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 implicit-labs/autosymph --skill context-managementgit clone --depth 1 https://github.com/implicit-labs/autosymphWrote 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/implicit-labs/autosymph/context-management)<a href="https://agentmods.dev/skills/implicit-labs/autosymph/context-management"><img src="https://agentmods.dev/badge/skills/implicit-labs/autosymph/context-management/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/implicit-labs/autosymph/context-management"><img src="https://agentmods.dev/badge/skills/implicit-labs/autosymph/context-management.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 | $0.00056 | $0.02005 |
| Opus 5.5 | $0.00022 | $0.00802 |
| Sonnet 5.5 | $0.00011 | $0.00401 |
| Haiku 4.5 | $0.00006 | $0.00200 |
Grade A, and why
context-management 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 2mo 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Management for Long-Running Agents
Prevent context compaction from silently corrupting your data. This skill teaches three layers of defense: file-based handoff, size awareness, and context threshold management.
When to Use
- During verify runs that capture multiple screenshots or videos
- When base64-encoding files for Linear upload
- When test output exceeds a few hundred lines
- When a session is running long (35+ turns)
- When tool results start showing "persisted to disk" messages
- Any time you hold large data in context across API turns
Layer 1: File-Based Handoff
The core rule: Never hold large data in context across API turns. Write it to disk, then read it back in the SAME turn you need it.
Context compaction triggers at ~80% window fill. Any data in older turns is fair game for truncation — silently. You won't get a warning. Your 18,984-char base64 becomes 8,498 chars and the upload produces a corrupt file.
Pattern: Write → Read-in-Same-Turn → Consume
# Step 1: Generate data and write to disk (ONE Bash call)
sips -Z 800 -s format jpeg input.png --out output.jpg 2>/dev/null && \
base64 -i output.jpg > output.b64 && \
wc -c < output.b64
# Step 2: Read file back (in the SAME response as Step 3)
cat output.b64
# Step 3: Consume immediately (SAME API turn as Step 2)
mcp__linear__create_attachment(
issue: "ISSUE-XXX",
base64Content: <content from cat>,
filename: "output.jpg",
contentType: "image/jpeg",
title: "Description"
)
Steps 2 and 3 MUST be in the SAME response. If they're in different API turns, context compaction may truncate the base64 between them.
When to Use Files vs Inline
| Data Type | Size | Action |
|---|---|---|
| Base64 (any) | >10K chars | Write to .b64 file |
| Base64 (any) | <10K chars | OK inline if consumed same turn |
| JSON payload | >50 lines | Write to .json file |
| Test output | >200 lines | Pipe to file: swift test 2>&1 | tee test-output.txt |
| Screenshot PNG | Any | Always resize first (see Size Thresholds) |
| Video | Any | Always file-based (too large for context) |
| Git diff | >100 lines | Write to file, read relevant sections |
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.
- 2mo ago First seen · 199 lines · 56 tokens per session scan A 1ba52d9b4700
context-management is a skill published in the GitHub repository implicit-labs/autosymph (5 stars, last pushed 2mo ago), licensed MIT. It adds 56 tokens to every session and 2,005 once invoked, about $0.0002 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 2026-08-31.
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