p-daily-reflection

Daily Reflection

Setup

Locate this skill's directory (the folder containing this SKILL.md), then run the resolver script from there:

python <skill-dir>/scripts/skillctx-resolve.py resolve p-daily-reflection

The resolver outputs each binding as key: value (one per line). Substitute each {binding_key} placeholder below with the resolved value.

If any values are missing or the user requests changes, use:

python <skill-dir>/scripts/skillctx-resolve.py set p-daily-reflection <key> <value>

Reflect on past work and iterate to improve. Gathers activity from Claude sessions, GitHub, Slack, and Fireflies, then analyzes through reflection lenses to produce a journal with actionable improvements. Reads previous reflections to track follow-through and detect recurring patterns. Updates persistent memory so learnings feed back into future sessions.

Arguments

• --days N — look back N days (default: 1)
• --since YYYY-MM-DD — reflect from a specific date
• --skip-memory — generate journal only, don't update .claude/ memory

Steps

Step 0: Setup

Resolve all inputs into concrete variables before any other step runs.

Date range:

1. If --since YYYY-MM-DD provided: REFLECT_START = <that date>, REFLECT_END = today
2. If --days N provided: REFLECT_START = today minus N days, REFLECT_END = today
3. Default: REFLECT_START = today, REFLECT_END = today (last 24 hours)

Set REFLECT_START and REFLECT_END as YYYY-MM-DD strings. Derive REFLECT_END_PLUS_1 (one day after REFLECT_END, used in Slack queries).

Flags:

• SKIP_MEMORY = true if --skip-memory was passed, otherwise false

All subsequent steps use these variables. Do not re-derive them.

Step 1: Read Previous State

Before gathering new data, read two files:

1. Previous reflection journal: Find the most recent reflection.md in {notebook_daily_dir}. Scan date-stamped directories in reverse order to find the last one that contains a reflection.md. Extract:

   • Action items (checked and unchecked)
   • Key themes and patterns noted

2. Memory file: Read {reflections_memory_path} (if it exists). This contains confirmed learnings from past reflections.

Store both for use in Step 3 (analysis) and Step 4 (output).

Step 2: Gather Activity (Parallel)

Launch four Agent tool calls in parallel — one per data source. Each agent returns structured observations. If a source is unavailable (e.g., no Fireflies server), the agent must return the standard empty-result envelope:

{"source": "<name>", "status": "unavailable", "observations": []}

2a: Claude Code History

→ See references/agent-gather-claude-history.md

2b: GitHub Activity

→ See references/agent-gather-github.md

2c: Slack Activity

→ See references/agent-gather-slack.md

2d: Fireflies Meetings

→ See references/agent-gather-fireflies.md

Step 2.5: Aggregate

After all four agents complete, collect and consolidate their outputs before analysis.

1. Collect all outputs into a unified structure:

   {
     "claude_history": <2a output or empty-result envelope>,
     "github":         <2b output or empty-result envelope>,
     "slack":          <2c output or empty-result envelope>,
     "fireflies":      <2d output or empty-result envelope>
   }
   

   Any agent that returned {"source": "...", "status": "unavailable", "observations": []} is recorded as-is — do not attempt to re-fetch.

2. Deduplicate cross-source events by primary key:

   • PRs: use the PR URL as the primary key — if the same PR appears in both GitHub data and Slack messages, merge into one record.
   • Commits: use the commit SHA as the primary key — if the same commit appears in both GitHub and Claude history project references, keep one entry.
   • Meetings: use the Fireflies meeting ID — if a meeting is referenced in both Fireflies and Slack, merge.

3. Note missing sources: For any source with "status": "unavailable", record it in the aggregate so downstream lenses can acknowledge the gap rather than silently skip it.

Proceed to Step 3 only after this aggregate is complete.

Step 3: Analyze Through Reflection Lenses

After all agents return, analyze the combined data through five lenses. For each lens, produce 0–3 concrete observations. If a lens has no meaningful signal, skip it entirely — do not force insights.

Lens 1: Skill Health

Using slash command and skill invocation data from Step 2a:

• Which skills were used and how often?
• Any skills abandoned mid-use (session shows /skill-name but no completion)?
• Any skills that errored (error entries shortly after a slash command)?
• Skills not used during this period but used in previous reflections — are they forgotten or no longer needed?

