github-radar

name	GitHub Radar
description	A GitHub Intelligence Tool from an AI PM perspective. Goes beyond displaying data to deliver PM-grade paradigm insights. Powered by the locally authenticated gh CLI + GitHub API.
version	0.1.1
author	Kun
tags	["github","intelligence","pm-insight","trending","ecosystem-analysis"]
categories	["research","developer-tools","product-intelligence"]
tools	[{"name":"gh","version":">=2.40.0","type":"cli","required":true,"install_url":"https://cli.github.com","auth_required":true,"check":"gh auth status"},{"name":"python","version":">=3.9","type":"runtime","required":true,"check":"python --version","packages":[]}]

GitHub Radar

An open-source intelligence engine for AI PMs. Five modes, one Layer analysis framework.

Language Selection

This skill ships with both English and Chinese versions. The agent should automatically match the user's language:

• If the user speaks English -> Use skill.md, agents/analyzer.md, references/layer_model.md, templates/*.html, and generate English reports
• If the user speaks Chinese -> Use skill_cn.md, agents/analyzer_cn.md, references/layer_model_cn.md, templates/*_cn.html, and generate Chinese reports
• Scripts (scripts/) and config (config/) are language-neutral and shared by both versions

When to Use

• "What's worth looking at today?" / --pulse -> Mode 1
• "Find me GitHub projects related to [topic]" -> Mode 2
• "Monitor anomalous signals" / --watch -> Mode 3
• "Analyze the ecosystem around [repo]" -> Mode 4
• "Map the evolution of [topic]" / --evolve -> Mode 5
• Any need involving GitHub project discovery, trend analysis, or paradigm assessment

File Structure

github-trend-observer/
├── skill.md                     # Agent execution instructions (English)
├── skill_cn.md                  # Agent execution instructions (Chinese)
├── ONBOARD.md                   # Agent cold-start instructions (English)
├── ONBOARD_CN.md                # Agent cold-start instructions (Chinese)
├── requirements.txt             # Dependency declaration
├── agents/
│   ├── analyzer.md              # PM insight analyzer agent (English)
│   └── analyzer_cn.md           # PM insight analyzer agent (Chinese)
├── scripts/
│   ├── gh_utils.py              # Unified gh CLI utility functions
│   ├── check_rate_limit.py      # API rate limit checker
│   ├── fetch_star_history.py    # Star growth data fetcher
│   ├── radar_pulse.py           # Mode 1 trending fetcher
│   ├── search_repos.py          # Mode 2 search
│   ├── watch_signals.py         # Mode 3 anomaly detection
│   ├── deep_link.py             # Mode 4 relationship analysis
│   ├── evolution_timeline.py    # Mode 5 topic evolution data collector
│   ├── generate_report.py       # HTML/MD report generation
│   └── test_oss.py              # Automated tests (6 tiers, 48 tests)
├── config/
│   ├── seed_list.json           # Key developer list
│   └── domain_keywords.json     # Domain keyword mappings
├── templates/
│   ├── radar-pulse.html         # Mode 1 report template (en/cn variants)
│   ├── direction-search.html    # Mode 2 report template
│   ├── signal-watch.html        # Mode 3 report template
│   ├── deep-link.html           # Mode 4 report template
│   └── evolution-timeline.html  # Mode 5 report template (D3.js interactive)
├── evals/
│   ├── evals.json               # Test cases (English)
│   └── evals_cn.json            # Test cases (Chinese)
└── references/
    ├── layer_model.md           # Layer classification standard (English)
    └── layer_model_cn.md        # Layer classification standard (Chinese)

Dependencies

Dependency	Requirement	Check Command
gh CLI	>= 2.40.0, authenticated	gh auth status
Python	>= 3.9	python --version
Extra Python packages	None, stdlib only	—
API quota	5,000 requests/hour when authenticated	python scripts/check_rate_limit.py

Common Prerequisites

Must be completed before running any mode:

# 1. Check API quota
python scripts/check_rate_limit.py

Determine the execution strategy based on the returned mode field:

• full -> Normal execution, including star history fetching
• degraded -> Skip fetch_star_history.py, use basic data only
• minimal -> Run search scripts only, skip detail API calls

---

Mode 1: Proactive Exploration (Radar Pulse)

Trigger: --pulse or "What's worth looking at today?"

