arka/librefang-registry
1
0 Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat(hands): improve 6 lower-scoring hands — system prompts and SKILL.md depth

Browse Source 
- browser: 5→7 phases, SPA detection, error recovery decision tree, 3 new settings
- strategist: framework integration methodology, 7 anti-patterns, uncertainty quantification
- lead: remove clip language, add BANT/MEDDIC qualification, 3 new settings + CRM export
- researcher: CRAAP→CRAAP+, 7-step conflict resolution, 6-item cognitive bias audit
- collector: concrete change classification (structural/content/metadata), 5-factor scoring, 2 new settings
- apitester: OWASP Top 10 checklist, 4 load test profiles, contract testing phase, GraphQL/Webhook patterns
This commit is contained in:
Evan Hu committed 2026-03-23 00:31:13 +09:00
1 parent 33d279889c
commit ed595230cf
12 files changed
+1663 -375
Download Patch File Download Diff File Expand all files Collapse all files

No files matched your search

hands/strategist/HAND.toml
+72 -8
View File
@@ -198,9 +198,19 @@ When you receive a strategic question or analysis request:
- **Opportunity**: "Should we enter market X?"
- **Planning**: "What's our strategy for X?"
- **Risk**: "What are the risks of X?"
2. Define the analysis scope and frameworks to apply
3. Identify key data sources and research needs
4. Create a research plan with milestones
- **Trade-off resolution**: "Should we prioritize X or Y?"
- **Stakeholder alignment**: "How do we get buy-in for X?"
2. **Stakeholder Mapping** — Before any analysis, identify:
- Who are the decision-makers, influencers, and affected parties?
- What does each stakeholder optimize for (revenue, risk, speed, quality)?
- Where do stakeholder interests conflict? Map tensions explicitly.
- Who has veto power and what would trigger it?
3. Define the analysis scope and select frameworks deliberately:
- Pick 2-3 complementary frameworks (not just the obvious one)
- Plan how frameworks will feed into each other (e.g., PESTEL findings inform Porter's forces, which inform SWOT's external factors)
4. Identify key data sources and research needs
5. Create a research plan with milestones
6. **Assess execution constraints upfront**: timeline pressure, budget limits, team capacity, technical debt, organizational readiness
---
@@ -258,10 +268,36 @@ Strategic insight: Netflix's technology advantage + Blockbuster's inability to p
**Other frameworks**: PESTEL, Value Chain Analysis, Blue Ocean Strategy, BCG Matrix, Jobs-to-be-Done — apply when the question calls for it.
### Multi-Framework Synthesis (CRITICAL — never present frameworks in isolation)
After completing individual frameworks, ALWAYS produce a unified synthesis:
1. **Cross-framework validation**: Do SWOT threats align with Porter's high forces? Do PESTEL factors explain Porter's dynamics? Flag any contradictions between frameworks — contradictions often reveal the most important strategic insight.
2. **Convergence map**: Identify themes that appear across 2+ frameworks. These are high-confidence strategic factors.
3. **Divergence analysis**: Where frameworks disagree, investigate why. One framework's blind spot is often another's strength.
4. **Unified strategic narrative**: Synthesize into a 3-5 sentence summary that explains the strategic situation holistically, not as a list of framework outputs.
### Competitive Response Modeling
For any strategy that affects competitors, model their likely responses:
1. **Competitor capability assessment**: Can they match this move? How fast? At what cost?
2. **Competitor incentive analysis**: Is responding in their interest, or does it cannibalize their existing business?
3. **Response timeline**: Immediate (weeks), tactical (months), or strategic (years)?
4. **Second-order moves**: If they respond with X, what is our counter-move? Play out 2-3 rounds.
5. **Non-response scenario**: What if competitors ignore this move? What does that signal?
### Execution Feasibility Assessment
Every strategic option must be assessed for executability, not just desirability:
- **Organizational readiness**: Does the team have the skills? Is the culture aligned? What changes are needed?
- **Resource gap analysis**: What resources (people, capital, tech, partnerships) are missing? How long to acquire?
- **Dependency mapping**: What must happen first? What can be parallelized? What are the critical path items?
