Retrospect Collab — Collaboration Analysis
Analyze collaboration patterns (HOW) and compute metrics from captured sessions. Use when reviewing collaboration quality, analyzing human-AI interaction, computing session metrics. Triggers include "retrospect collab", "collaboration analysis", "session patterns", "how am I collaborating".
npx skills add github:pantheon-org/tekhne --skill specialized--retrospect-collabRole: Expert in collaboration analysis, agile retrospectives, human-AI interaction patterns, and cognitive skill development.
When to Use
Apply this skill when the user asks about:
- How they collaborate with AI (“how am I collaborating?”, “session patterns”)
- Collaboration quality review for a specific time window
- Computing impact metrics (Automation / Low-impact / High-impact breakdown)
- Longitudinal skill progression analysis across multiple sessions
When Not to Use
- Use
retrospect-domaininstead when the focus is on WHAT was learned, not HOW the collaboration worked. - Do not run this skill on fewer than one complete session file; partial session data produces unreliable scores.
Principles
- Evidence-only: Reference actual prompts and tool calls from sessions. NEVER invent patterns that are not supported by session data.
- Quantify always: Write “3 out of 8 sessions showed X”, not vague generalizations. ALWAYS state ratios, not adjectives.
- Impact targets: >60% high-impact augmentation, <20% automation. Surface the gap explicitly when targets are missed.
- Longitudinal view: Track skill evolution across sessions, not just a point-in-time snapshot.
Anti-Patterns
<!– BAD: report claims “user often accepted outputs without challenge” | GOOD: “user accepted AI output without follow-up in 6 of 9 exchanges in session 2026-03-14” –>
- NEVER fabricate session evidence — WHY: the entire value of this skill is grounded analysis; invented evidence erodes trust and makes the retrospective useless
- NEVER report a single session as a trend — WHY: one instance is an observation, not a pattern; patterns require recurrence across at least two sessions
- NEVER skip the impact categorization step — WHY: without Automation/Low/High classification the metrics summary is incomplete and the report template cannot be filled
- NEVER use a hardcoded output path — WHY: the
.retro/insights/location is project-configurable; assume it, don’t hardcode it
Steps
-
Filter sessions:
bash ${CLAUDE_PLUGIN_ROOT}/scripts/retrospect-load-sessions.sh $@First line:
PERIOD: YYYY-MM-DD_to_YYYY-MM-DD. Remaining: session paths.Note:
CLAUDE_PLUGIN_ROOTpoints to the script bundle location. The learnings output path (.retro/insights/) is project-configurable — set it to wherever your project stores retrospective outputs. -
Read sessions — extract:
user_prompts,tool_calls,duration_seconds,subagent_spawns, JSONL events -
Analyze technical effectiveness:
- Context Management: preparation, over/under-dump, progressive feeding
- Guidance: prompt clarity, exploration vs constraints balance
- Critical Thinking: user challenges, AI pushback, alternative-seeking
- Bias: over-trust, dismissal, confirmation bias, automation bias
-
Analyze cognitive posture:
- Intentionality: structured prompts vs trial-and-error, “why before how”
- Agency: custom construction vs template copy-paste, decision ownership
- Impact: categorize each session (Automation / Low-impact / High-impact augmentation)
- Progression: session 1→N sophistication, prompt evolution, strategic vs tactical
-
Generate Start/Stop/Continue with specific session examples
-
Write insights to
.retro/insights/collab/{PERIOD}.md# Output path example (project-configurable) .retro/insights/collab/2026-03-01_to_2026-03-31.md -
Report: file path, suggest
/retrospect reportfor aggregates
Usage Examples
Run for the last 7 days:
/retrospect collab --last 7d
Run for the current week:
/retrospect collab --week
Run for a custom date range:
/retrospect collab --from 2026-03-01 --to 2026-03-31
Run and then view aggregates:
/retrospect collab --month
/retrospect report
Metrics Summary Format
The Metrics Summary section uses this format:
## Metrics Summary
- **Sessions**: 8 | **Total prompts**: 64 (avg 8) | **Total tool calls**: 112 (avg 14)
- **Total duration**: 3h 20m (avg 25m/session) | **Subagents**: 4
- **Impact**: High 62% (>60% target ✓) | Low 25% | Auto 13% (<20% target ✓)
Gotchas
- The session filter script outputs a PERIOD header on the first line; everything after is a file path. Parse carefully — do not treat the PERIOD line as a session path.
duration_secondsmay be absent in older session files. Optionally skip duration metrics rather than erroring.- Consider providing a partial report if some session files are unreadable rather than aborting entirely.
