Agent skill

log-evidence

Record findings from completed offline human tasks (interviews, observations, outreach) back into the canvas. The re-entry point after /handoff.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/log-evidence

SKILL.md

Log Evidence Skill

The re-entry point after offline human work. Takes raw conversation notes, observations, or survey results and integrates them into the canvas with proper provenance.

When to Use

  • After completing a human task from canvas/human-tasks.yml
  • When the user returns from an offline conversation and has findings to record
  • When SessionStart reminds about pending human tasks and the user has completed them
  • When the user pastes conversation notes or interview summaries

Workflow

  1. Check pending tasks:

    • Read canvas/human-tasks.yml for pending_tasks
    • List them: "You have [N] pending human task(s): [objective summaries]"
    • Ask: "Which task did you complete? Or paste your notes and I'll match them."
  2. Guided evidence capture (if user doesn't have a filled template):

    • Who did you talk to? (role and context, not name -- privacy)
    • What did you learn? (open-ended first, let them tell the story)
    • Any direct quotes worth capturing?
    • Anything surprising or contradicting our current assumptions?
    • JTBD signals: functional job, emotional job, social job?
    • Any follow-up conversations needed?
  3. Classify the evidence on Gilad's ladder:

    • Single conversation -> anecdotal (0.3)
    • 2 conversations with consistent signals -> anecdotal (0.3), note convergence
    • 3+ triangulated conversations -> data-supported (0.5-0.6)
    • Explain the classification: "One conversation is anecdotal evidence. We'd need 2-3 more to call it data-supported."
  4. Update canvas provenance:

    • Identify the relevant canvas file and section (from the task's canvas_refs)
    • If the canvas entry has NO provenance object yet (early project), create one:
      yaml
      provenance:
        evidence_type: anecdotal  # single conversation
        evidence_sources:
          - "interview-YYYY-MM-DD-[role-descriptor]"
        source_classes:
          - external_human
        captured_at: "YYYY-MM-DDTHH:MM:SSZ"
        confidence: 0.3
      
    • If provenance already exists: add to evidence_sources and source_classes arrays
    • Update evidence_type if the new evidence strengthens it
    • Update confidence score with explicit reasoning
    • Update captured_at timestamp
  5. Update canvas/human-tasks.yml:

    • Move task from pending_tasks to completed_tasks
    • Record: completed_at, evidence_logged_to, key_findings, source_class: external_human

Task Cancellation

If the user reports a task couldn't be completed (contact unavailable, timing didn't work, etc.):

  1. Ask: "Should we cancel this task or reschedule it?"

  2. If cancel: move to completed_tasks with source_class: cancelled and a note explaining why

  3. If reschedule: update the task's objective or target_persona if needed, keep in pending_tasks

  4. Either way: "The evidence gap still exists. Consider /handoff to plan an alternative approach."

  5. Check for contradictions:

    • Compare findings against existing canvas data
    • If findings contradict assumptions: flag clearly
      • "This contradicts [canvas section / assumption]. The user said [X] but we assumed [Y]."
      • Suggest: "Consider running /devils-advocate to stress-test this assumption, or update the canvas with /canvas-update."
    • If findings support assumptions: note the confirmation
      • "This supports [canvas section]. Confidence for [item] can increase."
  6. Recalculate confidence:

    • Show before/after: "Diamond confidence: 0.45 -> 0.52 (added 1 external_human source)"
    • If this was the first external evidence: "First external human voice recorded. Evidence ratio improved from 0% to [X]%."
  7. Suggest next steps:

    • If more conversations needed: "One conversation is a start. Consider /handoff for 1-2 more to reach triangulation."
    • If enough evidence: "Evidence looks solid for /diamond-progress to attempt the next transition."
    • If contradictions found: "Before progressing, resolve the contradiction. Run /devils-advocate or revisit the canvas."

Canvas Output

  • Updates: relevant canvas file provenance (evidence_sources, source_classes, evidence_type, confidence)
  • Updates: canvas/human-tasks.yml (moves task to completed)
  • May update: canvas/opportunities.yml, canvas/user-needs.yml, canvas/jobs-to-be-done.yml depending on findings

Theory Citations

  • Torres (CDH): Triangulation requirement (3+ sources for data-supported)
  • Gilad (Evidence-Guided): Confidence ladder classification
  • Christensen (JTBD): Functional/emotional/social capture structure
  • Argyris (Double-Loop): Contradiction detection triggers assumption questioning

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