GitHub Deep Research
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
Comprehensive analysis of Uniswap Firepit burn economics: historical burn P&L, accumulation trends, fee source breakdown, competitive dynamics, and profitability projections.
$ npx skills add LeoYeAI/openclaw-master-skills --skill analyze-burn-economics -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills analyze-burn-economics --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analyze-burn-economics .claude/skills/analyze-burn-economics && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "analyze-burn-economics" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/analyze-burn-economics into .claude/skills/analyze-burn-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-burn-economics", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/analyze-burn-economicsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill analyze-burn-economics -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills analyze-burn-economics --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/analyze-burn-economics .agents/skills/analyze-burn-economics && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analyze-burn-economics" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/analyze-burn-economics into .agents/skills/analyze-burn-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-burn-economics", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill analyze-burn-economics -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills analyze-burn-economics --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/analyze-burn-economics .cursor/skills/analyze-burn-economics && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "analyze-burn-economics" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/analyze-burn-economics into .cursor/skills/analyze-burn-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-burn-economics", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/analyze-burn-economics--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill analyze-burn-economics -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills analyze-burn-economics --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/analyze-burn-economics .gemini/skills/analyze-burn-economics && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "analyze-burn-economics" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/analyze-burn-economics into .gemini/skills/analyze-burn-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-burn-economics", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills analyze-burn-economicsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill analyze-burn-economics -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/analyze-burn-economics .github/skills/analyze-burn-economics && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "analyze-burn-economics" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/analyze-burn-economics into .github/skills/analyze-burn-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-burn-economics", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill analyze-burn-economics -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills analyze-burn-economics --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/analyze-burn-economics .opencode/skills/analyze-burn-economics && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "analyze-burn-economics" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/analyze-burn-economics into .opencode/skills/analyze-burn-economics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-burn-economics", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
analyze-burn-economicsComprehensive analysis of Uniswap Firepit burn economics: historical burn P&L, accumulation trends, fee source breakdown, competitive dynamics, and profitability projections.
Analyze Burn Economics is an agent skill from LeoYeAI/openclaw-master-skills. Comprehensive analysis of Uniswap Firepit burn economics: historical burn P&L, accumulation trends, fee source breakdown, competitive dynamics, and profitability projections. Governance-grade research report. Use when user asks "What's the burn economics?", "History of protocol fee burns", or "Average profit per burn."
Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `README.md` and `_meta.json`).
It sits in Research & Science, covering Deep research. It works with Uniswap. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
Task(subagent_type:protocol-fee-seeker)mcp__uniswap__get_burn_historymcp__uniswap__get_fee_accumulation_ratemcp__uniswap__get_firepit_statemcp__uniswap__get_tokenjar_balancesmcp__uniswap__get_token_pricemcp__uniswap__get_token_price_historyFrom allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Analyze Burn Economics loads about 4k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 716 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 716 words, ~3,974 tokens.
.claude/skills/analyze-burn-economics/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.A pure research skill that produces a governance-grade analysis of the Uniswap protocol fee system's burn economics. This skill answers the questions that UNI holders, governance participants, and protocol researchers care about: How profitable have burns been? How are fees trending? What drives accumulation? When should parameters be adjusted?
No execution capability -- this is strictly analytical.
Why this is 10x better than calling tools individually:
get_burn_history call returns raw event logs. This skill cross-references each burn with the UNI price at that time (via get_token_price_history), the gas cost, and the assets claimed -- producing a per-burn profit/loss table that no single tool can generate.Activate when the user says anything like:
Do NOT use when the user wants to execute a burn (use seek-protocol-fees instead) or wants a real-time monitoring dashboard (use monitor-tokenjar instead).
