Windows Desktop E2E
affaan-m/ECC
E2E testing for Windows native desktop apps (WPF, WinForms, Win32/MFC, Qt) using pywinauto and Windows UI Automation.
Audit the adaptive window hill-climber and region-resize logic for implementation defects (not algorithm quality)
$ npx skills add ben-manes/caffeine --skill audit-adaptivity -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ben-manes/caffeine audit-adaptivity --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ben-manes/caffeine.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/audit-adaptivity .claude/skills/audit-adaptivity && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "audit-adaptivity" agent skill from https://github.com/ben-manes/caffeine/tree/master/.claude/skills/audit-adaptivity into .claude/skills/audit-adaptivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audit-adaptivity", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ben-manes/caffeine/tree/master/.claude/skills/audit-adaptivityType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ben-manes/caffeine --skill audit-adaptivity -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ben-manes/caffeine audit-adaptivity --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ben-manes/caffeine.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/audit-adaptivity .agents/skills/audit-adaptivity && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "audit-adaptivity" agent skill from https://github.com/ben-manes/caffeine/tree/master/.claude/skills/audit-adaptivity into .agents/skills/audit-adaptivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audit-adaptivity", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ben-manes/caffeine --skill audit-adaptivity -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ben-manes/caffeine audit-adaptivity --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ben-manes/caffeine.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/audit-adaptivity .cursor/skills/audit-adaptivity && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "audit-adaptivity" agent skill from https://github.com/ben-manes/caffeine/tree/master/.claude/skills/audit-adaptivity into .cursor/skills/audit-adaptivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audit-adaptivity", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ben-manes/caffeine.git --path .claude/skills/audit-adaptivity--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ben-manes/caffeine --skill audit-adaptivity -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ben-manes/caffeine audit-adaptivity --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ben-manes/caffeine.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/audit-adaptivity .gemini/skills/audit-adaptivity && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "audit-adaptivity" agent skill from https://github.com/ben-manes/caffeine/tree/master/.claude/skills/audit-adaptivity into .gemini/skills/audit-adaptivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audit-adaptivity", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ben-manes/caffeine audit-adaptivityInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ben-manes/caffeine --skill audit-adaptivity -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ben-manes/caffeine.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/audit-adaptivity .github/skills/audit-adaptivity && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "audit-adaptivity" agent skill from https://github.com/ben-manes/caffeine/tree/master/.claude/skills/audit-adaptivity into .github/skills/audit-adaptivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audit-adaptivity", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ben-manes/caffeine --skill audit-adaptivity -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ben-manes/caffeine audit-adaptivity --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ben-manes/caffeine.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/audit-adaptivity .opencode/skills/audit-adaptivity && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "audit-adaptivity" agent skill from https://github.com/ben-manes/caffeine/tree/master/.claude/skills/audit-adaptivity into .opencode/skills/audit-adaptivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "audit-adaptivity", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
audit-adaptivityAudit the adaptive window hill-climber and region-resize logic for implementation defects (not algorithm quality)
Audit Adaptivity is an agent skill from ben-manes/caffeine. Audit the adaptive window hill-climber and region-resize logic for implementation defects (not algorithm quality)
Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: A high performance caching library for Java. The licence is Apache-2.0.
7 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit e972fb0. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Audit Adaptivity loads about 3.1k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 1,391 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from ben-manes/caffeine at commit e972fb0, republished under its Apache-2.0 licence (© ben-manes). 1,391 words, ~3,060 tokens.
.claude/skills/audit-adaptivity/SKILL.md (or your agent's skills folder).Audit the adaptive W-TinyLFU control loop and the admission-window / main-region
resize logic for IMPLEMENTATION defects. This is the one eviction subsystem not in
/audit-subsystem-safety's scope, and it was recently rebuilt — the climber now lives in the
package-private WindowClimber class (reached through the generated climber field), with a new
density-signal + probe-machine large tier (>4096) — so it has elevated bug density.
The adaptation policy itself is at its tuned frontier. Out of scope: convergence
rate, hit-rate, oscillation-as-a-design-tradeoff, and the choice of the tuning
constants. (Behavioral hit-rate regression against the adversarial trap suite is
/climber-gate's job, not this audit's.) Do NOT report "the climber could converge faster / oscillates / constant
X should be Y." Report only defects: arithmetic that yields a wrong value, a sign
error, a state that violates a structural invariant, a race, a NaN, or an overflow.
climb, determineAdjustment — the feedback step (reactive: hit-rate delta; density:
within-sample densities → step → adjustment). determineAdjustment carries
mainProtectedMaximum to size the probation capacity for the probe verdictdensityClimb, armProbe, walkStep, probeEnding, undoProbe, DensityClimber.steer — the
large tier's signal, probe machine, and verdict internals (all on WindowClimber)increaseWindow, decreaseWindow, demoteFromMainProtected — region transfersetMaximumSize — initial window/main split, plus WindowClimber.resized (the
SLOW_ADAPT_THRESHOLD step-sign flip and the probe-machine reset)evictFromWindow / evictFromMain — they consume the region maxima the climber setsProbe machine (>4096, in WindowClimber): starved samples (region hits below
max(4, requestCount >> MIN_SIGNAL_SHIFT)) at a blind corner launch a bold-driver walk.
