Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add LearnPrompt/cc-harness-skills --skill structured-context-compressorgit clone --depth 1 https://github.com/LearnPrompt/cc-harness-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/learnprompt/cc-harness-skills/structured-context-compressor)<a href="https://agentmods.dev/skills/learnprompt/cc-harness-skills/structured-context-compressor"><img src="https://agentmods.dev/badge/skills/learnprompt/cc-harness-skills/structured-context-compressor.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.00282 |
| Opus 5 | $0.00018 | $0.00141 |
| Sonnet 5 | $0.00007 | $0.00056 |
| Haiku 4.5 | $0.00004 | $0.00028 |
Grade A, and why
structured-context-compressor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Structured Context Compressor
Use this skill when a free-form summary is too lossy and you need a reliable continuation artifact.
Use It For
- long coding conversations
- session handoff
- agent-to-agent continuation
- preserving user intent under context pressure
Quick Start
Render the standard template:
python3 {baseDir}/scripts/render_template.py
Then apply the prompt in references/prompt-template.md.
Nine Sections
- primary request and intent
- key technical concepts
- files and code sections
- errors and fixes
- problem solving
- all user messages
- pending tasks
- current work
- next aligned step
Highest-Value Rule
Preserve all user messages or an accurate equivalent. Do not compress away the corrections that changed the direction of work.
Supporting Files
- Prompt template: references/prompt-template.md
- Source notes: references/source-notes.md
- Helper script:
python3 {baseDir}/scripts/render_template.py
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 48 lines · 36 tokens per session scan A 1747e3a0333f
structured-context-compressor is a skill published in the GitHub repository LearnPrompt/cc-harness-skills (233 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 282 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
remem-mcp
Long-term memory for coding agents. Auto-applies at the start of any coding task — recall past context before answering, capture decisions/learnings/fixes after work, use CodeGraph instead of grep for symbol lookup. Invoke when you see [remem-mcp] in your context or when starting any non-trivial coding work.
write-terva-lore
Author terva lore. Lore is context that a person authors in files. terva injects it only when it is keyword-relevant, or always-on. Use to create/add/write lore, "world info", a lorebook entry, keyed background, or a book of facts for a character.
handoff
Compact the current session into a handoff document. A fresh session or another agent can pick it up with no need to re-derive context. Use when asked to hand off, hand over, wrap up for another session or agent, or park work mid-stream.
retrospective
Distill the reusable lessons of this session into the durable homes that shape future sessions. The lessons are corrections, conventions you discovered, and traps you hit. The homes are the project or global AGENTS.md, a project skill, and memory when available. Use when asked for a retro, to capture or remember…
create-new-ref
Systematically add a new memory-system reference to AutoMemory's reference hub. Use when analyzing a new agent memory system, adding reference/ /details.md, saving prompts, or updating reference/hub/techniquemap.json.
health-memory
Recognize, store, and recall structured health data across sessions.