Lens 2: Recurring Friction

Cross-reference all four data sources to find repeated pain points:

• Same error appearing in multiple Claude sessions
• Repeated manual steps that could be automated (same sequence of commands across sessions)
• Knowledge-gap questions on Slack ("how do I…", "where is…") that indicate missing docs or tooling
• Blockers raised in Fireflies meetings that reappear in later meetings

Compare against the previous reflection from Step 1. If a friction point was noted before, flag it as recurring — it needs escalation or a different approach.

Lens 3: Decision Quality

Cross-reference Slack and Fireflies with GitHub to check follow-through:

• Meeting commitments ("I'll do X by Friday") — did corresponding commits or PRs appear?
• Slack decisions ("let's go with approach B") — is there evidence of execution?
• Scope changes — did the actual work (commits) match the stated plan, or did scope shift?

Lens 4: Learning Patterns

From Claude Code history corrections and debugging sequences:

• New techniques tried — first-time use of a tool, library, or approach
• Corrections made — cases where the user suggested X, then corrected to Y (indicates a learning moment or a tool gap)
• Multi-step debugging breakthroughs — long debugging sessions that eventually resolved (what was the key insight?)

Lens 5: Follow-Through

Using the previous reflection's action items from Step 1:

• Addressed — search commits, PRs, and Slack messages for evidence that the action item was completed
• Outstanding — action items with no evidence of progress
• Meeting commitments not acted on — promises from Fireflies that have no matching activity in GitHub or Slack

Step 4: Generate Journal Entry

Write the reflection journal to {notebook_daily_dir}/YYYY-MM-DD/reflection.md (create the directory if needed).

Format:

# Reflection — YYYY-MM-DD

## What happened
Brief narrative (3-5 sentences) of the day's work across all projects. Synthesize from all sources — don't list sources. Write as if journaling, not reporting.

## What I learned
- Bullet list from Lens 4 (Learning Patterns)
- Include new techniques, debugging insights, corrections

## What went well
- Effective patterns observed
- Good decisions that played out well
- Smooth workflows, skills that worked great

## What didn't
- Friction points from Lens 2
- Failed approaches, time sinks
- Scope mismatches from Lens 3

## Skill health
- Observations from Lens 1 (only include if meaningful signal)

## Action items
- [ ] Concrete, actionable improvements (from all lenses)
- [ ] Each item should be doable in a single session
- [ ] Maximum 5 items — prioritize by impact

## Follow-through
- [x] Items from previous reflection that were completed (Lens 5)
- [ ] Items still outstanding — carried forward with context

Rules:

• Never mention "Claude", "Claude Code", "sessions", "MCP", or implementation details — source-agnostic prose
• Don't force sections — skip any with no meaningful content
• Action items must be specific ("refactor X in file Y") not vague ("improve code quality")
• If reflecting over multiple days (--days N), organize "What happened" chronologically

Step 5: Update Memory

Unless SKIP_MEMORY is true (set in Step 0), update persistent memory.

Memory file: {reflections_memory_path}

What to write: Only learnings meeting the confirmation threshold:

• Pattern observed 2+ times across reflections (check previous entries)
• Explicit correction that should never be repeated
• Skill improvement that was validated

Entry format:

### YYYY-MM-DD: [Brief title]
- [One-line learning or pattern]

Maintenance rules:

• Cap at ~100 lines. Remove oldest irrelevant entries when exceeded.
• Deduplicate — if new learning is more specific version of existing, replace old.
• Don't write speculative observations. Single-occurrence patterns stay in journal only.

First-run setup:

1. If reflections.md doesn't exist, create with header: # Reflection Learnings\n\nConfirmed patterns and learnings from daily reflections.\n
2. If MEMORY.md exists in same dir, append reference: - See reflections.md for accumulated learnings from daily reflections
3. If MEMORY.md doesn't exist, create with just that reference line.

Announce completion:

• "Reflection saved to {notebook_daily_dir}/YYYY-MM-DD/reflection.md"
• "N new learnings added to memory" (or "No new learnings met the confirmation threshold")
• List the action items generated