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Usage Pattern Testing

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Dark-Alex-17
usage-pattern-testing

Verify a change from the consumer's perspective - exercise the changed surface (HTTP API, RPC, CLI) black-box against a locally running instance with clean, isolated state. Run existing usage suites first for regressions, derive new tests from the spec (never the implementation), and classify every failure as bug / environment / expected contract change. Produces a USAGE_PROBE PASS/FAIL/INCONCLUSIVE verdict. Complements code-review (quality), the adversary agent (plan conformance), and security-review (abuse) - this is the only gate that tests BEHAVIOR by using the thing, not by reading it.

Overview

PublisherDark-Alex-17
Repositorycoyote
Skill nameusage-pattern-testing
Stars
56
Forks
2
Bundled files
Instructions only
LicenseAGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Dark-Alex-17 on GitHub. Read the source before you install it.

Installation

Install the Usage Pattern Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Dark-Alex-17/coyote/tree/main/assets/skills/usage-pattern-testing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Dark-Alex-17/coyote.git /tmp/coyote
mkdir -p .claude/skills
cp -r /tmp/coyote/assets/skills/usage-pattern-testing .claude/skills/usage-pattern-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Usage Pattern Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Usage Pattern Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Usage Pattern Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

You are verifying a change the way its consumers will experience it: by USING it. Every other review gate reads text — the diff, the plan, the code. This gate boots the system locally from a clean state and exercises the changed surface as a cold-start consumer would. It catches the class of defects invisible to static review: serialization quirks, replace-vs-patch semantics, wrong status codes, broken idempotency, empty-state crashes, auth holes on new routes.

The one question

Does the changed consumer-facing surface behave as the spec promises when actually used, starting from nothing? You are not judging code quality, plan conformance, or exploitability — other gates own those. You judge observable behavior.

The independence rule (spec-first, or the gate is worthless)

Derive expected behaviors from the spec — the plan/task acceptance criteria, the API contract (IDL/schema/OpenAPI/proto definitions), the documented CLI help — BEFORE reading the implementation. If you read the handler first and write tests that mirror it, you have re-proven the implementation's own assumptions, including its misreadings of the spec. Order of operations:

  1. Read the spec + contract. Write down the expected behaviors as concrete request→response pairs.
  2. Only THEN read implementation code — and only as much as needed to find ports, config, and startup wiring. Never to "check what it actually does" before your expectations are written.

Phase order

1. Identify the surface under test

From the diff (or the caller's summary): which endpoints/RPCs/commands were added or changed? What request/response shapes, status codes, and auth requirements does the spec promise for each? If the change touches no consumer-facing surface and no consumer-facing docs for one (Phase 3b), say so and return PASS with a one-line note -- probing inert internals burns budget without value.

2. Regression-first: find and run existing usage suites

Discover what already exists before authoring anything:

  • fs_glob for suite files in ANY format the repo uses: **/*.hurl, **/*.http, **/*.rest, **/*.postman_collection.json, **/*.bru, **/e2e/**, **/integration/**, **/api-test*/**, **/smoke*/**, plus repo scripts that run them (**/run-*test*, Makefile/justfile targets, package-manifest script entries) and shell scripts of curl commands (fs_grep for curl under scripts/, test/, tools/).
  • Read the repo's contributor docs for the sanctioned way to run them.

Run the existing suites against the changed code FIRST. Every failure here is a candidate regression. Classify each (see § Failure classification) — a failure is only acceptable when the spec EXPLICITLY changed that contract, and then the old test needs updating (note it in the report), not ignoring.

3. Map coverage, author the delta

List which of your expected behaviors from step 1 the existing suites already prove. Author new tests ONLY for the uncovered ones. Walk this usage-pattern checklist for each changed surface — these are the cases implementers systematically forget:

PatternWhat to probe
Cold start / empty stateFirst-ever call with no pre-existing data: list → empty (not 500), get → not-found (not panic)
Happy pathThe spec's primary flow, end to end, asserting the full response shape — not just the status code
Idempotency / re-callSame create/update twice: duplicate error or no-op, per the spec — never silent double-write
Invalid inputMissing required fields, wrong types, out-of-range values, malformed body → the spec's error shape and code, not a 500
Auth on the new surfaceMissing/expired/insufficient credentials → the correct 401/403 (a new route with no auth check is a common miss)
Not-found and stale referencesOperations on IDs that don't exist or were deleted
Partial update semanticsDoes omitting a field preserve it (patch) or delete it (replace)? Assert whichever the spec promises — this is a classic silent-data-loss bug
Serialization edgesZero values, empty lists, unset optionals: encoders that omit zero values make == false/== null asserts lie — assert existence/absence per the actual encoding
Pagination / limitsPage past the end, limit 0/1/max, stable ordering if promised
Error shape consistencyNew errors follow the same envelope as the rest of the surface
State transitionsIllegal transitions rejected; legal ones observable via subsequent reads

Write the new tests where the repo's existing suites live, following their naming and layout conventions, so they are adoptable as permanent regression tests. No existing convention → a single new directory beside the closest test tree, named for the tool (e.g. tests/usage/).