| Parameter | Required | Default | How to Extract |
|---|---|---|---|
| chain | No | ethereum | Always Ethereum mainnet for TokenJar/Firepit |
| days | No | 90 | Lookback period: "last 30 days", "past year" = 365 |
| include-projections | No | true | "Just history" or "no projections" implies false |
ANALYZE-BURN-ECONOMICS PIPELINE
┌─────────────────────────────────────────────────────────────────────┐
│ │
│ Step 1: DATA COLLECTION (parallel MCP calls) │
│ ├── get_burn_history — all burns in lookback window │
│ ├── get_fee_accumulation_rate — current accumulation dynamics │
│ ├── get_firepit_state — current threshold and parameters │
│ ├── get_tokenjar_balances — current jar state │
│ ├── get_token_price (UNI) — current UNI price │
│ └── get_token_price_history (UNI) — UNI price over lookback │
│ │ │
│ ▼ (all data feeds into Step 2) │
│ │
│ Step 2: ANALYSIS (protocol-fee-seeker in analysis mode) │
│ ├── Per-burn P&L calculation │
│ ├── Burn frequency and timing analysis │
│ ├── Fee source and composition trends │
│ ├── Accumulation rate changes over time │
│ ├── Competitive dynamics (searcher behavior) │
│ └── Output: Historical Analysis Report │
│ │ │
│ ▼ (if include-projections: true) │
│ │
│ Step 3: PROJECTIONS │
│ ├── Next profitable burn timing │
│ ├── Expected profit at current rates │
│ ├── Sensitivity to UNI price changes │
│ ├── Impact of threshold parameter changes │
│ └── Output: Projection Report │
│ │
└─────────────────────────────────────────────────────────────────────┘Make all calls simultaneously for speed:
mcp__uniswap__get_burn_history with limit: 100 -- all burns in the lookback window.mcp__uniswap__get_fee_accumulation_rate -- current daily/weekly/monthly rates.mcp__uniswap__get_firepit_state -- current threshold, nonce, contract parameters.mcp__uniswap__get_tokenjar_balances -- current jar contents for context.mcp__uniswap__get_token_price for UNI -- current UNI price.mcp__uniswap__get_token_price_history for UNI with interval: "1d" and limit matching the lookback days -- UNI price history for cross-referencing burn events.Present to user:
Step 1/3: Data Collection Complete
Burn events found: 17 burns in last 90 days
UNI price range: $5.80 - $8.20 (90d)
Current UNI price: $7.00
Current jar value: $52,000
Accumulation rate: ~$7,400/day
Analyzing burn economics...Delegate to Task(subagent_type:protocol-fee-seeker) in analysis mode with all collected data:
Produce a comprehensive burn economics analysis report.
Historical data:
- Burn history: {full burn event data from Step 1}
- UNI price history: {daily OHLCV from Step 1}
- Current accumulation rates: {from Step 1}
- Current Firepit state: threshold={threshold}, nonce={nonce}
- Current TokenJar balances: {from Step 1}
- Current UNI price: ${price}
- Lookback period: {days} days
Analysis tasks:
1. For each burn event, calculate:
- UNI cost at the time of burn (threshold * UNI price at that block)
- Gas cost (from transaction receipt)
- Gross value of assets claimed
- Net profit/loss
- ROI percentage
2. Compute aggregate statistics:
- Total burns in period
- Average profit per burn
- Median profit per burn
- Best and worst burns
- Total value distributed through burns
- Average time between burns
3. Analyze trends:
- Is burn profitability increasing or decreasing?
- Is burn frequency increasing (more competition)?
- How has fee composition changed? (more WETH vs USDC vs others)
- Correlation between UNI price and burn profitability
4. Competitive dynamics:
- How many unique searcher addresses?
- Are the same addresses burning repeatedly?
- What profitability level triggers burns? (min ROI observed)
Return a structured analysis report with all metrics.Present to user after completion:
Step 2/3: Historical Analysis Complete
17 burns analyzed over 90 days.
Total value distributed: $612,000
Average profit: $18,400/burn (65.7% avg ROI)
Generating projections...The agent produces forward-looking projections based on the analysis:
Based on the historical analysis, produce projections:
Current state:
- TokenJar value: ${jar_value}
- Accumulation rate: ${daily_rate}/day
- UNI price: ${uni_price}
- Burn threshold: {threshold} UNI
- Burn cost: ${burn_cost}
Projections to compute:
1. Time to next profitable burn (if not already profitable).
2. Expected profit at current accumulation rate (1-day, 3-day, 7-day projections).
3. Sensitivity analysis: how does profitability change if UNI price moves +/-20%?
4. Threshold sensitivity: what if threshold changed to 2,000 or 8,000 UNI?
5. Break-even analysis: at what UNI price does the current jar become unprofitable?Burn Economics Report (Last 90 Days)
══════════════════════════════════════
SUMMARY STATISTICS
══════════════════════════════════════
Total Burns: 17
Total Value Claimed: $612,000
Total UNI Burned: 68,000 UNI ($476,000)
Total Gas Spent: $765
Total Net Profit: $135,235
Average Profit/Burn: $7,955
Median Profit/Burn: $6,200
Average ROI: 65.7%
Average Burn Interval: 5.3 days
══════════════════════════════════════
BURN HISTORY
══════════════════════════════════════
Date Jar Value UNI Cost Gas Net Profit ROI Searcher
2026-02-03 $52,000 $28,000 $45 $23,955 85.4% 0xab..12
2026-01-28 $41,200 $27,200 $38 $13,962 51.3% 0xcd..34
2026-01-22 $38,500 $26,800 $42 $11,658 43.5% 0xab..12
2026-01-17 $35,100 $25,600 $35 $9,465 37.0% 0xef..56
... ... ... ... ... ... ...
(17 burns total)
Best Burn: 2026-02-03 — $23,955 profit (85.4% ROI)
Worst Burn: 2025-12-15 — $1,200 profit (4.3% ROI)
══════════════════════════════════════
FEE COMPOSITION
══════════════════════════════════════
Token Avg Share Trend (90d)
WETH 35.2% Stable
USDC 27.8% Growing (+3.2%)
USDT 16.5% Stable
WBTC 11.4% Declining (-1.8%)
DAI 6.1% Declining (-0.5%)
Other 3.0% Growing (+1.1%)
══════════════════════════════════════
ACCUMULATION TRENDS
══════════════════════════════════════
Current Rate: $7,400/day
30d Avg Rate: $6,800/day
90d Avg Rate: $6,200/day
Trend: INCREASING (+19.4% over 90 days)
Rate by Source (estimated):
V3 Fees: ~$4,200/day (56.8%)
V4 Fees: ~$1,400/day (18.9%)
UniswapX: ~$1,100/day (14.9%)
V2 Fees: ~$500/day (6.8%)
Unichain: ~$200/day (2.7%)
══════════════════════════════════════
COMPETITIVE DYNAMICS
══════════════════════════════════════
Unique Searchers: 4 addresses (last 90d)
Most Active: 0xab..12 (8 of 17 burns, 47%)
Min ROI at Burn: 4.3% (some searchers burn at thin margins)
Avg ROI at Burn: 65.7%
Competition Trend: Increasing (2 new searchers in last 30d)
══════════════════════════════════════
PROJECTIONS
══════════════════════════════════════
Current Jar: $52,000 (PROFITABLE — $23,955 net)
Next 10% ROI: Already exceeded
Next 100% ROI: ~0.5 days
If jar were empty today:
Break-even: ~3.8 days ($28,045 / $7,400/day)
10% ROI: ~4.2 days
50% ROI: ~5.7 days
UNI Price Sensitivity (current jar $52,000):
UNI at $5.60 (-20%): Burn cost $22,445 → Profit $29,555 (131.7% ROI)
UNI at $7.00 (now): Burn cost $28,045 → Profit $23,955 (85.4% ROI)
UNI at $8.40 (+20%): Burn cost $33,645 → Profit $18,355 (54.6% ROI)
UNI at $13.00 (break-even): Burn cost $52,045 → Profit -$45
Threshold Sensitivity (current UNI price $7.00):
2,000 UNI: Burn cost $14,045 → Profit $37,955 (270.2% ROI)
4,000 UNI: Burn cost $28,045 → Profit $23,955 (85.4% ROI) ← current
8,000 UNI: Burn cost $56,045 → Profit -$4,045 (NOT PROFITABLE)
══════════════════════════════════════
GOVERNANCE IMPLICATIONS
══════════════════════════════════════
- The fee system is healthy: accumulation rate is growing (+19.4% over 90d),
driven primarily by V3 and emerging V4 volume.
- Current threshold (4,000 UNI) produces healthy competition with 4 active
searchers and average 5.3-day burn intervals.
- Increasing the threshold to 8,000 UNI would make burns unprofitable at
current rates unless the jar accumulates for ~7.6 days.