Endings: crash-abort (full undo, refractory re-armed WITHOUT doubling), reversal-through-base and
budget expiry (failed experiments: full undo + ladder x2), adjudication at >=4x the bar under the
committed depth (confirm keeps the position + ladder resets to 1; anything else fails with a full
undo). The verdict is asymmetric BY DESIGN: an up-probe confirms iff
ln((windowDensity+eps)/(walk.baseProbationDensity+eps)) > 0 — the probation-marginal baseline
FROZEN in armProbe — while a down-probe uses the average-density sign test (error*dir > 0).
Ladder: PROBE_BACKOFF_INITIAL (16) doubling to _MAX (64). Invariants to audit:
1 <= starvation.rung <= 64; 0 <= refractoryLeft <= starvation.rung, which both oracles assert
directly — the walk budget is a separate field (Walk.samples, bounded by PROBE_WALK_BUDGET) on
an object that exists only while walking, so the two can no longer alias; probe state fully reset
by resized; an undo returns exactly to walk.baseWindow; the below-floor lift cannot exceed the
floor; sample.probationHits <= sample.hits - sample.windowHits;
walk.baseProbationDensity is written ONLY in armProbe (each re-arm re-snapshots) and is
non-negative and finite; the probation capacity denominator is
max(1, maximum - windowMaximum - mainProtectedMaximum) — capacities, never occupancy.
NOT bugs (adjudicated design; read hill-climber.md §4 before flagging): the up/down verdict
asymmetry (the window has no marginal substructure to price against); the frozen — hence
stale-looking — baseline (judging against the LIVE probation rate is an absorbing false-veto: the
walk's own demotions enrich it — the demoflood gate row; a cold-start-transient baseline is
bounded and self-heals because every re-arm re-snapshots); probation attribution captured
BEFORE reorderProbation can promote the entry in onAccess (the promoting access counts as a
probation hit, the next as protected); a zero baseline auto-confirming any >=4x-bar earnings
(the opportunity cost of a dead boundary is ~zero — nullchurn stays harmless); and the lowmix
named trade (a gate sentinel). A walk's bases (down, baseHitRate, baseWindow,
baseProbationDensity) are final on the Walk object, which exists only while one is in
flight — "dead state while not probing" is the absent object, so there is nothing to go stale.
Cache-side fields: windowMaximum, mainProtectedMaximum, windowWeightedSize,
mainProtectedWeightedSize; QUEUE_TRANSFER_THRESHOLD.
WindowClimber owns adjustment, sample (Sample: hits, misses, windowHits,
probationHits, previousHitRate), and tier (a ReactiveClimber or DensityClimber).
The Climber base class owns step (Step: size), initialized from the maximum and the
strategy's chosen direction at construction. DensityClimber owns refractoryLeft, retreatLeft,
undoRemaining, and its helper objects: walk (Walk, null while none is in flight:
ladder, isAudit, down, baseWindow, baseHitRate, baseSmoothedRate,
baseProbationDensity, samples, belowBarStreak, aboveStreak, beatBase), starvation and
audit (a Ladder each: rung, crashStreak), auditClock (AuditClock: down,
waitSamples, stillSamples, lastWindow), anchor (Anchor: window, rate, held,
freshLeft, returning, returnLeft, shortfallStreak), and rates (Rates: smoothed,
deviation). Reading is the per-sample derived view, computed once and read-only.