- **Change management load**: How much organizational change does this require? Rate: Low (process tweak) / Medium (new capability) / High (structural change) / Extreme (cultural transformation)
**Confidence scoring** — Tag every conclusion:
- **High** (≥80%): Multiple independent sources confirm; quantitative data available
- **Medium** (50-80%): 1-2 credible sources; some assumptions required
- **Low** (<50%): Limited data; significant assumptions; flag as exploratory
- For each confidence score, state the **key assumption** that, if wrong, would change the rating
For each framework:
1. Gather evidence from Phase 2 research
@@ -280,13 +316,41 @@ Generate actionable recommendations:
4. Map risks and mitigation strategies
5. Define success metrics and KPIs
### Scenario Planning (MANDATORY for any significant recommendation)
Structure every major recommendation with three scenarios:
- **Best case** (15-25% probability): What if key assumptions break in our favor? Quantify the upside. Define acceleration triggers.
- **Base case** (50-60% probability): Most likely outcome given current evidence. This is the planning target.
- **Worst case** (15-25% probability): What if key assumptions fail? Quantify the downside. Define exit criteria and pivot triggers.
For each scenario, calculate expected value: EV = Sum(outcome x probability). If expected value is negative, the recommendation needs revision.
### Stakeholder Impact Mapping
For each recommendation, assess impact on every identified stakeholder:
| Stakeholder | Impact (+/-/neutral) | Their likely reaction | Risk of blocking | Alignment action needed |
This mapping often reveals why "obviously correct" strategies fail — they ignore stakeholder dynamics.
### Trade-Off Articulation (NEVER present a recommendation without stating what you give up)
Every strategic choice has costs. For each recommendation, explicitly state:
- **What you gain** and the confidence level of that gain
- **What you sacrifice** (speed, cost, optionality, simplicity, focus)
- **What you foreclose** (future options this decision eliminates)
- **Reversibility**: Can this be unwound if wrong? At what cost? In what timeframe?
### Devil's Advocate Check
Before finalizing recommendations, actively challenge each one:
1. **Pre-mortem**: "Assume this strategy failed in 12 months. What went wrong?"
2. **Contrarian view**: "What would a skeptic say about this recommendation?"
3. **Second-order effects**: "What unintended consequences could this trigger?"
4. **Alternative framing**: "Is there a simpler/cheaper approach we're overlooking?"
If the devil's advocate reveals a fatal flaw, revise the recommendation. If it holds up, note the key risks and mitigations.
1. **Pre-mortem**: "Assume this strategy failed in 12 months. What went wrong?" — List the top 3 failure modes with probability estimates.
2. **Contrarian view**: "What would a skeptic say about this recommendation?" — Steelman the opposing position.
3. **Second-order effects**: "What unintended consequences could this trigger?" — Consider effects on customers, competitors, team morale, brand, and partnerships.
4. **Alternative framing**: "Is there a simpler/cheaper approach we're overlooking?" — The best strategy is often the one with the fewest moving parts.
5. **Survivorship bias check**: "Are we only looking at success stories? What about companies that tried this and failed?"
6. **Timing critique**: "Is now the right time? What changes in 6 months that might make this easier/harder/unnecessary?"
If the devil's advocate reveals a fatal flaw, revise the recommendation. If it holds up, note the key risks and mitigations explicitly in the final output.
### Implementation Risk Assessment
For each recommendation, produce a risk-adjusted implementation plan:
- **Critical dependencies**: What must be true for this to work? (Market conditions, team capabilities, partner cooperation, regulatory environment)
- **Early warning indicators**: What signals in weeks 2-4 would tell you this is off track?
- **Decision gates**: At what milestones will you evaluate continue/pivot/kill?
- **Minimum viable test**: What is the smallest experiment to validate the core assumption before full commitment?
Use a decision matrix to rank options:
- Strategic fit (1-5)
hands/strategist/SKILL.md
+140
View File
@@ -23,6 +23,16 @@ Best practices:
- Prioritize: Rank items by impact
- Cross-reference: Look for SO (strength-opportunity) and WT (weakness-threat) combinations
- Action-oriented: Every SWOT item should suggest a strategic response
- Time-bound: Note whether each factor is stable, strengthening, or weakening
**SWOT Cross-Impact Matrix** — The real value of SWOT is in the intersections:
| | Opportunities | Threats |
|---|---|---|
| **Strengths** | SO strategies: Use strengths to capture opportunities (offensive) | ST strategies: Use strengths to neutralize threats (defensive) |
| **Weaknesses** | WO strategies: Fix weaknesses to unlock opportunities (investment) | WT strategies: Minimize weaknesses exposed by threats (survival) |
Prioritize: SO strategies first (highest ROI), then ST (protect position), then WO (selective investment), last WT (only if existential).
### Porter's Five Forces
@@ -36,6 +46,8 @@ Analyze industry attractiveness:
Rate each force: Low / Medium / High with supporting evidence.
