Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/stalcup-dev/tl-dps-mcp/copilot-instructionsgit clone --depth 1 https://github.com/stalcup-dev/tl-dps-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/stalcup-dev/tl-dps-mcp/copilot-instructions)<a href="https://agentmods.dev/instructions/stalcup-dev/tl-dps-mcp/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/stalcup-dev/tl-dps-mcp/copilot-instructions.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00973 | $0.00973 |
| Opus 5 | $0.00487 | $0.00487 |
| Sonnet 5 | $0.00195 | $0.00195 |
| Haiku 4.5 | $0.00097 | $0.00097 |
Grade A, and why
tl-dps-mcp copilot-instructions.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TL DPS MCP Copilot Guide
- Project Intent: Parse Throne & Liberty combat logs into deterministic DPS/DPM summaries consumable by CLI, MCP tools, and the PySide6 desktop app.
- Data Contract:
server.analyze_logsreturns the canonical payload (generated_at,source,runs,summary) expected by the CLI, FastMCP tool, markdown reporter, and UI tables—keep field names/types stable. - Log Expectations: Parser assumes UTF-8
.txt/.logrows shaped astimestamp,event_type,skill,internal_id,damage,crit_flag,heavy_flag,hit_type,source,target; unexpected column counts are skipped, not fatal. - Run Filtering: Only
DamageDonerows withdamage > 0contribute to DPS math; maintain_safe_durationfallback to1.0seconds to avoid divide-by-zero regressions. - Rounding: Metrics are rounded to three decimals (
dps,dpm,%helpers) to match validation scripts and sample reports.
Analyzer Stack
- Parser (
dps_logs/parser.py):load_runsyields(run_id, events)covering single files or newestlimit_runsin a directory; reuse its iterator instead of reimplementing file discovery. - Metrics (
dps_logs/metrics.py):summarize_runshapes per-run dictionaries;build_summarymerges runs and mirrorstop_skillsintotop_skills_by_damagefor downstream consumers—extend both when adding new stats. - Reporting (
dps_logs/reporting.py): Markdown builder expects the same payload shape and recomputes means/medians; update helper tables when payload fields change. - CLI (
server.py): Treatanalyze_logsas pure; CLI glue handles argparse, pretty-printing, and optional file mirrors via_write_text. - MCP (
mcp_server.py): FastMCP exposesanalyze_dps_logswith identical argument semantics (log_dir,limit_runs); validation relies on parity withanalyze_logs.
Desktop App
- Client (
app/mcp_client.py):MCPAnalyzerClientshells out topython -m mcp_server; keep stdout JSON-compatible so_extract_payloadcan parse either structured or text responses. - UI (
app/main.py):AnalysisWorkerruns analysis on aQThread; update_update_summary,_update_runs_table,_update_skills_tablein lockstep with payload changes to avoid KeyErrors. - Constants (
app/constants.py): Shared defaults live here (log dir autodetect, run limits, banner path, Python executable); adjust limits once so both UI and analyzers stay aligned. - Dependencies: Install UI deps via
pip install -r app/requirements_app.txt(PySide6 +mcp[cli]); runtime Python 3.11+.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 34 lines · 973 tokens per session scan A 6ceb33e04bbf
tl-dps-mcp copilot-instructions.md is an instructions file published in the GitHub repository stalcup-dev/tl-dps-mcp (0 stars, last pushed 8mo ago), licensed MIT. It adds 973 tokens to every session, about $0.0049 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-01.
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