- Run the session loader directly to test it:
./scripts/retrospect-load-sessions.sh --last 7d
References
- Reference — scoring rubrics, metric definitions, and report format
Explicit NEVER/WHY/consequence triplets with side-by-side bad/good examples. Generic warnings score low — concrete failure modes with real impact (security, correctness, performance) score high.
Runtime validation via eval scenarios proving the skill actually changes agent behaviour. Requires evals/ directory, instructions.json, ≥3 scenarios, and ≥80% instruction coverage in summary.json.
Appropriate rigidity for the skill type: strict rules for critical/security skills, balanced steps for process skills, flexible options for tool skills. Mismatched calibration (too rigid or too loose) reduces score.
Expert-only knowledge the AI couldn't derive on its own — domain-specific patterns, production gotchas, decision frameworks, and non-obvious tool behaviour. High scores mean <5% redundancy with what the model already knows.
A clear philosophy plus numbered step-by-step workflows with explicit entry/exit points. Should answer: what is the core principle, how to execute it, and when (and when not) to apply it.
Rich activation keywords and trigger scenarios in the description so the skill fires when needed. Best descriptions read like an exhaustive list of the tasks and concepts that should invoke this skill.
Copy-paste-ready code, complete runnable examples, real file paths and commands. Pseudocode and abstract descriptions score low — concrete, executable content scores high.
SKILL.md as a <100-line navigation hub pointing to focused references/ files. Content should be lazy-loaded on demand, not front-loaded. Includes actionable 'When to Use' conditions per reference.
Proper frontmatter, single-task focus, activation keywords, and cross-harness portability. Skills must work across 40+ agentic harnesses without hardcoded paths or agent-specific references.
Expert-only knowledge the AI couldn't derive on its own — domain-specific patterns, production gotchas, decision frameworks, and non-obvious tool behaviour. High scores mean <5% redundancy with what the model already knows.
A clear philosophy plus numbered step-by-step workflows with explicit entry/exit points. Should answer: what is the core principle, how to execute it, and when (and when not) to apply it.
Explicit NEVER/WHY/consequence triplets with side-by-side bad/good examples. Generic warnings score low — concrete failure modes with real impact (security, correctness, performance) score high.
Proper frontmatter, single-task focus, activation keywords, and cross-harness portability. Skills must work across 40+ agentic harnesses without hardcoded paths or agent-specific references.
SKILL.md as a <100-line navigation hub pointing to focused references/ files. Content should be lazy-loaded on demand, not front-loaded. Includes actionable 'When to Use' conditions per reference.
Appropriate rigidity for the skill type: strict rules for critical/security skills, balanced steps for process skills, flexible options for tool skills. Mismatched calibration (too rigid or too loose) reduces score.
Rich activation keywords and trigger scenarios in the description so the skill fires when needed. Best descriptions read like an exhaustive list of the tasks and concepts that should invoke this skill.
Copy-paste-ready code, complete runnable examples, real file paths and commands. Pseudocode and abstract descriptions score low — concrete, executable content scores high.
Runtime validation via eval scenarios proving the skill actually changes agent behaviour. Requires evals/ directory, instructions.json, ≥3 scenarios, and ≥80% instruction coverage in summary.json.
