windags-skill-cdm-elicitation

license	BUSL-1.1
name	windags-skill-cdm-elicitation
description	Runs CDM (Critical Decision Method) interviews to elicit the perceptual layer missing from science-only SKILL.md files. Activate when: Looking Back Q3 fires with scope≠narrow, a pattern_crystallization event is detected, a skill underperforms (expectancy_violation), or a novel task has no catalog match. Produces structured SKILL.md v2 metadata: recognition-cues, expectancies, decision-cues, adaptive-workarounds. NOT for: general Q&A, skill selection, DAG execution.
allowed-tools	Read,Write,Edit
category	Agent & Orchestration
tags	["skill-creation","cdm","elicitation","knowledge-capture","cci"]
pairs-with	[{"skill":"skill-architect","reason":"CDM output feeds skill-architect to produce the final SKILL.md draft"},{"skill":"windags-looking-back","reason":"Looking Back Q3 fires this elicitation; Q4 feeds taxonomy updates"}]
io-contract	{"kind":"structured","outputSchema":"./schemas/output.json"}
metadata	{"recognition-cues":["Looking Back Q3 returns q3_generalizable=true with scope=medium or broad","Session log shows 3+ consecutive failures on same subtask then success","A skill was injected but confidence score < 0.4 after execution","Task completes with BM25 top-score below 0.35 (no catalog match)"],"expectancies":["Interview produces at least 3 decision-cue entries mapping branch → cue → indicates","Recognition-cues describe the task description, not the task category","Adaptive-workarounds capture at least one departure from the documented procedure","Output maps cleanly to SKILL.md v2 metadata fields with no ambiguity"],"decision-cues":[{"branch":"Select pattern_crystallization interview (fail→succeed)","cue":"Session shows N failures then success on same subtask within one session","indicates":"Expert knowledge just crystallized — hot cognition, interview NOW"},{"branch":"Select pattern_extension interview (novel success)","cue":"Task completed with high confidence AND BM25 top-score below 0.35","indicates":"New pattern not in catalog — extract recognition boundary via contrast"},{"branch":"Select expectancy_violation interview (skill underperformed)","cue":"Skill was injected AND confidence < 0.4 AND which_expectancy is known","indicates":"Skill's causal model is wrong or its NOT-FOR boundary needs extension"}],"adaptive-workarounds":["When the session expert (LLM) says 'I don't know why it worked' — use the timeline probe before the decision-point probe, the sequence reveals the cue","When interview produces abstract answers — ask 'What does the task description look like when you recognize this?' not 'When do you use this skill?'"],"execution-pattern":"sequential","needs-cdm":false}

WinDAGs Skill CDM Elicitation

Runs Critical Decision Method interviews to extract the perceptual layer that makes skills expert-level rather than just procedural (science-only).

When to Activate

Activate on:

• "Looking Back Q3 fired" / "Q3 scope=broad" / "Q3 scope=medium"
• "pattern crystallization" / "skill needs CDM" / "elicitation interview"
• "skill underperformed" / "expectancy violation" / "which expectancy failed"
• needs-cdm: true in any skill's metadata

NOT for:

• General skill writing (use skill-architect)
• Post-execution quality review (use windags-looking-back)
• Skill selection during DAG planning (use skill-selector)

Three Interview Protocols

Choose based on the trigger type. Each targets a different knowledge layer.

---

Protocol A: Pattern Crystallization (fail→succeed)

When: 3+ consecutive failures then success on the same subtask, within one session.

Why this protocol: Hot cognition. The expert (LLM session) just solved a hard problem. The solution path is maximally articulable RIGHT NOW. This is the moment Klein calls "crystallization" — a new entry is being added to the pattern library.

Probe sequence:

1. Timeline reconstruction (extracts the contrast cases automatically):

   "Walk me through the attempts that failed. What did you try first? Then what?"

2. Switch-point probe (extracts the key decision cue):

   "At attempt [N-1], what changed that made you try something different? What were you noticing or observing at that point?"

3. What-if probe (extracts the implicit prerequisite — the thing that would have let you succeed on attempt 1):

   "If you had known [X] from the start, would attempt 1 have worked? What is X?"

4. Cue inventory probe (extracts the recognition signal):

   "What does the task description look like — what are you reading or detecting — when you know this approach is the right one?"

5. Workaround probe (extracts the art layer — what wasn't in any existing skill):

   "Was there anything you did that you wouldn't find in a standard guide? Any departure from the usual approach?"

