context-optimization
Skill5 starsChanged 6 months agoMITView on GitHub
Lifecycle context audit/optimize/plan. Audit mode (Phase 0..6) + Guide mode (catalog lookup). Targets long-conv agents, RAG pipelines, sub-agents, large-doc workflows. Detects bloat/dedup/cache/masking/partition opportunities. Emits findings (SAFE/REVIEW/BREAKING) + optimization plan + token delta. Read-only — LGTM if sound. Triggers: 'optimize context', 'compact context', 'auditar contexto', 'otimizar contexto', '/context-optimization'.
24 other projects use a version of this.
Adopted by
Most recently taken up by
- mansourvery-hub/skillstheir own version, first seen Nov 1, 2026
- cassandre60/skillstheir own version, first seen Nov 1, 2026
- docxology/templatetheir own version, first seen Oct 30, 2026
- Anmolop97/Agent-Skillstheir own version, first seen Oct 20, 2026
- AbdelrhmanAlquran/claude-skillstheir own version, first seen Oct 20, 2026
- xfstudio/skillstheir own version, first seen Oct 13, 2026
Used with
Projects with it also use bdi-mental-states, long-horizon-prompting and 10 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.
Downey6689Skillbdi-mental-states
Transforms external RDF context into BDI mental states. Invoke when modeling beliefs, desires, and intentions.
Also in 7 projects that use this
DVwebBiz11Skilllong-horizon-prompting
This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.
Also in 5 projects that use this
Downey6689Skillhosted-agents
Guidance for hosted/sandboxed agents. Invoke when running background agents in isolated VMs or multi-client setups.
Also in 6 projects that use this
DVwebBiz11Skillself-improvement-loops
This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mechanism that produces context is versioned and evolved. Route governance of a single autonomous loop (locked surfaces, durable logs, rollback, novelty gates, approval boundaries) to harness-engineering, measurement and quality-gate design to evaluation, judge design to advanced-evaluation, and remote sandbox infrastructure to hosted-agents.
Also in 4 projects that use this
AbdelrhmanAlquranSkilllatent-briefing
This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.
Also in 4 projects that use this
rohunvoraSkilladvanced-evaluation
Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, comparing model outputs, or establishing quality standards for AI-generated content.
Also in 6 projects that use this
- +1 more from Downey6689
Other versions24 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 24
- muratcankoylan/Agent-Skills-for-Context-Engineering★ 18ksame name
- brake71/1c-ssl-skills★ 85same name
- VoDaiLocz/kilo-kit-mcp★ 28same name
- Fandry96/k3-agentic-skills★ 27same name
- docxology/template★ 23same name
- AbdullahMalik17/Digital-FTE★ 12same name
- AbdullahMalik17/Hacathan_5★ 9same name
- AbdullahMalik17/My_skills★ 9same name
- xfstudio/skills★ 9same name
- rohunvora/my-claude-skills★ 6same name
14 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository17 other files it gives its agents.
- celebrimborSkill
- ci-cd-generatorSkill
- clean-codeSkill
- code-debuggerSkill
- code-optimizationSkill
- code-reviewSkill
- code-security-reviewSkill
- github-commitSkill
- github-pr-reviewSkill
- github-release-noteSkill
- mcp-builderSkill
- obsidian-basesSkill
- obsidian-brainSkill
- obsidian-cliSkill
- obsidian-markdownSkill
- only-planSkill
- prompt-engineeringSkill
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 1 is distinctive, and 3 are very specific to this project.
0
Common
Most of the 22 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 51
No degradation signal reported
- Very specific: line 93
When harness ≠ agnostic, Block 3 emits concrete commands per HARNESS_NOTES.md: Claude Code /compact, Codex session.compaction, Antigravity …
- Very specific: line 97
If the audited configuration has a version field, Block 3 emits a SemVer hint: PATCH (only SAFE), MINOR (≥ 1 REVIEW), MAJOR (≥ 1 BREAKING).
- Very specific: line 103
Never compact a never/must not/do not rule.
“Common” is measured: kept by at least 6 in 10 of the 22 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.