**Dynamic Five Forces**: Forces change over time. For each force, note the **trend direction** (strengthening/stable/weakening) and the **trigger event** that could shift it. A force rated "Low" today with a strengthening trend deserves more attention than a stable "Medium" force.
### PESTEL Analysis
Macro-environmental scanning:
@@ -49,6 +61,45 @@ Macro-environmental scanning:
| **Environmental** | Climate regulations? Sustainability demands? Resource scarcity? |
| **Legal** | Employment law? IP protection? Competition law? Data privacy? |
### Framework Integration Methodology
Individual frameworks are lenses. Strategic insight comes from combining them. Here is how to synthesize multiple frameworks into a unified analysis:
**The Integration Cascade** — Use frameworks in dependency order:
```
Step 1: PESTEL (macro context)
→ Identifies external forces shaping the industry
→ Output: Which macro factors matter most? What is changing?
Step 2: Porter's Five Forces (industry structure)
→ PESTEL outputs feed directly into Porter's forces
→ Example: "AI adoption accelerating" (PESTEL-Tech) → "Threat of new entrants rising" (Porter)
→ Output: How attractive is this industry? Where is structural power?
Step 3: SWOT (company positioning within industry)
→ Porter's outputs define the external O/T quadrants
→ Internal assessment (S/W) is company-specific
→ Output: Where does this company sit relative to industry forces?
Step 4: Strategic Options Generation
→ SWOT cross-impact matrix generates candidate strategies
→ Porter's forces identify which strategies are structurally viable
→ PESTEL trends determine timing and urgency
```
**Cross-Framework Contradiction Resolution:**
When frameworks disagree, do not average or ignore — investigate:
- PESTEL says favorable + Porter says unattractive → Macro tailwind but bad industry structure (e.g., restaurant industry: everyone eats, but margins are terrible)
- SWOT says strong + Porter says high rivalry → Company advantage may erode faster than expected
- Resolution: State both findings, explain the tension, and let the tension inform the recommendation (e.g., "Enter but with a differentiation strategy that exploits the macro trend while avoiding head-on competition")
**Synthesis Quality Checklist:**
- Does the conclusion follow logically from framework outputs, or did you skip to a preferred answer?
- Did you weight frameworks by relevance (PESTEL matters more for market entry; Porter matters more for competitive strategy)?
- Are the frameworks consistent? If not, is the inconsistency explained?
- Could someone reconstruct your reasoning by reading the framework outputs alone?
### Market Sizing (TAM-SAM-SOM)
**TAM** (Total Addressable Market): Total market demand for a product/service.
@@ -958,3 +1009,92 @@ Strategic Implications:
Key insight for SaaS: The majority of LTV is created AFTER the initial sale.
Disproportionate investment should go to Onboarding → Success → Expansion.
```
---
## Strategic Analysis Anti-Patterns
Common cognitive traps that produce bad strategy. Actively check for these in every analysis:
| Anti-Pattern | Detection Question | Countermeasure |
|---|---|---|
| **Confirmation Bias** — Seeking data that supports pre-existing beliefs; ignoring contradictory evidence | "Did I search for disconfirming evidence with equal effort?" | For every key conclusion, explicitly search for the strongest counterargument |
| **Anchoring** — First number encountered dominates all later estimates (first source says "$10B market" and final estimate drifts toward $10B) | "Is my final estimate suspiciously close to the first number I found?" | Collect 3+ independent estimates; use both bottom-up and top-down methods; investigate any 2x+ divergence |
| **Strategy-by-Analogy** — "Uber did X, so we should do X in healthcare" without testing structural similarity | "What are the 3 most important differences between this situation and the analogy?" | Use analogies to generate hypotheses, never to validate conclusions |
| **Missing Causal Chain** — Clear start and desirable end, but no credible mechanism connecting them (Step 1 → ??? → Profit) | "What specifically happens between 'launch' and 'achieve outcome'?" | Every recommendation needs a testable causal chain: A → B → C → D |
| **Denominator Neglect** — Citing impressive absolutes while ignoring base rates ("10,000 users!" out of 2M impressions = 0.5%) | "Relative to what?" | Always present metrics as ratios/rates; compare to benchmarks |
| **Survivorship Bias** — Deriving strategy from winners only; ignoring that failed companies tried the same thing | "How many companies tried this and failed?" | Seek failure case studies; note success AND failure rates |
| **Planning Fallacy** — Timelines assuming everything goes right | "Does this plan require performing better than we ever have?" | Use reference class forecasting; add 30-50% buffer; present best/base/worst timelines |
---
## Uncertainty Quantification
### Expressing Uncertainty
**For quantitative estimates (market size, revenue, costs):**
- Never give a single number. Always give a range: "Market size: $8-12B (base estimate $10B)"
- State the confidence interval: "80% confident the market is between $8B and $12B"
- Identify the key variable driving the range: "Range is driven primarily by uncertainty in adoption rate (15-25%)"
**For qualitative assessments:**
- Use the calibrated confidence scale consistently:
- **Very High (>90%)**: Would be genuinely surprised if wrong. Multiple high-quality sources agree.
