Levyra context-efficiency workflow
SkillDocs & knowledgeAutomatically use at the start of any non-trivial Levyra engineering task that needs repository exploration, cross-session memory, and for builds, tests, lint, logs, broad searches, dependency output, Git/GitHub inspection, CI diagnostics, agent setup, or other high-volume work. Reduce token waste t
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Levyra context-efficiency workflow skill
What this skill tells your AI
The instructions your AI receives, as published by luc4n3x/levyra-deepsound in .agents/skills/levyra-context-efficiency/SKILL.md and read by ahel’s review.
Purpose
Spend model context on code and decisive evidence. This skill reduces repeated instructions, broad reads, noisy command output, and stale history. It never replaces domain skills, source inspection, testing, review, or exact diagnostics.
Token savings must come from context and output, never from shallower reasoning. If omitted evidence can change correctness, read or rerun it.
Automatic routing
Route this skill only when the task is likely to create substantial repository or command-output volume. Tiny edits, ordinary explanations, and already-local code changes should not load it just because the prompt says "implement", "modify", "analyze", or "inspect".
Before broad reading:
- identify the architecture owner and the exact question the next read answers;
- search symbol/path/call site first;
- read the smallest useful source/test range;
- expand only when a concrete unanswered question remains;
- do not reread unchanged evidence already in context;
- load only the domain/companion skills that materially affect correctness.
RTK
For shell-capable noisy work, prefer the repository RTK layer after checking it.
Use scripts/ensure-rtk.ps1 -Quiet on Windows or ./scripts/ensure-rtk.sh --quiet
elsewhere. Manual repair remains available through
scripts/setup-ai.ps1 -InstallRtk or scripts/setup-ai.sh --install-rtk.
If RTK is unavailable, continue raw rather than weakening validation.
Useful compact routes include:
rtk gradlew <tasks>
rtk git diff
rtk git status
rtk gh pr view <number>
rtk test <command>
rtk err <command>
rtk grep <pattern> <path>
rtk log <file>
rtk adb logcat -d -t 400
rtk summary adb shell dumpsys <service>
Compact output is not proof of success. Check the command exit status and the authoritative success/failure marker. If compression hides the deciding cause, rerun the exact command raw.
Keep decisive evidence raw
Do not compress away evidence needed for:
- compiler/test/lint failures whose exact diagnostic matters;
- security, redirects, MIME, permissions, secrets, signing, checksums, or trust boundaries;
- Perfetto/thread/frame timing, SQL/query failures, concurrency, or memory root cause;
- R8/Proguard missing-class, mapping, metadata, or release-only failures;
- exact protocol, quoting, encoding, stdout/stderr, or regression reproduction.
For ADB, bound noisy textual output first. Keep tiny control queries raw. Never
wrap binary/payload commands such as adb exec-out screencap -p in a text
compression path.
Cross-session context
Use claude-mem only when earlier-session context materially affects the task and
the runtime exposes it. Retrieve progressively: search -> timeline when
chronology matters -> get_observations for only relevant IDs -> verify against
the current repository. Current code, tests, CI, runtime evidence, and owner
decisions always outrank memory.
Fail open: if memory tooling is unavailable, unhealthy, or unsupported, continue ordinary engineering without it. Do not enable cloud sync or semantic injection implicitly. For ChatGPT, Repository configuration alone cannot make ChatGPT reach a local claude-mem worker.
If a shell-capable runtime genuinely needs the optional integration and it is
missing, one bounded setup attempt may use scripts/setup-ai.ps1 or
scripts/setup-ai.sh. Manual forcing remains scripts/setup-ai.ps1 -ClaudeMem
or ./scripts/setup-ai.sh --claude-mem. Failure must not block ordinary work.
Never store or retrieve secrets, tokens, cookies, keystores, private URLs,
.env, or local.properties through project memory.
Long-task checkpoints
Carry forward only the verified goal, root cause/decision, affected files or symbols, preserved behavior, current edit state, validation results, real blockers, and one next action. Drop superseded logs and disproved hypotheses. A compact handoff never replaces source-of-truth evidence.
Safety
- Do not trade correctness, review depth, or testing for a smaller context window.
- Do not enable
danger-full-access, approval bypasses, or unrestricted sandboxing. - Do not install unrelated plugins or broad system upgrades.
- Do not let RTK or memory hide security/signing/runtime evidence.
- Never infer permission to commit, push, open/merge a PR, tag, release, or deploy.
Validation
After changing this workflow or its routing, run:
python3 scripts/validate_claude_mem.py
python3 scripts/validate_agent_config.py
python3 scripts/validate_ai_efficiency.py
python3 scripts/evaluate_skill_routing.py
On Windows use py for the same scripts when appropriate.
Signals
- GitHub stars
- 164
- Forks
- 1
- Last commit
- Sep 2026
Advanced
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levyra-context-efficiency- Source
- github.com/luc4n3x/levyra-deepsound