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 agentmods add skills/lollipopkit/cc-plugins/context-firewallnpx skills add lollipopkit/cc-plugins --skill context-firewallgit clone --depth 1 https://github.com/lollipopkit/cc-pluginsWhat 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 | $0.00102 | $0.00948 |
| Opus 5 | $0.00051 | $0.00474 |
| Sonnet 5 | $0.00020 | $0.00190 |
| Haiku 4.5 | $0.00010 | $0.00095 |
Grade A, and why
Context Firewall 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 2d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement a Context Firewall workflow to prevent the master context from being flooded by large inputs.
Purpose
Route large inputs (big files, logs, PDFs/images, long MCP/tool outputs) through sub-agents that produce compressed, auditable results. Only pass back:
- Structured claims
- Evidence locators (line/symbol/tool-call)
- Coverage statements
When to use
Prefer this workflow when any of the following is true:
- The user wants the "full" content of a large file/log/document.
- Tool output is long/noisy (search results, crawls, API responses).
- The task needs multi-file scanning or cross-file synthesis.
- The output must be verifiable without re-reading everything.
Core contract (Evidence Contract)
For every meaningful claim:
- Attach at least one
evidenceitem. - Use a locator that enables low-cost verification.
Locator preference order:
line_range(default)symbol_range(functions/classes)tool_call(tool-derived facts; include args hash and rerun hint when safe)byte_range/json_path/stack_signature(define, but treat as harder to verify in v1)
Keep quotes short:
- Respect the
quote_max_charsconstraint. - Never paste large raw blobs.
Recommended workflow
1) Generate TaskSpec
Create a TaskSpec.v1 that is explicit about:
- Objective
- Inputs (files/tool calls)
- Questions
- must_cover checklist
- Constraints (budget + evidence requirements)
- Risk level
Prefer using the command:
/cf-specto generate a schema-valid template.
2) Run Map-Reduce preprocessing
Use /cf-run to:
- Split inputs into shards
- Run
cf-fileworkeron each shard in parallel - Merge with
cf-aggregatorinto a finalSubResult.v1
Hard requirements for sub-agents:
- Output strict JSON only (no markdown fences, no extra commentary).
- Ensure every claim includes evidence.
3) Verify cheaply
Use /cf-verify to sample-check claims:
- Choose sampling rate by
risk_level. - Re-read only the referenced snippets.
- Produce
VerifyReport.v1.
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.
- 2d ago First seen · 131 lines · 102 tokens per session scan A 4fe311097fc0
Context Firewall is a skill published in the GitHub repository lollipopkit/cc-plugins (7 stars, last pushed 5mo ago), licensed MIT. It adds 102 tokens to every session and 948 once invoked, about $0.0005 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 2026-08-31.
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