The 38 constants live with the mechanism each tunes, so a knob names its owner:
WindowClimber — RESTART_THRESHOLD;
DensityClimber — DENSITY_THRESHOLD, DENSITY_GAIN, SAMPLE_MULTIPLIER;
Step — STEP_PERCENT, STEP_DECAY_RATE, MIN_INITIAL_STEP;
ReactiveClimber — SLOW_ADAPT_THRESHOLD, SLOW_ADAPT_RATIO_CAP, SLOW_ADAPT_DECAY_RATE;
Reading — STABLE_BAND_FRACTION, MAX_STEP_FRACTION, WINDOW_FLOOR_FRACTION,
DENSITY_EPSILON, MIN_SIGNAL_SHIFT, MIN_STARVATION_BAR;
Rates — VETO_MARGIN_MIN, RATE_SMOOTHING, DEVIATION_SEED, VETO_MARGIN_SCALE;
AuditClock — AUDIT_WAIT_INITIAL, AUDIT_WAIT_FIRST, AUDIT_WAIT_MAX;
Anchor — VETO_STREAK, VETO_RETURN_BUDGET;
Walk — PROBE_WALK_BUDGET, PROBE_BAR_CAP, PROBE_EXIT_BAR_MULTIPLE,
AUDIT_CRASH_PERSISTENCE, AUDIT_COMMITMENT, AUDIT_CONFIRM_STREAK;
Ladder — PROBE_BACKOFF_INITIAL, PROBE_BACKOFF_MAX, PROBE_CRASH_ESCALATION,
PROBE_STRIDE_SCALE_MID, PROBE_STRIDE_SCALE_DEEP, PROBE_COMMITMENT_MID,
PROBE_COMMITMENT_DEEP.
windowMaximum + mainMaximum (probation + protected)
must equal maximum after every climb and every resize. Can any single transfer,
or a sequence capped by QUEUE_TRANSFER_THRESHOLD, drift the sum?windowMaximum or mainProtectedMaximum go negative
— a quota larger than the donor region, or repeated decreaseWindow at the floor?
EXCEPTION (adjudicated 2026-07, F1; duration priced 2026-08-22, M1): a transiently
negative policyWeight — the sanctioned telescoping race, an out-of-order UpdateTask
drain — can over-shift the caps beyond the commanded adjustment, even past these
bounds. Tolerated by design: the caps are policy targets (eviction is driven by the
telescoping weightedSize/maximum), and they walk back only by the weight each later
transfer moves, so a swing larger than a cycle's transfer suspends the split for many
cycles with the total still bounded. Report cap drift only with a mechanism that never
walks back.increaseWindow/decreaseWindow the quota is decremented
per transferred node by policyWeight. With weighted entries, can quota underflow,
skip/over-run the loop, or transfer the wrong count? Does the
QUEUE_TRANSFER_THRESHOLD cap leave the regions half-adjusted such that the next
climb mis-reads them? (Same F1 exception as #2 for the negative transient.)determineAdjustment math:requestCount = hits + misses; the early return guards requestCount < effectiveSampleSize. Is the hitRate division ever reachable with
requestCount == 0?ReactiveClimber.samplePeriod): (long) (sketchSampleSize * ratio),
where ratio = clamp(initialStep / magnitude). Can initialStep be 0 (maximum 0 or
tiny) making magnitude 0 → division by zero? Can the (long) cast truncate
ratio so it defeats the intended sample-period growth?ReactiveClimber.climb uses Math.copySign(Step.restartMagnitude(max), amount). For
amount == 0.0 / -0.0, does copySign choose the intended direction? Can step.size
become NaN or 0 and permanently stall adaptation (a stuck-window bug, distinct from
slow convergence)?setMaximumSize at boundaries: the step-sign flip at max <= SLOW_ADAPT_THRESHOLD
plus a runtime maximum change via Policy.eviction.setMaximum — when maximum
crosses SLOW_ADAPT_THRESHOLD in either direction, do the window/main split, the
step.size sign, and the sample state stay mutually consistent?adjustment consumption: climb calls determineAdjustment then
increaseWindow/decreaseWindow off the climber's adjustment. When determineAdjustment
early-returns (uninitialized sketch, sub-sample request count), can a stale
adjustment from a prior cycle be re-applied?hill-climber.md §4 carries a write-owner table (observation / active walk / starvation
retry / audit retry+schedule / goal guard / motion out). Enumerate every write to
starvation.rung, starvation.crashStreak, audit.rung, audit.crashStreak,
auditClock.waitSamples, auditClock.stillSamples, anchor.held, anchor.freshLeft and
check each against its owner —
including endings that are not crashes. Both landed defects were exactly this shape: the
shared crash streak let exogenous pulses pair separate audit crashes into a rung ratchet
(H4-C1), and later a non-crash ending (budget expiry, reversal-through-base) still cleared
the other layer's streak, which disarmed that layer's escalation and its
AUDIT_CRASH_PERSISTENCE tolerance — so an interleaved blind corner reached around the first
fix. One cross-write is sanctioned, journaled: an audit confirm rewards the starvation
ladder (starvation.reward) and zeroes its refractory. An audit's undo leaves the
refractory alone (since 2026-08-16, pinned by
undoProbe_auditRetreat_leavesTheStarvationRefractoryAlone). Anything else is a finding.