Execution Steps

# Step 1: Check quota
python scripts/check_rate_limit.py

# Step 2: Fetch candidates
python scripts/radar_pulse.py --days 7

# Step 3: Read agents/analyzer.md + references/layer_model.md
#         Layer classification -> Filter out L1/L5 -> Select 1-2 with highest PM value

# Step 4: Fetch star history for selected projects (full mode only)
python scripts/fetch_star_history.py owner/repo

Filtering Rules

1. Label each candidate's Layer
2. Remove L1 (model-level, too low-level) and L5 (wrapper/demo, noise)
3. PM value weighting: L2 x 1.5, L3 x 1.3, L4 x 1.0
4. Take Top 3-5, deep-dive into 1-2

Output Format

# Radar Pulse — {date}
> L2/L3/L4 selection | Filtered {m} from {n} candidates | API: {remaining}/{limit}

## Today's Picks
### {repo} [L?]
> {description}
| Stars | 30d Growth | Language | Created |
|-------|------------|----------|---------|
**Why this one**: {rationale}
**Paradigm signal**: {where the stack is shifting}
**Recommendation**: Deep-dive via Mode 4 / Keep watching

## Also Worth a Look
| Repo | Layer | Stars | One-liner |
|------|-------|-------|-----------|

## Filtered Out
- L1: {n} projects ({examples})
- L5: {n} projects ({examples})

Report saved to: output/radar-pulse_{date}.md

---

Mode 2: Direction Search

Trigger: User provides a technical direction or keywords

Execution Steps

Step 1: Check Quota

python scripts/check_rate_limit.py

Step 2: Keyword Expansion + Layer 1 Relevance Review

1. Understand the topic: State the core concept of the user's search in one sentence
2. Expand keywords: Generate 8-15 search keywords around the topic, covering:
   • Synonymous expressions (swarm -> fleet, colony)
   • Scenario-specific terms (swarm observability, coding agent swarm)
   • Adjacent concepts (coordination, monitoring — things adjacent to swarm)
3. Layer 1 self-review: Review each keyword; the criterion is "would most results returned be about the same category of thing?" — exact match to every word is not required:
   • Keep: Results are different angles on the same topic
   • Remove: Most results fall into a broader category where the topic is only a small subset
   • Example — topic "agent swarm": swarm orchestration keep (same topic); multi-agent framework remove (swarm is a subset of multi-agent; most results won't be about swarms)
   • Example — topic "Agent-human collaboration": human-in-the-loop agent keep (same topic); AI assistant remove (assistant != human-agent collaboration)
4. Present to user for confirmation: List kept and removed keywords with rationale; proceed with search only after user confirms

Step 3: Search

python scripts/search_repos.py "{main keyword}" \
  --also "{keyword2}" "{keyword3}" ... \
  --expand "{fallback1}" "{fallback2}" ... \
  --min-stars 20 --min-recall 50

Step 3.5: Dynamic Strategy When Recall Is Low

If deduplicated results < 50, do not silently expand. Instead, present the current situation to the user with three options:

Searched {n} keywords, only {m} unique results after deduplication. Possible reasons and options:

A. This direction hasn't formed a distinct category yet — The relevant capabilities may be embedded as features within larger frameworks rather than existing as standalone projects. Recommend abandoning the search; this finding is itself valuable.

B. Keyword coverage is insufficient — Current keywords may be missing expressions commonly used by the community. I suggest adding the following keywords: {list}. Will continue after confirmation.

C. Proceed with existing results — Although {m} results is a small set, if quality is sufficient, we can go straight to analysis. Suitable for a quick overview of the landscape.