- V4 fee contribution is growing (18.9%) and may overtake V2 within 30 days
at current trajectory.Burn Economics Summary (Last {days} Days)
Burns: {count} | Total Distributed: ${total}
Avg Profit: ${avg_profit}/burn ({avg_roi}% ROI)
Avg Interval: {days} days
Accumulation: ${daily_rate}/day (trend: {direction})
Current Jar: ${jar_value} ({PROFITABLE | NOT_PROFITABLE})| Error | User-Facing Message | Suggested Action |
|---|---|---|
| No burn history | "No burns found in the last {days} days." | Increase lookback period |
| Insufficient burns | "Only {count} burns found. Analysis may be limited." | Increase lookback or accept limited data |
| UNI price history unavailable | "Could not retrieve UNI price history. Per-burn P&L will be approximate." | Proceed with current price as fallback |
| Accumulation data sparse | "Limited accumulation data. Rate estimates may be imprecise." | Try a larger lookback window |
| Token price unavailable | "Could not price {token}. Some jar values may be incomplete." | Token may be exotic or illiquid |
| RPC connection failed | "Cannot connect to Ethereum RPC. Analysis unavailable." | Check RPC configuration |
| Lookback too large | "Lookback of {days} days exceeds available data." | Reduce lookback period |
© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files in skills/analyze-burn-economics of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Analyze Burn Economics next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Analyze Burn Economics this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~4k | Automated safety check: Pass | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 84k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Deep Research WorkflowTokenRhythm/opensquilla | 7.1k | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Deep Researchsanjay3290/ai-skills | 432 | 9 repos | ~683 | Automated safety check: Notes | Apache-2.0 | |
| Horizontal-Vertical Deep ResearchKKKKhazix/khazix-skills | 21k | — | ~2.1k | Automated safety check: Pass | MIT | |
| Academic Research PipelineImbad0202/academic-research-skills | 51k | — | ~15k | Automated safety check: Pass | Custom licence |
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
TokenRhythm/opensquilla
Runs multi-round research in three stages with a persisted state file, evidence tracking and a long-form report with per-claim citations.
sanjay3290/ai-skills
Execute autonomous multi-step research using Google Gemini Deep Research Agent.
KKKKhazix/khazix-skills
Runs a two-axis deep research method on a product, company, concept or person: its full history over time, compared with peers today, delivered as a typeset PDF report.
Imbad0202/academic-research-skills
Orchestrates a ten-stage academic workflow from research to finished manuscript, including integrity checks, two rounds of peer review and revision.
Imbad0202/academic-research-skills-codex
A router skill that sends academic work such as literature reviews, drafting, citation checks, peer review and revision to the right workflow in the ARS suite.
LeoYeAI/openclaw-master-skills
Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.
LeoYeAI/openclaw-master-skills
Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.
LeoYeAI/openclaw-master-skills
Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.
LeoYeAI/openclaw-master-skills
Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.
LeoYeAI/openclaw-master-skills
Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.
LeoYeAI/openclaw-master-skills
Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
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Comprehensive analysis of Uniswap Firepit burn economics: historical burn P&L, accumulation trends, fee source breakdown, competitive dynamics, and profitability projections. Analyze Burn Economics is an agent skill from LeoYeAI/openclaw-master-skills. Comprehensive analysis of Uniswap Firepit burn economics: historical burn P&L, accumulation trends, fee source breakdown, competitive dynamics, and profitability projections.
Analyze Burn Economics fits situations like: user asks Whats the burn economics?; history of protocol fee burns; average profit per burn.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill analyze-burn-economics -a claude-code`. Or copy the skill folder (skills/analyze-burn-economics in LeoYeAI/openclaw-master-skills) into .claude/skills/analyze-burn-economics in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill analyze-burn-economics -a codex`. Or copy the skill folder (skills/analyze-burn-economics in LeoYeAI/openclaw-master-skills) into .agents/skills/analyze-burn-economics in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill analyze-burn-economics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyze-burn-economics, .gemini/skills/analyze-burn-economics, .github/skills/analyze-burn-economics and .opencode/skills/analyze-burn-economics in your project.
SKILL.md names no scripts, command-line tools or credentials: Analyze Burn Economics is instructions for the agent only. Its frontmatter pre-approves these tools: Task(subagent_type:protocol-fee-seeker), mcp__uniswap__get_burn_history, mcp__uniswap__get_fee_accumulation_rate, mcp__uniswap__get_firepit_state, mcp__uniswap__get_tokenjar_balances, mcp__uniswap__get_token_price, mcp__uniswap__get_token_price_history.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Analyze Burn Economics is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Analyze Burn Economics: GitHub Deep Research (bytedance/deer-flow, 84k stars), Deep Research Workflow (TokenRhythm/opensquilla, 7.1k stars), Deep Research (sanjay3290/ai-skills, 432 stars) and Horizontal-Vertical Deep Research (KKKKhazix/khazix-skills, 21k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.