dashboard path: "Course dashboard at .idstack/exports/<course-slug>/index.html — open it in any browser for the cross-cutting view; the per-skill HTML reports in the same folder carry the full detail. Zip the folder to hand it to a stakeholder."
• If /course-quality-review produced a score, show it.
• Remind the user they can re-run any skill individually if needed, and that re-running /idstack:pipeline regenerates the course dashboard.

Important Rules

• One skill at a time. Never run skills in parallel. Each skill needs the output of the previous one.
• Don't interfere. When a skill is running, let it drive. Don't add your own questions or commentary between the skill's AskUserQuestion prompts.
• Respect the user. If at any point the user says "stop", "pause", or "that's enough for now", stop the pipeline gracefully. Their progress is saved in timeline.jsonl and they can resume later with /idstack pipeline.
• No quality gate yet. In v2.0, skip logic is purely completion-based. Quality-gated skipping (only skip if score > threshold) is planned for a future version.