No eval scenarios available.
retrospect-collab Reference
Report Template
# Collaboration Retrospective: YYYY-MM-DD to YYYY-MM-DD
## Sessions Analyzed
- [List with prompts/tools counts]
## Part 1: Technical Effectiveness
### Context Management
**What worked well:** [Specific examples]
**What didn't work:** [Specific examples]
**Actions:** - [ ] [Specific improvements]
### Guidance & Direction
**Clarity wins:** [Effective prompt examples]
**What didn't work:** [Vague/problematic prompt examples]
**Actions:** - [ ] [Specific improvements]
### Critical Thinking
**Effective challenges:** [Productive pushback examples]
**Missed challenges:** [Where questioning would have helped]
**AI challenged me:** [Helpful AI pushback examples]
**Actions:** - [ ] [Specific improvements]
### Bias & Blindspots
**Biases noticed:** [Specific examples]
**Blindspots AI helped overcome:** [Examples]
**Blindspots AI introduced:** [Examples]
**Actions:** - [ ] [Specific improvements]
---
## Part 2: Cognitive Posture & Professional Development
### Intentionality & Learning Posture
**What worked well:** [Intentional design, concept understanding examples]
**What didn't work:** [Copy-paste, trial-and-error examples]
**Actions:** - [ ] [Specific improvements]
### Agency & Ownership
**Evidence of agency:** [Decision ownership, custom prompts, self-correction]
**Passive patterns:** [Template following, AI dependency]
**Actions:** - [ ] [Specific improvements]
### Impact Categorization
**Breakdown:**
- **High-impact** (Z%): [Key sessions with examples]
- **Low-impact** (Y%): [Sessions with patterns]
- **Automation** (X%): [Sessions with patterns]
**Triggers for high-impact:** [What preparation/framing led to transformative sessions?]
**Regression triggers:** [What caused drops? Time pressure? Unclear goals?]
### Skill Progression (Longitudinal)
**Evidence of growth:** [Session 1→N comparisons]
**Stagnation patterns:** [Repeating mistakes]
**Prompt evolution:** [Simple → Structured → Advanced]
---
## Start/Stop/Continue
### Start
**Technical:** [New collaboration practices]
**Cognitive:** [New learning/agency practices]
### Stop
**Technical:** [Anti-patterns]
**Cognitive:** [Dependency patterns]
### Continue
**Technical:** [Effective patterns]
**Cognitive:** [Effective learning patterns]
---
## Metrics Summary
- **Sessions**: N | **Total prompts**: X (avg Y) | **Total tool calls**: X (avg Y)
- **Total duration**: Xh Ym (avg Ys/session) | **Subagents**: X
- **Impact**: High Z% (>60% target) | Low Y% (20-30%) | Auto X% (<20%)
## Action Items
**Priority 1 (Technical):** [Specific improvements]
**Priority 2 (Cognitive):** [Specific improvements]
Technical Effectiveness Analysis Questions
Context Management:
- How was context prepared before sessions?
- Evidence of context over-dump (many reads without using info)?
- Evidence of context under-dump (repeated clarification questions)?
- Use of CLAUDE.md, specs, structured context?
- Progressive feeding vs all-at-once?
Guidance & Direction:
- Clarity of initial prompts (specific vs vague)
- Balance of exploration vs constraints
- Over-constraining vs under-constraining patterns
Critical Thinking:
- When did user challenge AI outputs?
- When should user have challenged but didn’t?
- When did AI challenge user assumptions?
- Pattern of accepting first solution vs requesting alternatives
Bias & Blindspots:
- Over-trust: accepting without verification
- Dismissal: ignoring helpful suggestions
- Confirmation bias: only accepting matching outputs
- Automation bias: preferring AI solutions over manual review
Impact Indicators
Automation: Repetitive tasks, simple summaries, copy-paste prompts, could be a script
Low-impact augmentation: Basic Q&A, incremental improvements, standard templates, surface-level
High-impact augmentation: Transformative insights, complex problem-solving, custom prompts, professional skill amplification, novel approaches, 6-block structured prompts, capability-building
Cognitive Posture Analysis
Intentionality signals: Structured prompts, clear objectives, “why before how”, concept understanding, reusable patterns
Agency signals: Custom prompt construction, template adaptation, decision ownership, self-correction, tool mastery progression
Progression tracking: Prompt complexity evolution, concept mastery vs memorization, strategic vs tactical usage, sustainable growth vs beginner’s luck