- **High (70-90%)**: Strong evidence, but plausible alternative interpretations exist.
- **Medium (50-70%)**: Balanced evidence. Reasonable people could disagree.
- **Low (30-50%)**: More uncertain than certain. Treat as hypothesis, not finding.
- **Very Low (<30%)**: Speculative. Useful for scenario planning but not for action.
### Assumption Tracking
Every analysis rests on assumptions. Make them explicit:
```
ASSUMPTION REGISTER:
| # | Assumption | Confidence | Impact if Wrong | Validation Method |
|---|-----------|------------|-----------------|-------------------|
| 1 | Market grows 15% YoY | High | Changes TAM by +/- 30% | Track quarterly industry reports |
| 2 | No new regulation in 12mo | Medium | Could block market entry | Monitor regulatory pipeline |
| 3 | Key hire joins by Q2 | Medium | Delays launch 3-6 months | Pipeline status check monthly |
| 4 | Competitor does not cut price | Low | Margin compression 10-15% | Track competitor pricing weekly |
```
Flag any assumption rated "Low" that has "High" impact — these are the **strategic landmines** that deserve contingency plans.
### When to Say "We Don't Know"
It is better to say "insufficient data to assess" than to fabricate a confident-sounding answer. Specifically:
- If fewer than 2 independent sources support a data point, flag it as unverified
- If the key variable has a range wider than 3x (e.g., market could be $5B or $15B), call out that the analysis is highly sensitive to this input
- If you are extrapolating a trend beyond the data range, state the extrapolation explicitly
---
## Industry-Specific Strategic Patterns
Certain strategic dynamics recur within industry categories. Recognizing these patterns accelerates analysis:
### Platform / Marketplace Businesses
- **Winner-take-most dynamics**: Network effects create power-law outcomes. Market share of #1 player often exceeds #2 + #3 combined.
- **Chicken-and-egg problem**: Must solve supply and demand simultaneously. Common solutions: single-player mode, subsidize one side, constrain geography first.
- **Multi-homing risk**: If users can easily use multiple platforms, network effects weaken. Strategy must increase switching costs or exclusive value.
- **Key metric**: Liquidity (match rate between supply and demand). Revenue follows liquidity, not the reverse.
### B2B SaaS
- **Land-and-expand**: Initial deal size matters less than expansion potential. Net revenue retention >120% can drive growth even at 0 new logos.
- **Switching cost lifecycle**: Switching costs increase with integration depth, data accumulation, and workflow embedding. Year 1 churn is always highest.
- **Category creation vs. category entry**: Creating a new category requires 3-5x more marketing spend but yields pricing power. Entering an existing category is cheaper but forces competitive positioning.
- **Key metric**: Net Revenue Retention (NRR). Above 130% = exceptional. Below 100% = leaky bucket that marketing cannot fill.
### Consumer / D2C
- **Acquisition cost spiral**: As easy-to-reach audiences saturate, CAC rises. Growth requires channel diversification or organic/viral mechanics.
- **Brand as moat**: In commoditized categories, brand is the primary differentiation. Brand building requires consistency over years, not campaigns over months.
- **Retention curve shape**: If the retention curve flattens (users who stay past day 30 tend to stay indefinitely), invest in onboarding. If it keeps declining, the product has a retention problem, not an acquisition problem.
- **Key metric**: Cohort retention at day 30/60/90. Payback period on CAC.
### Regulated Industries (Healthcare, Finance, Insurance)
- **Compliance as moat**: Regulatory requirements (HIPAA, SOC2, PCI-DSS) are expensive to achieve but create durable barriers to entry.
- **Sales cycle reality**: Enterprise sales cycles of 6-18 months are normal. Budget accordingly. Premature scaling of sales teams is the #1 killer.
- **Build vs. partner**: In heavily regulated industries, partnering with incumbents (who have regulatory relationships) often beats trying to disrupt them directly.
- **Key metric**: Sales cycle length, regulatory approval timeline, compliance cost as % of revenue.