Heuristics for recommending an option:

• Most keywords return 0 results -> lean toward A (category doesn't exist)
• Only the main keyword has results, expanded terms return nothing -> lean toward B (missing community terminology)
• Few results but highly relevant -> lean toward C (small category with clear signal)

Step 4: Layer 2 Result Relevance Classification

After raw search results are returned and before analysis, classify each repo's relevance:

Classification	Criteria	Handling
high	This project is directly working on the topic	Include in competitive landscape analysis
medium	Related to the topic, but not its primary focus	Include based on quality
low	Keyword match was coincidental; project is actually about something else	Filter out, list under "Filtered Out"

Judgment basis: repo name + description. Ask yourself, "Would this project's author consider themselves working on {user's topic}?"

Step 5: Star History + PM Analysis

# Fetch star growth for key high/medium projects (full mode only)
python scripts/fetch_star_history.py owner/repo

# Read agents/analyzer.md and references/layer_model.md
# Perform Layer classification + PM insight analysis on the data

Output Structure

headline (one paradigm-level judgment)
-> Worth Watching (3-5 deep analysis cards)
-> Competitive Landscape (grouped tables by subcategory; count depends on actual relevant projects)
-> Paradigm Assessment (blue-bordered section)
-> Suggested Deep Dives (3-5, pointing to other Modes)
-> Filtered Out (collapsed, grouped with reasons)

Reports generated in both HTML and MD: output/search_{keyword}_{date}.html/.md

---

Mode 3: Anomalous Signal Monitoring (Signal Watch)

Trigger: --watch or "Monitor anomalous signals"

Known blind spot: Currently only detects growth anomalies in new projects (created within 90 days). Detecting sudden surges in established projects requires persistent storage for differential comparison — reserved for future iteration.

Execution Steps

Step 1: Check Quota

python scripts/check_rate_limit.py

Step 2: Candidate Discovery

python scripts/watch_signals.py
# Global scan (default), three windows: 7d/30d/90d
# Domain scan: python scripts/watch_signals.py --domain ai-agent
# domain options: ai-agent, llm-tools, ai-infra, mcp, all (default)

The script returns a candidate list (sorted by rough velocity descending), each containing:

• stars, forks, created, age_days
• rough_velocity = stars / age_days (rough velocity)
• fork_ratio = forks / stars (adoption depth signal)

Step 3: Initial Screening + Growth Curve Fetching

1. Exclude obviously irrelevant items: Check descriptions, exclude games, tutorials, awesome-lists, and other non-technical projects
2. Fetch star history for remaining candidates (full mode only):

python scripts/fetch_star_history.py owner/repo

Returned growth metrics:

Metric	Meaning
avg_daily_7d / avg_daily_30d	Average daily growth
acceleration	7d avg / 30d avg; >1 means accelerating
trend_direction	Last 3 days avg / prior 4 days avg; indicates current trend
consecutive_growth_days	Consecutive days of growth
peak_recency	Days since peak; 0 = today
burst_ratio	Peak day / 7d avg; high = spike-type growth
recent_7_days[]	Daily breakdown; used to assess growth shape

Step 4: Growth Pattern Classification

Examine the shape of recent_7_days[] to determine the growth type:

Pattern	Characteristics	PM Implication	Signal Quality
sustained	consecutive > 7 + burst_ratio < 3	Organic growth, real demand	High
accelerating	trend_direction > 2 + consecutive > 5	Currently surging, act fast	Highest
spike+decay	burst_ratio > 5 + trend_direction < 0.5	One-time launch burst, likely noise	Low
step	Single-day spike + stable before and after	Event-driven (influencer repost)	Medium, watch follow-through

Step 5: Three-Tier Assessment + PM Analysis

Read agents/analyzer.md and assess each candidate holistically:

• Worth deep-diving: sustained/accelerating pattern + L2/L3 layer
• Watch: Has growth signal but pattern is unclear, or step-type awaiting follow-through
• Ignore: spike+decay + L5 wrapper / tutorial / fork_ratio < 0.02