Files
Context Optimization Lifecycle
Audits and plans optimization for an LLM context (system prompt, tool defs, message history, retrieved chunks, tool outputs). Two modes, classified at Phase 0.2: Audit (Phase 0..6 lifecycle → 5-block report) when a session/context is pasted; Guide (catalog lookup §1..§13) when invoked as a knowledge source. Read-only — never auto-applies. Evidence-first: every finding cites a measurable category in the inventory or a configuration line. Zero-findings is a valid LGTM.
When to Use
- Long-conversation agents (50+ turn sessions; cache miss + tool-output bloat + late compaction).
- RAG pipelines (top-K stuffing without re-rank, duplicate chunks, prompt-injection via chunk content).
- Sub-agent orchestrators (parent window blowup, per-child unbounded budgets, cost-tier mis-routing).
- Large-doc workflows (single-shot > 100k, no caching across runs, output-token cap clipping synthesis).
- Skill-prompt context (whole-skill payload eats budget on every load).
Trigger: user pastes session/RAG template, statusline shows ≥ 80% utilization, or another skill cross-links @context-optimization.
Do not use when
- Audit target is a single prompt (clarity, refusal hooks, schema) — use
@prompt-engineering. - User wants response brevity — out of scope; this skill compresses input (context), not output.
- User wants project-specific skills or agents generated — use
@celebrimbor. - Phase 0.5 triage gate fires (trivial context).
- Domain has stricter compliance than the skill can verify (legal hold, audit trail).
Prerequisites
| Input | Required | How to obtain |
| Context inventory (categories + sizes) | Yes | User pastes, or bash scripts/run-all.sh <project> (invokes ctxopt Rust binary) |
| Use case | Yes | long-conv-agent / rag-pipeline / sub-agent-orchestrator / large-doc / skill-prompt |
| Target harness | No | claude-code / codex / opencode / antigravity / agnostic — drives §13 plan output |
| Current utilization (%) | No | Estimate via ctxopt count <project> or harness statusline |
| Known degradation signal | No | Quality drop, cost spike, latency — focuses the audit |
Skill never sends context to another model. Findings derived from inventory against TECHNIQUES.md catalog.
Phase 0 — Capture & Classify
Capture inventory verbatim (Phase 0.1). Classify mode + use case + harness on three axes (Phase 0.2). Bound against use-case ceiling (Phase 0.3). Honest audit pledge — see CHECKLIST §Skill self-audit (Phase 0.4). Then triage gate:
0.5 Triage gate
Drop the audit when all three hold:
- Estimated context < 8000 tokens
- Window utilization < 50%
- No degradation signal reported
→ emit Block 1 (inventory) + Block 2 row (out of scope: trivial context — audit overhead exceeds value) + stop.
Otherwise, proceed to Phase 1.
Phase 1 — Inventory
Break inventory into five categories (system prompt · tool defs · message history · retrieved chunks · tool outputs) with token estimate per category. Top category > 50% = top-of-mind finding. Evidence-based audit invokes the ctxopt Rust binary (built at install time):
bash scripts/run-all.sh <project_root>
# or directly:
scripts/bin/ctxopt run-all <project_root> --format toonOutputs out/findings.toon, out/summary.{toon,json}, out/report.md. Detailed checklist: CHECKLIST Phase 1.
Phase 2 — Cost & Quality Audit
Walk cost levers (cache hit rate · dedup ratio · masking opportunity · partition candidates · harness primitive coverage) against the inventory. Each lever absent or weakly applied is a finding with category-cite. Severity per SEVERITY_RUBRIC.md. Heuristic findings start Medium; promotion requires manual confirmation.
Phase 3 — Technique Selection
Walk TECHNIQUES.md §1..§13. Use case dispatches the subset (see TECHNIQUES §How the skill uses):
| Use case | Required techniques |
long-conv-agent | §1, §2, §3, §4, §6, §9, §13 |
rag-pipeline | §1, §3, §6, §8, §9, §10, §11 |
sub-agent-orchestrator | §1, §7, §9, §11 |
large-doc | §1, §5, §7, §9, §11, §12 |
skill-prompt | cross-link @prompt-engineering for single-prompt audit |
Common context-bloat smells (quick-detection): see TECHNIQUES §Common context-bloat smells.
Phase 4 — Plan Recommendations
Plan ordered by ROI (highest token-cost or quality-impact first). Canonical skeleton per use case in USE_CASES.md §1–§4. Generic order: cache layer (§1+§9) → compression (§2/§3/§4/§5/§6) → architecture (§7) → RAG-specific (§8/§10) → cost (§11/§12) → harness (§13).
