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noahowshnoahowsh/acesapi-mcp/skills/dispatch/SKILL.md

dispatch

Skill1 starsChanged 28 days agoMITView on GitHub

Routes tennis analytics requests to the right 2 or 3 skills. Load this FIRST when an ATP or WTA data or analytics request comes in and you are unsure which skills to activate. Prevents loading all 16 skills when only a few are needed. Also sets expectations on what the data cannot answer (serve statistics, in-play, props).

40 other projects use a version of this.

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Used with

Projects with it also use orchestrate, spec and 2 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • varun29ankuSSkill

    orchestrate

    This skill should be used when the user asks to 'orchestrate a task', 'break down work into parallel agents', 'coordinate subtasks', 'run agents in parallel', or mentions 'multi-agent'. Decomposes complex tasks into tracked subtasks, dispatches parallel subagents, and coordinates until completion.

    Also in 4 projects that use this

  • SentrySkill

    spec

    Produce a verified spec — problem, desired outcome, and acceptance criteria — before building. Use when given a GitHub/Linear issue link, when starting from a fuzzy in-conversation idea, or asked to "figure out what to do" / "scope this" / "what should I build" — whenever the *what* isn't yet pinned down. Grills the ambiguities, checks the premise against the code, and produces acceptance criteria you sign off on — then hands to design-first.

    Also in 3 projects that use this

  • AWSSkill

    ship

    Finalize dev work on an issue branch — update docs, push, and open a PR.

    Also in 4 projects that use this

  • MicrosoftSkill

    handoff

    Compact the current conversation into a handoff document for another agent to pick up.

    Also in 6 projects that use this

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 38 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use pre-match ratings only (rating.before), never rating.after.

    line 121
  2. Distinctive:

    If a user asks for a lock, a guarantee or a pick to bet the house on, decline that framing and give the analysis.

    line 123

“Common” is measured: kept by at least 6 in 10 of the 38 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Skill Dispatch Guide

Match the request to a pattern below. Load those skills only.

Default data tool: acesapi-tool. It covers 13 tools for ATP and WTA singles. Load it alongside the strategy skill unless the request needs no data (user-supplied numbers).

Tour check first. ATP is the default. For WTA questions, every call needs "league": "wta". Ask which tour only if the player or event is ambiguous.

First-Use Persona Detection

SignalPersonaStart With
"What matches are on today?" / exploringExplorermatch-lookup + acesapi-tool
"Tell me about this player"Scoutplayer-scouting + ranking-history
"Who wins tomorrow?"Previewermatch-preview + surface-elo
"I want to build a prediction model"Builderfeature-engineering + surface-elo
"Is my model any good?"Validatorwalk-forward-validation + probability-calibration
"Where is the value?" / pricesBettorodds-explorer + edge-detection
"I am new to odds"New Bettorodds-analysis + odds-explorer

Request Patterns -> Skills to Load

"Who plays today?" / "Who won?" / "Show me the draw" / "Every final this season"

  1. match-lookup: results, schedules, draws
  2. acesapi-tool: get_matches, get_schedule, get_tournament, list_tournaments

"Tell me about [player]" / "How is she doing?" / "Compare these two players"

  1. player-scouting: profile, surface fit, opposition quality, form
  2. acesapi-tool: search_players, get_player, get_surface_splits, get_player_stats
  3. Two players with a direct record: also head-to-head-analysis

"[A] vs [B] head to head" / "Who leads the rivalry?" / "How do they split on clay?"

  1. head-to-head-analysis: record, surface split, finals, weighting
  2. surface-elo if a probability is wanted

"Who is number one?" / "Ranking on a date" / "How did the ranking change?"

  1. ranking-history: lists, trajectories, points versus rank
  2. surface-elo if the question is really about strength

"Who is best on clay?" / "Win probability for A vs B" / "How does the rating work?"

  1. surface-elo: ratings, probability formula, blending
  2. head-to-head-analysis for context if a rivalry exists

"Preview the match" / "Who is favored tomorrow and why?"