Output mapping:

• Timeline → worked-example section in SKILL.md body
• Switch-point + What-if → decision-cues entry (branch, cue, indicates)
• Cue inventory → recognition-cues list
• Workaround → adaptive-workarounds list
• What the agent expected → expectancies list

---

Protocol B: Pattern Extension (novel success, no catalog match)

When: Task completed successfully AND BM25 top-score below 0.35 (no close match in catalog).

Why this protocol: A new domain or technique was used that the catalog doesn't cover. The goal is to extract the DISCRIMINATION BOUNDARY — what makes this skill different from the adjacent ones it might be confused with.

Probe sequence:

1. Domain description (extracts what category this skill belongs to):

   "Describe what kind of task you just completed in one sentence, as if naming a job title."

2. Contrast probe (extracts the recognition boundary — what makes this skill unique):

   "What's the closest existing skill or approach this could be confused with? How would you know THIS situation needs this skill rather than that one?"

3. Anti-pattern probe (extracts the failure modes that aren't obvious):

   "What would a naive approach do here that this approach avoids? What's the canonical mistake?"

4. Cue inventory (as in Protocol A, probe 4):

   "What does the task description look like when this skill applies?"

5. Scope probe (helps the auditor classify this as narrow/medium/broad):

   "Can you describe 3 other tasks where the same approach would work? And 2 tasks that LOOK similar but where it wouldn't apply?"

Output mapping:

• Domain description → name and category in frontmatter
• Contrast → NOT for section + at least 2 decision-cues entries
• Anti-pattern → Failure Modes section in body
• Cue inventory → recognition-cues list
• Scope → determines whether to create new skill or extend existing one

---

Protocol C: Expectancy Violation (skill underperformed)

When: Skill was injected AND confidence < 0.4 AND which_expectancy is known from the trigger event.

Why this protocol: The skill's causal model is wrong. Either the NOT-FOR boundary is too narrow, the expectancies are incorrect, or the causal chain in a Decision Point is broken. This is a surgical update, not a full skill rewrite.

Probe sequence:

1. Violation reconstruction (extracts exactly what failed):

   "The skill said [expectancy text]. What actually happened instead?"

2. Constraint probe (extracts the missing L1 constraint):

   "What was true about this situation that the skill's expectancy didn't account for?"

3. Discrimination probe (determines if NOT-FOR needs extending):

   "Would this situation have been excluded by a more precise NOT-FOR clause? How would you describe the situation type that the skill doesn't apply to?"

4. Causal probe (extracts why the skill's causal model was wrong):

   "Why did the documented approach fail here? What's the mechanism?"

Output mapping:

• Violation reconstruction → update existing expectancy entry OR remove incorrect one
• Constraint probe → new decision-cues entry OR update existing Decision Point branch
• Discrimination → extend NOT for section in skill body
• Causal probe → update or add Failure Modes entry

---

Producing SKILL.md v2 Metadata

After running the appropriate protocol, structure the output as:

metadata:
  recognition-cues:
    - "[specific situation description — what the task looks like, not the category name]"
    - "[second distinct recognition signal]"
  expectancies:
    - "[what should happen within the session if the skill is working]"
    - "[observable outcome that confirms correct application]"
  decision-cues:
    - branch: "[which Decision Point branch this gates]"
      cue: "[the textual/contextual signal that triggers this branch]"
      indicates: "[what the signal means about the situation]"
    - branch: "[second branch]"
      cue: "[second signal]"
      indicates: "[what it means]"
    - branch: "[third branch — minimum 3 for full CCI coverage]"
      cue: "[third signal]"
      indicates: "[what it means]"
  adaptive-workarounds:
    - "[departure from procedure that experts use in edge cases]"
  execution-pattern: sequential | loop | monitor-and-react
  needs-cdm: false

Quality Gates

• Interview followed the protocol for the trigger type (A/B/C), not a generic Q&A
• Recognition-cues describe task DESCRIPTIONS, not abstract category labels
• Minimum 3 decision-cue entries produced (below 3 = still science-only-risk)
• At least 1 adaptive-workaround captured (the art layer, not the procedure)
• Expectancies describe OBSERVABLE OUTCOMES within the session, not abstract goals
• Output validated against SKILL.md v2 schema before writing to file
• needs-cdm set to false after successful elicitation
• skill-architect reviewed the draft before committing

NOT-FOR Boundaries

Do NOT use this skill for:

• Writing skill BODIES (use skill-architect for prose sections)
• Evaluating skill quality scores (use windags-looking-back)
• Selecting skills for a DAG node (use skill-selector)
• Conducting general UX research or customer discovery
• Running CDM on non-skill domains (use cdm-interviewer)