4.1 Harness-specific plan output
When harness ≠ agnostic, Block 3 emits concrete commands per HARNESS_NOTES.md: Claude Code /compact, Codex session.compaction, Antigravity manual UI re-prompt. A plan that says "use compaction" without naming the harness primitive is itself a finding.
4.2 Living-context versioning
If the audited configuration has a version field, Block 3 emits a SemVer hint: PATCH (only SAFE), MINOR (≥ 1 REVIEW), MAJOR (≥ 1 BREAKING). Soft-spec; skips when no version.
Phase 5 — Token Economy
5.1 Compression rules
Never compact a never/must not/do not rule. Never drop a refusal hook during compaction. Never paraphrase citation-bound text (use §6 verbatim deletion). Always dedupe (§10) before compacting (§2). Always preserve verbatim when surviving content is citation-bound. Detailed checklist: CHECKLIST Phase 5.
5.2 Reporting the delta
Estimate before/after token count. Report signed delta + cache hit projection + invariants preserved (safety rules, refusal hooks, citation chunk_ids).
5.3 Token budget per use case
The plan respects a per-use-case ceiling. Excess → finding T-001 Token budget exceeded (REVIEW).
| Use case | Ceiling (after plan applied) |
long-conv-agent | ≤ 100k tokens (50% Sonnet/Opus 200k window) |
rag-pipeline | stable prefix ≤ 8k + dynamic ≤ 32k |
sub-agent-orchestrator | parent ≤ 30k; each child ≤ 30k |
large-doc | ≤ 200k (single-shot, no growth) |
skill-prompt | ≤ 800 tokens (aligns with prompt-engineering 5.3) |
| Other | ≤ 50% of model window |
User can override with explicit rationale; without override, T-001 fires.
5.4 Cache-friendly ordering
Position stable (system prompt, tool defs, schema, refusal hooks, few-shot) before dynamic (user input, retrieved chunks, conversation history). Anthropic prompt caching reuses prefix tokens with 5min/1h TTL; OpenAI auto-caches prefixes ≥ 1024 tokens. Reordering converts repeated tokens into cache hits — without §9, §1 caches only the leading stable prefix until the first dynamic byte.
Phase 6 — Output
Five-block report. Block 5 conditional (REQUIRED if overall risk = REVIEW or BREAKING; OPTIONAL if SAFE).
| Block | Content |
| 1 | Original context inventory (verbatim, fenced) |
| 2 | Findings table + per-finding detail (id, severity, confidence, risk, category-cite, fix, technique §N) |
| 3 | Optimization plan (ordered by ROI; harness-specific commands per HARNESS_NOTES.md when harness ≠ agnostic) |
| 4 | Summary table (cost lever coverage / token delta / cache-hit projection / risk tag / confidence) |
| 5 | Verification suggestions |
Worked example + verbatim output template: EXAMPLE.md. Zero-findings contract + LGTM rules: CHECKLIST §Skill self-audit.
Constraints
Three load-bearing rules (full 15-rule list in CHECKLIST §Skill self-audit):
| Rule | Why |
| Never apply the plan automatically | Reviewer not optimizer — Block 3 = proposal |
| Never strip a safety rule from system prompt during compaction | never/must not/do not/refuse if survive every compaction |
| Always emit Block 1 verbatim | Inventory contract; second auditor must reach same verdict |
Related Skills
@prompt-engineering— primary sibling. Audits single prompts; this skill audits whole context. Run@prompt-engineeringfirst to fix the prompt; then/valarmindskills:context-optimizationto optimize the surrounding context.@celebrimbor— forges project-specific skills, agents, and tool scripts.@code-security-review— pattern source forscripts/architecture.
References
- TECHNIQUES — §1..§13 catalog (cache, compression, architecture, RAG, cost, tool-specific)
- USE_CASES — canonical skeletons + findings catalogs for the four primary classes
- CHECKLIST — copy-paste cheat sheet by phase + skill self-audit
- SEVERITY_RUBRIC — Severity × Category matrix and risk-tag rubric
- HARNESS_NOTES — Claude Code / Codex / OpenCode / Antigravity primitives
- EXAMPLE — long-conv-agent worked example with verbatim output template
scripts/— evidence-based audit tools (scripts/README.md)
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