productivity/reflect/skills/reflect/SKILL.md
Mid-conversation reflection skill that pauses execution and zooms out from detail-mode to honestly reassess direction, assumptions, and bias. Use when the user says 'reflect', 'take a step back', 'step back', 'zoom out', 'are we missing something', 'bigger picture', 'sanity check this', 'are we on track', 'are we overthinking this', 'forest for the trees', or any variation signaling intent to break out of detail-mode and reassess. Also trigger when the conversation has gone deep on implementation details without strategic check-in, or when the user shows signs of being stuck — that's often a signal the framing needs a reset, not more detail work. Intentionally low-intake: runs the 5-dimension analysis immediately when prior context is rich enough; asks one forcing clarifier only when invocation context is too thin to reassess from.
npx skillsauth add alirezarezvani/claude-skills reflectInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Portability: Pure-reasoning skill. No external tools required. Works in Claude Code CLI + Claude.ai web natively. Most portable in the v2 collection.
When invoked mid-conversation, this skill pauses execution and produces a frank reassessment of where the conversation has been heading. Output is flowing analysis (no headers, conversational tone) covering macro perspective, gap analysis, reflective inquiry, bias check, and contextual alignment. The skill ends with a clear directional recommendation: continue, pivot, or pause to answer a specific question.
Explicit phrases:
Implicit signals (no phrase needed):
When you detect an implicit trigger, don't auto-invoke — ask the user if they want to step back. Implicit signals are a prompt to OFFER reflection, not to unilaterally run it.
Halt the current thread. Don't continue execution of the in-progress task. Reflection is a pause, not a side-quest.
This matters because:
This skill is intentionally low-intake — most invocations should run the 5-dimension analysis immediately without questions. The grill-me discipline applies only when the invocation is ambiguous (e.g., user pastes "step back" at the start of a fresh conversation with no prior context to reassess).
What specifically should I reassess? Pick one:
- The goal — are we solving the right problem?
- The approach — is the path we're on the best one?
- The assumptions — what are we taking for granted?
- All of the above (default if you have time)
Why I'm asking: I'm seeing limited prior context to reassess, so I want to focus the reflection rather than guess. If you'd rather I do all three, that's fine — say so.
Forcing choice with default. Asked only when context is genuinely thin; otherwise skip and run the full analysis on existing conversation.
Stop condition: One question max. If the user invokes mid-conversation with normal context, no questions are asked — the skill runs directly.
Re-read the full conversation from the original goal forward — not just recent turns. The discipline that distinguishes real reflection from local-context summary.
Anchor with specific evidence: "At turn 3 the goal was X; by turn 12 we're working on Y. Is Y a productive narrowing of X, or a drift away?"
Five biases — recognize each through specific conversation patterns:
| Bias | Recognition cue | |---|---| | Confirmation bias | Evidence cited only supports the working hypothesis; counter-evidence absent or dismissed | | Sunk cost fallacy | "We've already invested X" / "we're far enough in to..." instead of fresh cost/benefit | | Anchoring | Stuck on first option mentioned; new options compared against it rather than evaluated independently | | Complexity bias | Adding features / steps / safeguards without specific justification for each | | Recency bias | Over-weighting last few turns; older but important context being ignored |
For each detected bias: name it, cite the specific evidence, suggest a corrective move.
See references/cognitive_bias_canon.md for the full canon.
The skill must produce:
See references/honest_output_discipline.md for the anti-manufactured-problems framing.
Every run ends with one of three directional recommendations:
| Recommendation | When | Format | |---|---|---| | Continue | Path is solid | "Continue. {specific reasoning for why}." | | Pivot to {X} | Drift has occurred OR better path surfaced | "Pivot toward {X}, away from {what to drop}. {specific evidence}." | | Pause for {Q} | A specific question needs answering before continuing | "Pause for {Q}. Without answering this, the next step risks {specific cost}." |
The closing is always specific — never "you should think more about this" or "consider your options."
| Situation | Behavior | |---|---| | Conversation is very short (no real context to reassess) | Acknowledge limitation, ask user what they want reassessed (Q1 fires) | | Current direction is genuinely solid | State this clearly with reasoning; don't manufacture problems | | User invokes mid-task with no clear question | Default to macro perspective + bias check; offer to dig deeper | | Implicit trigger seems possible but unclear | Don't invoke proactively; ask user if they want to step back |
| Script | Role |
|---|---|
| scripts/bias_pattern_detector.py | Scan conversation text for patterns indicative of each of the 5 biases |
| scripts/conversation_depth_analyzer.py | Count turns + detect implicit-trigger signals (10+ detail turns, frustration markers) |
| scripts/directional_recommendation_validator.py | Verify output ends with Continue / Pivot / Pause + specific reasoning |
references/cognitive_bias_canon.md — 5 biases + recognition cues (7+ sources)references/honest_output_discipline.md — anti-manufactured-problems framing (7+ sources)references/conversation_reflection_practice.md — Schön reflective-practice canon (7+ sources)Version: 1.0.0
Source spec: megaprompts/02-reflect-megaprompt.md
Build pattern: Path B (direct conversion). Productivity light-prompt-flow sibling of capture.
tools
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
tools
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
development
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
development
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.