Retrospect Domain — Domain Insights Analysis
Analyze domain insights (WHAT/WHY learned) from captured sessions. Use when reviewing learnings, extracting patterns, analyzing decisions. Triggers include "retrospect domain", "domain analysis", "what did I learn", "session insights".
npx skills add github:pantheon-org/tekhne --skill specialized--retrospect-domainAnalyze captured sessions to extract domain learnings: what worked, decisions made, patterns emerged.
Arguments
- Domain (optional first arg):
technical|research| generic (default) - Timeframe:
--last Nd|--week|--month|--from DATE --to DATE| all
When to Use
Apply this skill when the user asks about:
- What was learned in sessions (“what did I learn?”, “session insights”)
- Extracting domain patterns or recurring decisions from a time window
- Surfacing anti-patterns or gotchas discovered during work
- Generating a domain knowledge summary across multiple sessions
When Not to Use
- Use
retrospect-collabinstead when the focus is on HOW the collaboration worked, not WHAT was learned. - Do not run this skill against zero session files; no sessions means no domain insights to extract.
Principles
- Extract, don’t invent: Base every insight on actual session content. NEVER fabricate learnings not present in the session data.
- Be specific: Reference actual conversations, tools used, and decisions made. ALWAYS name the session when citing evidence.
- Detect patterns: Look for themes across multiple sessions, not isolated observations.
- Domain-appropriate: Apply the domain framework matching the session type. Consider falling back to the generic framework only when domain content is ambiguous.
- Framework flexibility: Optionally adapt analysis headings when session content does not fit the standard framework questions.
Anti-Patterns
<!– BAD: “you learned to avoid N+1 queries” (invented) | GOOD: “session 2026-03-14 revealed an N+1 query in the reporting module — query was rewritten using eager loading” –>
- NEVER report a learning that is not evidenced in the session — WHY: fabricated insights give false confidence and may direct future work incorrectly
- NEVER apply the generic framework to clearly technical sessions — WHY: the Technical Domain framework surfaces six specific questions that generic analysis misses, reducing insight quality
- NEVER omit the Patterns Emerged section when reviewing more than two sessions — WHY: cross-session synthesis is the primary value-add of this skill; single-session summaries are not enough
- NEVER hardcode the output path — WHY: the
.retro/insights/location is project-configurable; use the configured path, not a hardcoded one
Steps
-
Load domain framework (if specified):
- Check for a
domain-{domain}.mdfile at the project-configurable domain frameworks location. - If missing: warn, use generic framework
Note: The domain frameworks location is project-configurable — point it to wherever your project stores domain reference files.
- Check for a
-
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 session files — extract conversation turns, tools used, git context
-
Analyze domain insights using framework:
- What new patterns/approaches discovered?
- What decisions were made and why?
- What worked well / what failed?
- What patterns recur across sessions?
-
Generate Start/Stop/Continue recommendations
-
Write insights to
.retro/insights/domain/{PERIOD}.md# Output path example (project-configurable) .retro/insights/domain/2026-03-01_to_2026-03-31.md -
Report: file path, suggest
/retrospect collabor/retrospect report
Usage Examples
Run for the last 7 days with the technical domain framework:
/retrospect domain technical --last 7d
Run for the current month with no domain filter (generic):
/retrospect domain --month
Run for a custom date range with the research framework:
/retrospect domain research --from 2026-03-01 --to 2026-03-31
Run and then follow up with collaboration analysis:
/retrospect domain technical --week
/retrospect collab --week
Report Section Format
The domain report follows this structure:
# Domain Retrospective: YYYY-MM-DD to YYYY-MM-DD
## Sessions Reviewed
- [List sessions with timestamps and brief summaries]
## Domain Learnings
### What I Learned
### What Worked Well
### What Didn't Work
### Decisions Made
### Patterns Emerged
## Start/Stop/Continue
## Action Items
## Metrics Summary
Gotchas
- The session filter script outputs a PERIOD header on line one; parse it separately from session file paths.
- Domain framework files may not exist for every domain. ALWAYS warn the user and fall back to generic rather than aborting.
- Consider including a short “Sessions Reviewed” list at the top of the report so the reader knows what data was used.
- 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-domain Reference
Domain Frameworks
Technical Domain
- What new patterns, APIs, or architectural approaches were discovered?
- What technical decisions were made and why?
- What implementation approaches worked well?
- What approaches failed or would be done differently?
- What edge cases or gotchas were encountered?
- What technical debt was uncovered or paid down?
Research Domain
Load from the project-configurable domain frameworks location (e.g. domain-research.md) if available.
- What hypotheses were tested?
- What methods were used?
- What insights emerged?
- What sources were most valuable?
Generic Framework
- What strategic insights emerged about the domain?
- What customer/user needs were clarified?
- What business decisions were made and their rationale?
- What trade-offs were considered?
- What assumptions were proven wrong?
Cross-Session Patterns
- Recurring technical patterns or anti-patterns
- Common decision-making themes
- Areas of consistent success or struggle
Report Template
# Domain Retrospective: YYYY-MM-DD to YYYY-MM-DD
## Sessions Reviewed
- [List sessions with timestamps and brief summaries]
## Domain Learnings
### What I Learned
[Key discoveries, new patterns, insights about the domain]
### What Worked Well
[Successful approaches, good decisions, effective strategies]
### What Didn't Work
[Failed approaches, mistakes, what would be done differently]
### Decisions Made
[Key decisions and their rationale, trade-offs considered]
### Patterns Emerged
[Recurring patterns noticed across sessions]
## Start/Stop/Continue
### Start (Begin Doing)
[New practices to adopt]
### Stop (Avoid)
[Anti-patterns to eliminate]
### Continue (Maintain)
[Effective practices to keep]
## Action Items
- [ ] [Specific actions based on insights]
## Metrics Summary
- **Sessions**: N (technical/business/mixed breakdown)
- **Period**: [start date] to [end date]
---
Generated: [timestamp]
Key Principles
- Extract, don’t invent: Base insights on actual session content
- Be specific: Reference actual conversations, tools used, decisions
- Detect patterns: Look for themes across multiple sessions
- Domain-appropriate: Use domain frameworks for deeper insights
- Framework flexibility: Adapt to session content type