Agent skill

Analyze Burn Economics

by LeoYeAI in 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.

MITAuto-check passedResearch & Science

Install Analyze Burn Economics

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill analyze-burn-economics -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills analyze-burn-economics --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
analyze-burn-economics
GitHub stars
2.2k
Token cost
~4k tokens
SKILL.md length
716 words
Files
3
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Comprehensive analysis of Uniswap Firepit burn economics: historical burn P&L, accumulation trends, fee source breakdown, competitive dynamics, and profitability projections.

  • Works in 3 steps: Data Collection (parallel MCP calls) → Analysis (protocol-fee-seeker) → Projections (if enabled)
  • User asks Whats the burn economics?
  • SKILL.md covers Overview, When to Use, Parameters and Workflow, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

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.

When your agent uses it

  • User asks Whats the burn economics?
  • History of protocol fee burns
  • Average profit per burn

Example prompts

  • “s the burn economics?”
  • “History of protocol fee burns”
  • “Average profit per burn.”
  • “/analyze-burn-economics”

Requirements

  • Pre-approved tools (allowed-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

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Data Collection (parallel MCP calls)
  2. Analysis (protocol-fee-seeker)
  3. Projections (if enabled)

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • 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

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    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.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~86
When it runs · the whole SKILL.md, loaded when a task matches
~4k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 716 words, ~3,974 tokens.

Download SKILL.mdSave it as .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.
name
analyze-burn-economics
description
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."
allowed-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
model
opus

Analyze Burn Economics

Overview

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:

  1. Cross-referenced historical analysis: A single 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.
  2. Trend analysis across time: By combining accumulation rates with burn history, the agent identifies whether the protocol fee system is becoming more or less profitable over time, whether burn frequency is increasing (more competition), and whether fee composition is shifting.
  3. Governance-relevant projections: The report includes sensitivity analysis -- how profitability changes with UNI price, threshold adjustments, and fee rate changes. This is the data governance participants need to evaluate parameter proposals.
  4. 30+ minutes of manual analysis in one command: Manually computing per-burn P&L requires querying each burn event, looking up token prices at that block, calculating gas costs, and aggregating. This skill automates the entire research workflow.

When to Use

Activate when the user says anything like:

  • "What's the burn economics?"
  • "Show me the history of protocol fee burns"
  • "What's the average profit per burn?"
  • "When was the last burn?"
  • "How profitable is the Firepit?"
  • "Analyze protocol fee trends"
  • "Burn history and profitability"
  • "Is the fee system working well?"
  • "Governance analysis of protocol fees"
  • "How has burn profitability changed over time?"

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).

Parameters

ParameterRequiredDefaultHow to Extract
chainNoethereumAlways Ethereum mainnet for TokenJar/Firepit
daysNo90Lookback period: "last 30 days", "past year" = 365
include-projectionsNotrue"Just history" or "no projections" implies false

Workflow

                     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                                      │
  │                                                                     │
  └─────────────────────────────────────────────────────────────────────┘
Step 1: Data Collection (parallel MCP calls)

Make all calls simultaneously for speed:

  1. mcp__uniswap__get_burn_history with limit: 100 -- all burns in the lookback window.
  2. mcp__uniswap__get_fee_accumulation_rate -- current daily/weekly/monthly rates.
  3. mcp__uniswap__get_firepit_state -- current threshold, nonce, contract parameters.
  4. mcp__uniswap__get_tokenjar_balances -- current jar contents for context.
  5. mcp__uniswap__get_token_price for UNI -- current UNI price.
  6. 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:

text
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...
Step 2: Analysis (protocol-fee-seeker)

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:

text
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...
Show full SKILL.md (278 more words)Show less
Step 3: Projections (if enabled)

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?

Output Format

Full Report
text
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.
Compact Report (no projections)
text
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})

Important Notes

  • Read-only skill. This skill never executes transactions. It produces analysis only.
  • Historical data depends on burn history depth. If the Firepit is new or has few burns, the analysis will be limited. The skill reports data availability clearly.
  • UNI price cross-referencing is approximate. The skill uses daily OHLCV candles to estimate UNI price at each burn time. Intra-day price movements may cause slight P&L inaccuracies.
  • Fee source attribution is estimated. The TokenJar receives fees from multiple sources but Transfer events don't always indicate the source. Source breakdown is based on known contract addresses and heuristics.
  • Projections assume stable conditions. Forward projections use current accumulation rates and UNI price. Market conditions, governance changes, or protocol upgrades could invalidate projections.
  • Governance implications are analytical, not prescriptive. The report presents data-driven observations but does not make governance recommendations.

Error Handling

ErrorUser-Facing MessageSuggested 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

Files

SKILL.md and 2 other files in skills/analyze-burn-economics of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • README.md
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

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Works with

Questions about Analyze Burn Economics

What does Analyze Burn Economics do?

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.

When should I use Analyze Burn Economics?

Analyze Burn Economics fits situations like: user asks Whats the burn economics?; history of protocol fee burns; average profit per burn.

How do I install Analyze Burn Economics in Claude Code?

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.

How do I install Analyze Burn Economics in Codex?

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.

Can I use Analyze Burn Economics in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Analyze Burn Economics need to run?

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.

Does Analyze Burn Economics access the network?

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.

Is Analyze Burn Economics safe to install?

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.

What licence does Analyze Burn Economics use?

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.

How many tokens does Analyze Burn Economics use?

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.

What are the alternatives to Analyze Burn Economics?

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.

Who maintains Analyze Burn Economics?

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.