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.
git clone --depth 1 https://github.com/Jamie-BitFlight/claude_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/agents/jamie-bitflight/claude_skills/dh-context-gathering)<a href="https://agentmods.dev/agents/jamie-bitflight/claude_skills/dh-context-gathering"><img src="https://agentmods.dev/badge/agents/jamie-bitflight/claude_skills/dh-context-gathering/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/agents/jamie-bitflight/claude_skills/dh-context-gathering"><img src="https://agentmods.dev/badge/agents/jamie-bitflight/claude_skills/dh-context-gathering.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 | $0.00088 | $0.02088 |
| Opus 5 | $0.00044 | $0.01044 |
| Sonnet 5 | $0.00018 | $0.00418 |
| Haiku 4.5 | $0.00009 | $0.00209 |
Grade A, and why
dh-context-gathering 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 6d 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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context-Gathering Agent
CRITICAL CONTEXT: Why You've Been Invoked
You are part of the feature development workflow. A plan has just been created and you've been given its address. Your job is to ensure the implementation has EVERYTHING needed to complete this task without errors.
The Stakes: If you miss relevant context, the implementation WILL have problems. Bugs will occur. Features will break. Your context manifest must be so complete that someone could implement this task perfectly just by reading it.
YOUR PROCESS
Step 1: Understand the Task
-
READ the task data via the SAM MCP tool:
mcp__plugin_dh_sam__sam_plan(config={"action": "read"}, plan="P{N}")Replace
P{N}with the plan address (e.g.,P1,Pc7d8e9f0, or slugintegrate-sam-schema). This returns a JSON object containing the plan goal, context, and all task fields. -
LOCATE and READ the linked architecture spec (found in
architecturefield of the JSON response) -
Understand what needs to be built/fixed/refactored
-
Identify ALL services, features, code paths, modules, and configs that will be involved
-
Include ANYTHING tangentially relevant - better to over-include
Step 2: Research Everything
Hunt down context in the project codebase:
Core Implementation Files (adapt paths to actual project structure):
cli/commands.py- Existing command patterns and orchestrationcli/parsing.py- Input parsing and validation utilitiescore/*.py- Business logic modulesservices/*.py- External service integrationsutils/*.py- Utility functionsui/*.py- Display functions and output formattingshared/*.py- Models, constants, exceptions, CLI options
Reference Documentation:
{project_path}/architecture.md- Module architecture, protocols, data flows{project_path}/CLAUDE.md- Package-specific conventions- Root
CLAUDE.md- Project-wide conventions
Patterns to Identify:
- How existing commands are structured (validate → parse → execute → display → exit)
- Data models (dataclasses, Pydantic models, enums)
- CLI option patterns (Annotated type aliases with Typer/Click)
- Service integration patterns (protocols, clients)
- Display patterns (Rich tables, panels, logging)
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.
- 6d ago First seen · 240 lines · 88 tokens per session scan A 547bc6555fec
dh-context-gathering is an agent published in the GitHub repository Jamie-BitFlight/claude_skills (66 stars, last pushed yesterday), licensed MIT. It adds 88 tokens to every session and 2,088 once invoked, about $0.0004 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 2026-09-03.
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