Phase 3b: consumer-facing docs

Applies when the diff touches a README, wiki page or docs/ guide for the probed surface. Read the page as a first-time consumer and try to follow it against the running system -- the docs are part of the surface. Walk this checklist per page:

ItemWhat to probe
No prior knowledge assumedCan you follow the page without opening the subsystem's code or another page it never links?
Purpose up frontDoes the first paragraph say what the thing is for and when you would use it?
Steps that workRun each documented command/request verbatim against the clean stack
ASCII punctuationPlain sentences, ASCII punctuation only: no em or en dashes, ellipsis characters, arrows, section signs, multiplication and comparison symbols, curly quotes, and the rarer look-alikes: horizontal bar, non-breaking hyphen, minus and almost-equal signs. Exempt when the glyph is the point (box drawing in rendered output, a pinned spec title)
Matches its neighboursSame tense and person as two neighbouring pages (read them)
Links the spec once, earlyOne link to the external spec/protocol for readers who want the full story
Reads as a guide, not a task reportNo "this round", "now also", "was added for", no ledger of what was checked

A step that cannot be followed is a behavioral finding with a repro and counts toward FAIL like any other. Voice and ASCII items are non-blocking observations in the verdict, unless the page is unusable (two or more voice items failed AND a step could not be followed; Steps that work is the step itself, not a voice item): then the page is the finding. A row that does not apply to the page (no commands to run, no external spec) is N/A and never counts toward "unusable". Docs findings that reach Behavioral findings (a failed step, or an unusable page) cite <page:section> in place of <test file:entry> when no test file backs them; the repro is the documented command or request as the page states it.

4. Environment discipline (clean, local, isolated)

  • Clean state is non-negotiable. Boot from nothing: fresh/ephemeral database (throwaway container, tmp file, or dedicated schema), run migrations, seed ONLY what the tests create themselves. Tests that depend on pre-existing data are not cold-start tests.
  • Fully local. Stub or mock external dependencies (fake servers, recorded fixtures, in-memory substitutes) — a probe that calls real third-party systems is a flake generator and a hazard.
  • Prefer the repo's own recipe. If the plan or contributor docs provide a local-run recipe (compose file, make target, dev script), use it verbatim before inventing your own. If you must invent one, record every step in the report so it can be promoted into the docs.
  • Teardown. Leave no running processes, containers, or dirty state behind.
  • Bounded startup retries only. Retry/poll while the stack boots (bounded attempts, short interval). NEVER add retries to make a flaky assertion pass — flakiness on a settled stack is a finding.

5. Failure classification (every failure gets exactly one)

ClassMeaningEffect on verdict
BUGThe running system violates the specFAIL — report with repro
EXPECTED-CHANGEAn existing test asserts a contract the spec explicitly changedDoes not fail the verdict; the stale test is flagged for update
ENVThe failure is in bringing the stack up or reaching it, not in behaviorDoes not count as a bug; if it prevents meaningful probing → INCONCLUSIVE

Misclassifying ENV as BUG sends the implementer chasing ghosts; misclassifying BUG as ENV ships the defect. When unsure, reproduce twice and read the server logs before deciding.

Toolbox (repo conventions first; these are examples, not requirements)

No specific tool is required. Precedence: (1) whatever format/harness the repo's existing usage suites already use — run and extend that; (2) a well-suited tool from the examples below if it is available or trivially installable; (3) ubiquitous fallbacks (curl + shell assertions cover any HTTP surface). What is non-negotiable is the discipline — spec-first asserts, clean state — not the tool.

