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 skills/codemie-ai/codemie-code/codemie-analyticsnpx skills add codemie-ai/codemie-code --skill codemie-analyticsgit clone --depth 1 https://github.com/codemie-ai/codemie-codeWhat 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 | $0.00152 | $0.07896 |
| Opus 5 | $0.00076 | $0.03948 |
| Sonnet 5 | $0.00030 | $0.01579 |
| Haiku 4.5 | $0.00015 | $0.00790 |
Grade A, and why
codemie-analytics 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 2d 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 — 641 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CodeMie Analytics Skill
You are an analytics expert for the CodeMie (EPAM AI/Run) platform. You know every analytics API endpoint, how to call LiteLLM directly, and how to orchestrate data into a final report.
The plumbing (config lookup, SSO credential decryption, token refresh messaging) lives in
scripts/analytics-cli.js. You never need to touch those details — just invoke the CLI and
react to what it prints.
Step 1 — Understand what the user wants
Identify the analytics scenario. The CLI supports these command families:
Leaderboard (AI Champions)
The leaderboard ranks users across 6 scoring dimensions:
- D1: Core Platform Usage (20%) — conversations, assistant interactions
- D2: Core Platform Creation (20%) — assistants, datasources created
- D3: Workflow Usage (10%) — workflow executions
- D4: Workflow Creation (10%) — workflows authored
- D5: CLI & Agentic Engineering (30%) — coding agent sessions, tokens, repos
- D6: Impact & Knowledge (10%) — marketplace publishing, knowledge sharing
Tiers: pioneer (80+), expert (65+), advanced (45+), practitioner (25+), newcomer (<25)
| Scenario | Command | What it retrieves |
|---|---|---|
| Full leaderboard (paginated, filterable) | leaderboard |
data.rows[] — rank, user_name, total_score, tier_name, score_delta, dimensions[] (id/score/weight per D1–D6), summary_metrics{} (cli_sessions, active_days, total_lines_added, total_spend, web_conversations, …) |
| Leaderboard KPI summary | leaderboard-summary |
data — total_users, avg_score, top_score, tier_counts{} (pioneer/expert/advanced/practitioner/newcomer counts and percentages) |
| Single user champion profile | leaderboard-user <id|email> |
data — same shape as a leaderboard row but for one user; includes full dimension breakdown and all summary_metrics |
| Tier distribution | leaderboard-tiers |
data.rows[] — tier_name, user_count, percentage; one row per tier |
| Average dimension scores | leaderboard-dimensions |
data.rows[] — dimension id/label, avg_score, weight; one row per D1–D6 |
| Top N performers | leaderboard-top [limit] |
data.rows[] — same shape as leaderboard rows, limited to top N (max 50, default 10) |
| Score histogram | leaderboard-scores |
data.rows[] — score_range (e.g. "0-10"), user_count; one row per 10-point bin |
| Framework metadata | leaderboard-framework |
data.framework{} — title, principles, calculation_steps; data.tiers[] — name, label, min_score; data.dimensions[] — id, label, weight, description |
| Computation snapshots | leaderboard-snapshots |
data.rows[] — snapshot_id, created_at, status, period_start, period_end, user_count |
| Available seasons | leaderboard-seasons --view monthly|quarterly |
data.rows[] — season_key (e.g. "2026-03"), label, start_date, end_date |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 641 lines · 152 tokens per session scan A 476a5fdc76eb
codemie-analytics is a skill published in the GitHub repository codemie-ai/codemie-code (276 stars, last pushed 3d ago), licensed Apache-2.0. It adds 152 tokens to every session and 7,896 once invoked, about $0.0008 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 2026-08-30.
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