Output Structure

headline (one sentence summarizing the most important signal this period)
-> Signal Overview (table: repo / stars / rough velocity / pattern / assessment)
-> Worth Deep-Diving (3-5 deep cards with growth curve data and PM insights)
-> Watch List (table with brief rationale)
-> Ignored This Period (collapsed, with reasons)

Report saved to: output/signal-watch_{date}.html

---

Mode 4: Deep Link Analysis

Trigger: User provides a repo URL or owner/repo name

Execution Steps

# Step 1: Check quota
python scripts/check_rate_limit.py

# Step 2: Fetch complete data
python scripts/deep_link.py langchain-ai/langgraph
# URL input supported: python scripts/deep_link.py https://github.com/langchain-ai/langgraph

# Step 3: Fetch star growth curve (full mode only)
python scripts/fetch_star_history.py langchain-ai/langgraph

# Step 4: Read agents/analyzer.md + references/layer_model.md
#         Generate ecosystem map + Layer positioning + paradigm assessment

Output Structure

headline (a bold, tension-bearing judgment that highlights the core tension or most important signal)
-> Basic Profile (table + spark trend chart + commit distribution)
-> Layer Positioning (badge + reasoning + "why not X")
-> Adoption Depth (fork rate / watcher rate / issue activity — distinguishing "spectating" from "actually using")
-> Contributor Structure (table + PM interpretation: bus factor / team vs solo / corporate vs community)
-> Release Cadence (timeline component + product strategy interpretation, not just "how many releases")
-> Issue Composition (table + PM interpretation. If categorization fails (>50% uncategorized),
   must manually sample recent_titles for qualitative analysis as a fallback; never leave blank)
-> Core Innovation (ASCII comparison diagram: traditional approach vs this project's approach.
   This is the fastest path for a PM to understand project value; every report must include one.)
-> Ecosystem Map (ASCII diagram + PM interpretation)
-> Competitor Candidates (collapsed details, annotated with "direct competitor or not" to filter noise)
-> Paradigm Assessment (blue section, structure:
   1. One-sentence paradigm thesis
   2. Core difference: old way vs new way
   3. Who may be threatened
   4. Who is not threatened
   Note: Do not include "relevance to you" — protect privacy)
-> PM Summary (summary-table: maturity / confidence / growth nature / PM value / risk / recommendation)

Output Style

• CSS uses the --bg/--surface/--border/--accent/--muted variable system, consistent across modes
• PM insights use .pm-box card component (white background + border), not inline <p>
• Layer positioning uses .layer-box component with badge + reasoning list + "why not X"
• Paradigm assessment uses .paradigm component (blue background + border)
• Competitor candidates go in <details> collapsed sections
• All technical metrics include plain-language explanations (plain language principle)

Report saved to: output/deep-link_{owner}_{repo}_{date}.html

---

Mode 5: Topic Evolution Timeline

Trigger: --evolve or user provides a technical topic and wants to see the full evolution landscape of open-source projects in that domain

Core Philosophy

Mode 5 is not just search + list. It stands on the arc of technology history, uses futurology frameworks to position the current stage, derives classification systems from evolution logic, then fills them with iterative search. Classifications come from technology evolution logic, not search keywords.

Theoretical Foundations

Framework	Author	Core Idea	Role in Mode 5
Tech Revolution Cycles	Carlota Perez	Irruption→Frenzy→Synergy→Maturity (50-60 years)	Position the topic's stage, determine classification granularity
Evolution Axis	Simon Wardley	Genesis→Custom→Product→Commodity	Label maturity level for each classification
Combinatorial Evolution	W. Brian Arthur	Technology = combination of existing technologies; tech domains = problem-solving "languages"	Classification = identifying "tech domains"
Technium Trends	Kevin Kelly	Technology has intrinsic trends: complexification, specialization, symbiosis	Predict inevitable emerging categories
TRL	NASA	9-level Technology Readiness Level	Assist in labeling domain maturity

Execution Steps

Phase 1: Scope Boundary Definition

Goal: Generate decidable IN/OUT criteria

1. Core definition: One sentence defining the topic's essence
2. Inclusion criteria: What must a project satisfy to be IN? (2-3 AND conditions)
3. Exclusion criteria: What makes a project immediately OUT? (2-3 OR conditions)

