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 JeremyDev87/codingbuddy --skill context-managementgit clone --depth 1 https://github.com/JeremyDev87/codingbuddyWrote 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/jeremydev87/codingbuddy/context-management)<a href="https://agentmods.dev/skills/jeremydev87/codingbuddy/context-management"><img src="https://agentmods.dev/badge/skills/jeremydev87/codingbuddy/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/jeremydev87/codingbuddy/context-management"><img src="https://agentmods.dev/badge/skills/jeremydev87/codingbuddy/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 | $0.00038 | $0.01410 |
| Opus 5.5 | $0.00015 | $0.00564 |
| Sonnet 5.5 | $0.00008 | $0.00282 |
| Haiku 4.5 | $0.00004 | $0.00141 |
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 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Management
Overview
AI assistant context windows are finite. Long tasks get compacted, earlier decisions are forgotten, and continuity breaks. Context management is the practice of preserving critical information so work survives compaction.
Core principle: If it matters after the current conversation, write it down. Memory is the enemy of continuity.
Iron Law:
EXTERNAL STATE > INTERNAL MEMORY
Write decisions to files. Files survive compaction; conversation memory does not.
When to Use
- Starting a task that will span multiple sessions
- Beginning PLAN mode (decisions must survive to ACT)
- Completing ACT mode (progress must survive to EVAL)
- When conversation is approaching context limits
- Before any context compaction event
- When resuming work after a break
The Context Document (codingbuddy)
The primary context persistence mechanism is docs/codingbuddy/context.md.
docs/codingbuddy/context.md
─────────────────────────────
This file is:
- Created/reset by PLAN mode
- Appended by ACT mode
- Appended by EVAL mode
- Always at a fixed, predictable path
- Safe to read at any point
What Goes in Context
PLAN mode writes:
- Task description
- Key design decisions and their rationale
- Architecture choices
- Dependencies and constraints
- Recommended ACT agent
ACT mode writes:
- Progress milestones completed
- Files created/modified
- Implementation decisions made
- Issues encountered and resolved
- Next steps
EVAL mode writes:
- Quality findings (with severity)
- Security issues found
- Performance observations
- Recommendations for next iteration
Context Document Format
# Context: [Task Title]
## PLAN — [timestamp]
**Task:** [Original task description]
**Primary Agent:** solution-architect
**Recommended ACT Agent:** agent-architect
### Decisions
- Decision 1: [What was decided and why]
- Decision 2: [What was decided and why]
### Notes
- Implementation note 1
- Constraint or dependency to remember
---
## ACT — [timestamp]
**Primary Agent:** agent-architect
### Progress
- [x] Created RulesService with search capability
- [x] Added Tests: 12 passing
- [ ] SSE transport implementation pending
### Notes
- Used glob for file discovery (faster than readdir recursion)
- NestJS module structure: McpModule → RulesModule
---
## EVAL — [timestamp]
### Findings
- [HIGH] Missing rate limiting on /sse endpoint
- [MEDIUM] Test coverage at 72%, below 80% target
### Recommendations
- Add rate limiting middleware
- Add tests for error cases in RulesService
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 · 246 lines · 38 tokens per session scan A b0699d2d728a
context-management is a skill published in the GitHub repository JeremyDev87/codingbuddy (31 stars, last pushed 6mo ago), licensed MIT. It adds 38 tokens to every session and 1,410 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 2026-08-30.
Other skills, from other repositories
performance-audit
Full-stack performance health check across 12 dimensions. Rendering, bundles, assets, API/network, algorithms, memory, database, caching, Web Vitals, backend runtime, concurrency, and framework-specific pathologies. Evidence-based Impact Models with confidence tiers and a prioritized optimization roadmap. Switches…
gohm
/gohm - Go Harvest Memory (Knowledge Harvester).
context
Context Manager - Token Budget Controller.
memory
Use this agent to manage permanent memory storage, retrieval weight calculation, and knowledge preservation following NASAB Pillar 5.
recall
/recall — Layered Knowledge Recall.
evolve
/evolve - Framework Evolution from Dev-Memory.