  1. match-preview: assembles ratings, form, record, route, price
  2. surface-elo for the probability
  3. odds-explorer if the event is a Slam, Masters or WTA 1000

"What are the odds?" / "Best price" / "How did the line move?"

  1. odds-explorer: prices, margins, open to close
  2. odds-analysis for devigging
  3. Remember: odds exist only for Grand Slams (mid-2020 on) and Masters and WTA 1000 (August 2024 on)

"What probability does +450 imply?" / "Remove the vig" / "Shin vs power"

  1. odds-analysis: no data needed unless prices must be fetched

"Is this a value bet?" / "My model says 62 percent" / "How much to stake?"

  1. edge-detection: EV, CLV, fractional Kelly
  2. odds-explorer for current prices
  3. probability-calibration if the model probability is untested

"Would this strategy have worked?" / "ROI over a Slam" / "Is my edge real?"

  1. backtesting: walk-forward simulation with closing lines
  2. walk-forward-validation if the question is about model accuracy only
  3. Warn about credits first (2 per priced match)

"How many games will the match go?" / "Straight sets chance" / "Tiebreak frequency"

  1. set-score-modeling: set distributions, totals, tiebreaks
  2. surface-elo for the match probability that feeds it
  3. State that serve data does not exist

"Build features" / "What should I feed the model?" / "Is there leakage?"

  1. feature-engineering: pre-match ratings, form, rest, symmetric layout
  2. walk-forward-validation when evaluation comes next

"Cross-validation okay?" / "Compare two models" / "How many matches do I need?"

  1. walk-forward-validation: time-ordered evaluation and significance
  2. probability-calibration for reliability

"Are my probabilities calibrated?" / "Overconfident on favorites"

  1. probability-calibration: reliability tables, Platt, isotonic
  2. walk-forward-validation for the split

Things the Data Cannot Answer

Say so immediately and offer the closest proxy.

RequestRealityOffer
Aces, double faults, first-serve percentage, break pointsNo serve statistics existGames-won share, tiebreak record, total games per match (player-scouting, set-score-modeling)
Live or in-play pricesPre-match open and close onlyOpen to close movement (odds-explorer)
Player props (aces, games won, sets)No props dataNot supported: do not build a prop model
Injury or fitness reportsNoneRest days and recent load from scores (feature-engineering)
Prices for tour 250 and 500 eventsNot coveredRatings-only analysis
Rankings outside the top 100 to 150Not in the listsRatings via get_ratings for active players with 10 or more rated matches
DoublesSingles onlyState the limit

Multi-Skill Workflows

Pre-match workflow: match-lookup (find the match) -> match-preview (brief) -> odds-explorer (price) -> edge-detection (only with a tested model).

Model workflow: feature-engineering -> walk-forward-validation -> probability-calibration -> backtesting.

Research workflow: ranking-history and surface-elo to find an anomaly (a rank far from rating) -> player-scouting to explain it -> head-to-head-analysis for the direct record.

Credit Awareness

Before any request that loops over many matches or players, state the number of calls and the credits (see acesapi-tool). Cheap by default: one preview is about 20 credits; a day of results is 2; odds for a Slam round of 64 matches is 128. The free tier is 500 credits a month with no card; plans and top-ups are on https://acesapi.com/pricing.

Rules for Every Answer

  • Cite the tool results used, and name the tour.
  • Use pre-match ratings only (rating.before), never rating.after.
  • Probabilities and edges are estimates. Mention variance and sample size, and prefer closing-line value over short-run results.
  • If a user asks for a lock, a guarantee or a pick to bet the house on, decline that framing and give the analysis.

Anti-patterns

What Claude Might DoWhy It's WrongDo This Instead
Load all 16 skillsWastes context2 or 3 per request
Reach for a nonexistent odds toolOdds are inside get_matchodds-explorer
Start betting analysis without checking coverageTour 250 events have no pricesCheck the tournament tier
Skip the tourWTA ids and ATP ids differPass league
Answer serve questionsNo dataOffer proxies

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