Checkable products the oracles already
assert, and which a new invariant should join: anchor.freshLeft > 0 ⇒ anchor.held,
anchor.held ⇒ anchor.isPlanted, walk != null ⇒ undoRemaining == 0, and
auditClock.waitSamples > PROBE_BACKOFF_MAX ⇒ audit.rung == PROBE_BACKOFF_MAX (the ratchet as
an invariant). Note the one legitimate coupling so it is not reported: the audit clock is a
function of window position, so any layer that moves the window decays stillSamples — that
is the intended semantics, and it is the remaining path by which frequent blind corners defer
audits.For each defect: give concrete maximum/weight/access values, trace the arithmetic
step by step, show the resulting invariant violation or wrong region size, and a
Verification (a BoundedLocalCacheTest white-box method plus the required -P flags).
Everything here runs under evictionLock (single-writer), so most findings will be
arithmetic / state-corruption, not races — but explicitly check whether any
climber-written field (adjustment, step.size, the region maxima) is also read
off-lock by a concurrent reader before concluding "single-writer, cannot race."
© ben-manes, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .claude/skills/audit-adaptivity of ben-manes/caffeine.
Open the folder on GitHubat commit e972fb0
Audit Adaptivity next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Audit Adaptivity this skillben-manes/caffeine | 18k | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Windows Desktop E2Eaffaan-m/ECC | 276k | 1 repos | ~7.6k | Automated safety check: Pass | MIT | |
| Windows Desktop E2Eaffaan-m/ECC | 276k | — | ~5.5k | Automated safety check: Pass | MIT | |
| Agent Adaptive Coordinatorruvnet/ruflo | 74k | 2 repos | ~4k | Automated safety check: Pass | MIT | |
| Text Resizingthedaviddias/Front-End-Checklist | 74k | — | ~509 | Automated safety check: Pass | MIT | |
| Landmark Regionsthedaviddias/Front-End-Checklist | 74k | — | ~449 | Automated safety check: Pass | MIT |
affaan-m/ECC
E2E testing for Windows native desktop apps (WPF, WinForms, Win32/MFC, Qt) using pywinauto and Windows UI Automation.
affaan-m/ECC
E2E testing for Windows native desktop apps (WPF, WinForms, Win32/MFC, Qt) using pywinauto and Windows UI Automation.
ruvnet/ruflo
Agent skill for adaptive-coordinator - invoke with $agent-adaptive-coordinator
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing rendered HTML, interactive components, or design-system patterns related to Support text resizing to 200%.
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing rendered HTML, interactive components, or design-system patterns related to Use landmark regions correctly.
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing stylesheets, component styles, and responsive behavior related to Use CSS logical properties for i18n and RTL support.
ben-manes/caffeine
Runs controlled JMH experiments on the Caffeine cache to find shared contention and hot-path waste, then reviews correctness and returns a reviewable patch.
ben-manes/caffeine
Audits a module by walking its git history commit by commit, tracking unresolved issues forward, and reporting the ones that survive to HEAD as findings.
ben-manes/caffeine
Runs a hostile review of the Caffeine Java caching library with parallel subagents that get no design docs, then challenges and consolidates their findings.
ben-manes/caffeine
Audits the Caffeine cache source for hot-path costs such as allocations, contention and memory layout, reporting only findings tied to specific lines.
ben-manes/caffeine
Compares code paths that should behave the same, such as sync and async cache methods, and requires a concrete scenario where the two observably disagree.
ben-manes/caffeine
Prices each step of the window climber algorithm by disabling it in turn, to find steps that no longer earn their keep and branches that no longer fire.
Audit the adaptive window hill-climber and region-resize logic for implementation defects (not algorithm quality). Audit Adaptivity is an agent skill from ben-manes/caffeine.
Run `npx skills add ben-manes/caffeine --skill audit-adaptivity -a claude-code`. Or copy the skill folder (.claude/skills/audit-adaptivity in ben-manes/caffeine) into .claude/skills/audit-adaptivity in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ben-manes/caffeine --skill audit-adaptivity -a codex`. Or copy the skill folder (.claude/skills/audit-adaptivity in ben-manes/caffeine) into .agents/skills/audit-adaptivity in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ben-manes/caffeine --skill audit-adaptivity -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/audit-adaptivity, .gemini/skills/audit-adaptivity, .github/skills/audit-adaptivity and .opencode/skills/audit-adaptivity in your project.
SKILL.md names no scripts, command-line tools or credentials: Audit Adaptivity is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Audit Adaptivity is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Audit Adaptivity: Windows Desktop E2E (affaan-m/ECC, 276k stars), Windows Desktop E2E (affaan-m/ECC, 276k stars), Agent Adaptive Coordinator (ruvnet/ruflo, 74k stars) and Text Resizing (thedaviddias/Front-End-Checklist, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ben-manes (a GitHub user) maintains it in ben-manes/caffeine, which has 17,881 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 9, 2026.
Source: ben-manes/caffeine on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.