| name | context-management |
| description | **UTILITY SKILL** — Two-mode context-window management. RUNTIME: artifact compression (full/summarized/minimal) used by orchestrator and codegen agents. AUDIT: post-mortem analysis of Copilot debug logs (token profiling, redundancy + hand-off gap detection) used by 11-Context Optimizer. WHEN: "context optimization", "token budget", "runtime compression", "log parsing". DO NOT USE FOR: infra, IaC code, deployments. |
| compatibility | Audit mode requires Python 3.14 for log parser script |
Context Management Skill
Unified context-window management with two distinct lifecycles:
- Runtime Compression — what an agent does before loading a large
artifact to stay under the model context limit (during workflow execution).
- Diagnostic Audit — what the 11-Context Optimizer agent does
after the fact to find waste in agent definitions, instructions, and
skill loads.
The two modes do not depend on each other — pick the section that matches
your need.
Mode A: Runtime Compression
Replaces the legacy context-shredding skill.
When to Use Runtime Compression
- Before loading a predecessor artifact file (01 through 07)
- When conversation length suggests >60% of model context is used
- When an agent needs to load multiple large artifacts
Compression Tiers
| Tier | Context Usage | Strategy |
|---|
full | < 60% | Load entire artifact — no compression |
summarized | 60-80% | Load key H2 sections only |
minimal | > 80% | Load decision summaries only (< 500 chars) |
Hard Token Checkpoints
Percentages are advisory; absolute input-token counts override them.
GPT-5.6-Terra and Luna hard-checkpoints at ≥300K input; Claude Opus 5 at ≥160K. When
hit, emit a compaction message and switch every further read to the
minimal tier. Full per-model table, checkpoint procedure (4 steps), and
background context (nordic-foods saturation event) in
references/hard-checkpoints.md.
Rules
- Estimate context usage — count approximate conversation tokens
- Select tier based on the thresholds above
- Apply compression template from
references/compression-templates.md
- If loading multiple artifacts, compress the older / less-critical ones first
Steps
1. Estimate current context usage (rough: 1 token ≈ 4 chars)
2. Check model limit (Claude family: 200K, GPT-5 family: 400K)
3. Calculate usage percentage and check hard-checkpoint table
4. Select tier:
< 60% → full (no compression needed)
60-80% → summarized (key sections only)
> 80% → minimal (decision summaries only)
5. Load artifact/skill using the appropriate variant
Skill Loading
Skills are single-tier — one file per skill, no digest / minimal variants.
Load each SKILL.md only once per session; defer references/*.md until
the SKILL.md body explicitly points to one. Full protocol in
references/skill-loading.md.
Mode B: Diagnostic Audit
Replaces the legacy context-optimizer skill.
Structured methodology for auditing how GitHub Copilot agents consume their
context window. Identifies waste, recommends hand-off points, and produces
prioritised optimisation reports.
When to Use Diagnostic Audit
- Auditing context-window efficiency across a multi-agent system
- Identifying where to introduce subagent hand-offs
- Reducing redundant file reads and skill loads
- Optimising instruction file
applyTo glob patterns
- Profiling per-turn token cost from debug logs
- Porting agent optimisations to a new project
Audit Capabilities & Prerequisites
Capabilities cover log parsing, turn-cost profiling, redundancy detection,
hand-off gap analysis, instruction audit, and structured report generation.
Prerequisites: Python 3.14, VS Code Copilot Chat debug logs, and
.github/agents/*.agent.md (or equivalent). Full capability matrix,
portability checklist, and debug-log discovery in
references/audit-setup.md.
Analysis Methodology
For the complete methodology — log format reference (ccreq line parsing,
request types, latency heuristics), Steps 1-5 (log parsing → optimisation
recommendations), common optimisation patterns, and baseline comparison
workflow (Phase 0 + Phase 6) — read
references/analysis-methodology.md.
Report Template
See templates/optimization-report.md
for the full output template.
Reference Index
Load on demand:
| Reference | Mode | When to Load |
|---|
references/compression-templates.md | Runtime | Per-artifact H2 sections per tier |
references/hard-checkpoints.md | Runtime | Hitting a model token threshold or wiring agent checkpoint logic |
references/skill-loading.md | Runtime | Multi-skill loads / clarifying single-tier load protocol |
references/token-estimation.md | Audit | Estimating token counts for context optimisation |
references/analysis-methodology.md | Audit | Log format, 5-step methodology, optimisation patterns, baseline comparison |
references/audit-setup.md | Audit | Prerequisites, enabling debug logs, audit capabilities, portability |
scripts/parse-chat-logs.py | Audit | Log parser producing structured JSON |
templates/optimization-report.md | Audit | Report output template |