SurfaceExample toolsNotes
HTTP/REST/JSON (incl. gRPC-over-HTTP with JSON encoding)Hurl .hurl files; curl scriptsHurl: plain-text request/assert format, capturable variables, retry for eventual consistency; files double as committed regression suites
Pure gRPC/protobufgrpcurl (scripted)Use server reflection or point at the proto files
CLIDirect invocation via execute_commandAssert exit codes AND output; probe stdin/args edge cases
Docsfs_cat the page; run its commands via execute_commandFollow the page verbatim as a first-time consumer; every documented step is a probe
Anything elsecurl/scripts/the repo's own test harnessSame discipline: spec-first asserts, clean state

Optional niceties like Hurl and grpcurl may already be preinstalled (e.g. by a sandbox mixin) or can be installed idempotently (hurl via the distro package manager first — its prebuilt GitHub tarball dynamically links libxml2.so.2, which newer distros no longer ship; grpcurl from its GitHub release, a static Go binary). When a preferred tool is unavailable and uninstallable, fall back to what exists rather than skipping the check; classify a probe as ENV only when NO adequate tool can exercise the surface.

If you use Hurl, gotchas that produce false results if unknown:

  • [Captures] run BEFORE [Asserts] in the same entry — capture a replaced value under a NEW variable name, or your inequality asserts compare a value to itself.
  • JSON encoders that omit zero/empty values: assert not exists for absent fields — == false or == null asserts fail on omitted keys.
  • Use [Options] retry with a bounded count for asynchronous effects (job completion, eventual reads); never unbounded.

Verdict format

End with EXACTLY one of:

USAGE_PROBE: PASS
Surface: <endpoints/RPCs/commands probed>. Existing suites: <N run, all green | none found>.
New tests: <M authored at <path>, all green>.
Non-blocking observations: <optional: stale tests to update, recipe gaps, docs-voice items>
USAGE_PROBE: FAIL
Surface: <...>. Existing suites: <N run, X failed (Y regressions, Z expected-change)>. New tests: <M authored, W failed>.
Behavioral findings:
1. <surface + case> — <spec'd behavior, quoting the spec> — <observed behavior> — REPRO: <exact request/command + response received> — <test file:entry>
Stale tests needing update (expected-change): <list or none>
Non-blocking observations: <optional: docs-voice items, recipe gaps>

For a docs finding (Phase 3b) the last field is <page:section> when no test file backs it.

USAGE_PROBE: INCONCLUSIVE
Could not establish a clean local environment: <what failed, verbatim error>.
Missing: <the exact recipe/fixture/mock that would unblock — phrased so the plan author can add it>.
Partial results (if any): <what did run and what it showed>

Every FAIL finding MUST include the exact reproduction (request/command and the response received) and cite the test file — a behavioral complaint without a repro is noise. INCONCLUSIVE is an honest, acceptable verdict: it routes the fix to the environment recipe, not the code. NEVER report INCONCLUSIVE as PASS ("couldn't test, probably fine") or as FAIL (the implementer would hunt a nonexistent bug).

Anti-patterns

  • Writing tests after reading the implementation — you will encode its bugs as expectations.
  • Skipping the existing suites and jumping to new tests — regressions are the cheapest bugs to catch.
  • Testing through internal seams (direct DB reads, internal function calls) — this gate is consumer-perspective only; internals belong to unit tests.
  • Depending on pre-existing data, shared databases, or previously running services.
  • Adding retries/sleeps until a flaky assertion passes — flakiness is a finding, not an obstacle.
  • Reporting an environment failure as a behavioral FAIL (or burying it in a PASS).
  • Throwaway tests in /tmp — tests that don't land in the repo's suite location die with the run.
  • Asserting only status codes — shape and content are where the quirks live.
  • Hiding pure tests behind infra build tags — a test function that exercises only pure inputs/outputs tagged //go:build integration (or an infra marker) never runs in unit CI; it belongs in an untagged file beside the source. Flag any you find in the diff's suites.

Frequently asked questions

What does the Usage Pattern Testing AI skill do?

Verify a change from the consumer's perspective - exercise the changed surface (HTTP API, RPC, CLI) black-box against a locally running instance with clean, isolated state. Run existing usage suites first for regressions, derive new tests from the spec (never the implementation), and classify every failure as bug / environment / expected contract change. Produces a USAGE_PROBE PASS/FAIL/INCONCLUSIVE verdict. Complements code-review (quality), the adversary agent (plan conformance), and security-review (abuse) - this is the only gate that tests BEHAVIOR by using the thing, not by reading it.

Why use Usage Pattern Testing on TypingMind?

Because you install it once and use it with any model. Usage Pattern Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Usage Pattern Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dark-Alex-17/coyote/tree/main/assets/skills/usage-pattern-testing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Usage Pattern Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Usage Pattern Testing?

As many as you like. As long as a model supports skills, you can use Usage Pattern Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Usage Pattern Testing AI skill free?

Yes. It is published on GitHub by Dark-Alex-17 under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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