Gate: Present to user for confirmation before proceeding

Phase 2-A: Technology Evolution Positioning (Macro)

Answer three questions using futurology frameworks:

1. Perez Phase: Which stage is this topic in — Irruption / Frenzy / Synergy / Maturity?
2. Core Building Blocks (Brian Arthur): Which existing technologies serve as "raw materials"? What Wardley stage is each at?
3. Kelly Trend Projection: Where do technology's intrinsic trends point?
   • Complexification: simple → composite → systemic
   • Specialization: general → vertical → niche
   • Symbiosis: independent → collaborative → symbiotic
   • Self-reference: execute → optimize execution → optimize "the optimization process"

Phase 2-B: Domain Identification (Derive Classifications)

Method A: Evolution Tree (top-down)

Draw the technology branching tree from earliest prototype to current frontier. Each branch = one candidate classification.

Method B: Project Clustering (bottom-up)

Sample 20-30 projects randomly, describe each sub-problem in 3-5 words, merge similar descriptions.

Method C: Speculative Domains (minimum 3, mandatory)

Derive from Kelly trends + Arthur combinatorial logic: categories that barely exist today but will inevitably emerge. Each speculative domain must include:

Field	Description
Speculation logic	Why must it emerge? Which building blocks are in place?
Signal signature	If germinating, what keywords / paper patterns / project traits should appear?
Search directive	Keywords for Phase 2-C signal hunting

Merge results from all three methods, targeting 5-12 classifications.

Phase 2-C: Signal Hunting (Actively Search Speculative Domains)

For each speculative domain, search GitHub / arXiv using signal signature keywords:

Found >= 3 projects → Upgrade to Emerging (confirmed germination)
Found 1-2 projects  → Keep as Speculative, annotate observed signals
Found 0 projects    → Keep as Speculative, search again in next iteration

Phase 2-D: MECE Validation + Confirmation

• Mutually Exclusive: Pick any two categories — can you find a project that perfectly belongs to both? Yes → adjust boundaries
• Collectively Exhaustive: Can every project be assigned to a category? No → add category or adjust definitions
• Anomaly detection: A category with < 3 projects and not speculative → consider merging; a category with > 30% of total → consider splitting

Gate: Present classification list + evolution tree + Wardley labels to user for confirmation

Phase 3: Judgment Cards

Write a judgment card for each classification:

Name: [English]
One-line definition: ...
Wardley Stage: Genesis / Custom / Product / Commodity
Status: Established / Emerging / Speculative
Typical traits (match 2/3 to classify):
  - Trait A
  - Trait B
  - Trait C
Anchor projects: [2-3 unambiguous exemplars]
Easily confused with: [list] → Distinguishing criteria: [how to decide]
Speculation logic (speculative only): Why it must emerge

Judgment cards are embedded at the top of the HTML report as a collapsible panel.

Phase 4: Iterative Search + Classification (Layered Strategy)

Each round:

1. Search Round N (reuse Mode 2's search_repos.py)
2. Scope Filter (Phase 1 rules)
3. Classify each project (Phase 3 judgment cards)
4. Conflict detection: Matches 2 categories → check which matches more traits
5. Speculative domain signal scan: Do any classified projects have sub-features touching speculative domains?
6. Sample validation: Randomly pick 2 from each category for manual confirmation
7. Progress report: Output "Round N added X new (Y%), cumulative Z, covering M lanes"

Search round control:

Round 1-2: Mandatory (minimum)
  └─ Checkpoint after Round 2

Checkpoint (after Round 2):
  ├─ Cumulative < 50 projects   → Force Round 3 (insufficient coverage)
  ├─ Cumulative 50-99           → Continue Round 3, unless Round 2 added < 10%
  └─ Cumulative >= 100          → Pause, report to user and ask:
       "Collected N projects covering M lanes.
        Saturation search may need 1-2 more rounds,
        potentially revealing new sub-domains. Continue?"
       User choice:
         ✓ Continue      → Run saturation search (until new < 10%)
         ✗ Stop          → Enter Phase 5 targeted supplement
         ✗ Specify focus  → User gives keywords, search 1 targeted round

