ranking-elo-update

ranking-elo-update is a skill for Claude Code, Codex from panjose/Co-Scientist. It costs 26 tokens per session (903 once invoked), scanned A, original, Apache-2.0.

A deterministic update step for Elo ratings, a score used to rank competitors after matches, across one completed tournament batch.

In plain words
What is it for?
Use it to update in-memory ratings, persist touched hypothesis records, and write a receipt describing the ranking update.
Why use it?
It applies the same rating rules every time and records which hypothesis files changed, avoiding manual updates and unclear ranking history.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to update in-memory ratings, persist touched hypothesis records, and write a receipt describing the ranking update.

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Install with agentmods
npx agentmods add skills/panjose/co-scientist/ranking-elo-update
Install

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.

Any agent
npx skills add panjose/Co-Scientist --skill ranking-elo-update
Clone the repo
git clone --depth 1 https://github.com/panjose/Co-Scientist

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 903 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00026 $0.00903
Opus 5 $0.00013 $0.00451
Sonnet 5 $0.00005 $0.00181
Haiku 4.5 $0.00003 $0.00090

Measured 10d ago against content hash e0df1e183fef, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

ranking-elo-update 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 10d 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.

skills/ranking-elo-update/SKILL.md · 70 lines

How it starts

The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ranking-elo-update

Goal:

  • Apply deterministic Elo updates for one completed tournament batch, persist the touched hypothesis artifacts, and write the ranking update receipt.

Inputs:

  • completed tournament matches
  • canonical matchups
  • strategy
  • run_dir
  • optional k_factor
  • optional top_k_limit

Outputs:

  • updated in-memory hypothesis ratings and match references
  • updated hypotheses/<id>/HYPOTHESIS.json artifacts for all touched hypotheses
  • state/ranking_update_receipts/<receipt_id>.json
  • RankingUpdateReceiptContract

Context Loading:

  • Open skills/shared-references/schema-index.md.
  • Read packages/agent_contracts/ranking.py and confirm the exact TournamentMatchContract plus HypothesisMatchupContract shapes.
  • Read packages/agent_contracts/hypothesis.py and confirm the exact HypothesisContract shape before writing any touched HYPOTHESIS.json artifact.
  • Use only tournament artifacts whose winners have already been decided and serialized.

Execution Contract:

  • This skill is deterministic and must not call an LLM.
  • Use from tools import apply_and_persist_elo_updates as the stable invocation surface.
  • The exported closeout helper is implemented in packages/run_artifacts/ranking_writeback.py.
  • The helper signature is apply_and_persist_elo_updates(run_dir, matches, matchups, strategy, k_factor=..., top_k_limit=...) -> RankingUpdateReceiptContract.
  • The closeout helper internally calls the canonical Elo helper, persists touched hypotheses, and writes the ranking update receipt.
  • The closeout helper is idempotent for the same stable receipt ID: if the receipt already exists, it returns the persisted receipt instead of reapplying Elo deltas or appending duplicate match refs.
  • Accepted strategy values are placement_tournament and ranked_tournament only.
  • The closeout helper updates canonical hypothesis bundle fields for touched hypotheses, but it must not rewrite standalone REVIEW/*.json stage artifacts.
  • Match refs are not a ranking update receipt. Completion requires the persisted RankingUpdateReceiptContract.

Execution Steps:

  1. Open skills/shared-references/schema-index.md, then read packages/agent_contracts/ranking.py and packages/agent_contracts/hypothesis.py before writing any touched HYPOTHESIS.json.
  2. Load the completed tournament matches as TournamentMatchContract payloads and the paired matchup payloads as HypothesisMatchupContract.
  3. Confirm every match already has a decided winner_id and that the requested strategy is valid.
  4. Call tools.apply_and_persist_elo_updates(run_dir, matches, matchups, strategy, k_factor=..., top_k_limit=...).
  5. Confirm the returned RankingUpdateReceiptContract names every completed match ID and touched hypothesis ID.
  6. Validate the touched hypothesis artifacts and state/ranking_update_receipts/<receipt_id>.json before declaring completion.

Artifact Rules:

  • This skill updates ratings only after the tournament winner artifacts are already finalized.
  • Placement rounds update placement_match_ids for the challenger hypothesis and ranked_match_ids for the defender.
  • Ranked rounds update ranked_match_ids for both participating hypotheses.
  • Match ref arrays must remain duplicate-free. If a completed match ID is already recorded by both touched hypotheses for the same strategy, do not apply the Elo delta again.
  • Do not invent extra ranking heuristics outside the helper; the helper is the canonical Elo update rule.
  • Do not replace canonical ranking closeout with ad hoc file edits; use tools.apply_and_persist_elo_updates(...) after every Elo batch.
  • Review-stage synchronization is a separate concern owned by tools.sync_hypothesis_review(...); Elo writeback must not clobber those standalone review artifacts.
  • Do not hand-write ranking update receipts. The closeout helper owns receipt creation.

Completion Rule:

  • This skill is complete only when the closeout helper has been applied once to the finished batch, every touched hypotheses/<id>/HYPOTHESIS.json artifact has been rewritten in canonical contract form, and the corresponding ranking update receipt exists.

Read the full file on GitHub · 70 lines

Changes

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.

  1. 10d ago First seen · 70 lines · 26 tokens per session scan A e0df1e183fef

Subscribe to this mod's changes

ranking-elo-update is a skill published in the GitHub repository panjose/Co-Scientist (5 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 903 once invoked, about $0.0001 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 2026-08-31.

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