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 yeaight7/agent-powerups --skill context-compressiongit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/context-compression)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/context-compression"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/context-compression/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/context-compression"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/context-compression.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00028 | $0.00630 |
| Opus 5.5 | $0.00011 | $0.00252 |
| Sonnet 5.5 | $0.00006 | $0.00126 |
| Haiku 4.5 | $0.00003 | $0.00063 |
Grade A, and why
context-compression 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Compression
When to use
Use when context approaches limits or response quality degrades (repeated mistakes, forgotten constraints, lost-in-the-middle effects) — typically after many turns of debugging or complex planning.
Requirements / Checks
- Active session contains long stack traces, large file dumps, or multiple failed iterations.
Workflow
-
Diagnose bloat — identify: redundant outputs (same file read multiple times), contradictory instructions that accumulated over turns, constraint forgetting (earlier requirements no longer respected).
-
Draft summary — synthesize the current valid state of the code or plan in 3–5 sentences.
-
Extract anti-patterns — list what failed and why:
"Attempted X → failed because Y". These are critical to prevent the next session from looping on the same mistakes. -
Generate handoff artifact — write the following template to disk:
## Goal [One-sentence objective] ## Current state [What is working / what is in place right now] ## Blocker / next step [The exact next action to take] ## Anti-patterns (do not repeat) - Tried X → failed because Y - Tried A → blocked by B ## Key files - `path/to/file.ts` — [what it does / why it matters] ## Open risks - [Anything uncertain or requiring verification] ## Decisions made - [Key tradeoffs and why alternatives were rejected] -
Restart — tell the user to start a new chat session and attach the handoff file as the first message.
Quality Probes
Before finalizing, verify the handoff can answer:
- Recall: exact commands, errors, versions, decisions that still matter.
- Artifact: files created, modified, inspected, intentionally untouched.
- Continuation: next action, current blocker, validation state, open risks.
- Decision: why key tradeoffs were chosen and what alternatives were rejected.
Safety Constraints
- Do NOT silently drop critical architectural constraints or user preferences during compression.
- Write the handoff document to disk before asking the user to restart.
What ships with it
1 file 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.
- 1mo ago First seen · 76 lines · 28 tokens per session scan A 5964a4017d88
context-compression is a skill published in the GitHub repository yeaight7/agent-powerups (8 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 28 tokens to every session and 630 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-14.
Other skills, from other repositories
persistent-notes
Save notes locally to /mnt/workspace/notes.json file. Use when user wants to "save a note" or "remember something".
session-summaries
What the chat right-panel session summary shows, what it costs, and how to make a session summarize well. Load when the user asks about the session summary panel, why a summary looks wrong or empty, or how to turn it on.
bmad-product-brief
Create, update, or validate a product brief. Use when the user wants help producing, editing, or validating a brief.
bmad-deep-recon
Research a topic to support a decision, three ways: draft a research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), turn a finished research report into a short summary with cited sources that other skills can use directly, or run the research here with parallel web searches.…
bmad-eval
Runs the evals of a BMad skill through the agent harness the user works in and reports what they show: baseline against the bare model, variant against a stripped or prior version, quality against a rubric, and trigger accuracy of the description with an optional optimization loop. Use when the user asks to evaluate…
bmad-advanced-elicitation
Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team.