Saturation search (if triggered):
  - Output progress report after each round
  - New < 10% → Declare saturation, enter Phase 5
  - Maximum 5 rounds (prevent infinite loops)

Phase 5: Targeted Supplement + Domain Iteration

• If an Emerging domain has < 3 projects after Phase 4 → trigger targeted search (using judgment card signal signatures)
• If > 20% of new-round projects can't be classified → trigger Phase 2 iteration (add/adjust classifications)
• Speculative domain status transitions:

Speculative → Signal found → Emerging → Projects >= 5 → Established
           → No signal after multiple rounds → Label "no current signal", keep for observation

Visualization Output

D3.js interactive timeline with swim-lane layout:

• X-axis: project creation date
• Y-axis: classification swim lanes
• Circle size: log(stars)
• Color: classification color
• Interaction: hover shows details, click opens GitHub
• Top panel: judgment cards (collapsible)
• Bottom table: full project index + one-line descriptions

Futurology Analysis Layer (Phase 6)

On top of the timeline and project index, overlay two analysis layers:

6-A: Evolution Panorama (6 Dimensions)

Holistic analysis of the complete classified dataset (not per-lane descriptions):

#	Dimension	Core Question	Method
1	Stage Assessment	Where is this domain in the technology lifecycle?	Mark stage from data, milestone events viewable per stage
2	Driving Forces	What underlying forces drive the entire domain?	Find 2-3 cross-lane common drivers, each with evidence chain
3	Convergence Trajectories	What's happening between lanes?	Identify lane intersection points, derive new directions from convergence
4	Inevitable vs. Contingent	Which trends are irreversible? Which depend on events?	Two-column comparison, each with reasoning
5	Scenario Projection	Possible landscape in 12 months?	2-3 scenarios (optimistic/baseline/risk), with trigger conditions and probability
6	Weak Signals	What's underestimated but could change the landscape?	Find anomalous proportions, missing directions, concept convergence signals from data

Key principles:

• Analysis subject is the entire domain, not lane-by-lane
• Every claim must have data support (project names/counts/ratios) — no empty statements
• Don't name-drop academics ("Kevin Kelly-style", "Carlota Perez framework") — let the analysis speak for itself

6-B: For the Reader (Three-Dimensional Afterword)

The report's final section — meta-analysis of the report itself. Three dimensions, each answering one core question:

Dimension	Perspective	Core Question	Method
Looking Back	Retrospective	What pattern is repeating? What does it predict?	Identify wave rhythms from data (capability layer → infrastructure layer → safety layer?). Find catalytic chains (which building block spawned which paradigm). Use patterns to predict the next "should-arrive-but-hasn't" layer
Looking Forward	Endgame	Regardless of which future, what tensions must be resolved?	Review multiple scenarios from projections, extract structural tensions common to all scenarios (not predicting an endpoint, but identifying unavoidable problems). Tension = two goals that are structurally contradictory
Looking Now	Action	What decisions have a time window?	Identify the report's most critical asymmetry (extract specific ratios from data). Name the negative space (directions that should exist per report logic but don't). Declare the report's horizon boundaries (what the data sources can and cannot see)

Key principles:

• All three dimensions are derived from report data, not generic philosophical musings
• Each dimension includes specific data points at the bottom (project distributions, ratios, source declarations)
• Looking Forward does not assume a single endgame — use "tensions" instead of "predictions"
• Looking Now must include an honest methodology statement (data coverage and biases)

Report saved to: output/evolution-timeline_{topic}_{date}.html

---

Seed List Customization

Edit config/seed_list.json to add or remove developers you follow:

{
  "builders": [
    {"github": "username", "note": "why they matter"}
  ],
  "last_updated": "2026-02-18"
}

The default list currently includes 76 important AI builders/orgs, covering 17 categories including labs, agent frameworks, coding agents, inference, platforms, and more.