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/qgolem/orc/adr-contextnpx skills add qGolem/orc --skill adr-contextgit clone --depth 1 https://github.com/qGolem/orcWhat 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 | $0.00014 | $0.00569 |
| Opus 5 | $0.00007 | $0.00284 |
| Sonnet 5 | $0.00003 | $0.00114 |
| Haiku 4.5 | $0.00001 | $0.00057 |
Grade A, and why
adr-context 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADR Context Loader
Load relevant architectural decisions from docs/adr/ before implementing or verifying tasks.
Process
- List all ADRs —
Glob docs/adr/**/*.md - Extract metadata — Read title (
# ADR-XXX: ...) and status line from each file - Filter by topic — Identify ADRs relevant to the caller's task topic (passed as
$ARGUMENTS) - Load relevant ADRs — Read full content of matching ADRs
- Output summary — Report which ADRs apply, their decisions, and constraints to respect
Implementation Steps
Step 1: Discover all ADRs
Glob pattern: docs/adr/**/*.md
Step 2: Read metadata
For each ADR file:
- Read the first line containing
# ADR-XXX:to extract title - Read the
## Statussection to determine status (Accepted, Rejected, Superseded, etc.)
Step 3: Filter by relevance
Given the task topic from $ARGUMENTS, identify ADRs that match by:
- Title keywords (e.g., "auth" matches "Authentication system architecture")
- Filename patterns (e.g.,
ADR-001-*.md) - Status (only include Accepted or superseded-but-still-relevant ones)
Step 4: Load and return content
For each relevant ADR:
- Read the full file
- Include in output summary
Step 5: Output summary format
## Relevant ADRs for [task topic]
### ADR-XXX: [Title]
- **Status**: [Accepted/Superseded]
- **Key Decision**: [one-liner]
- **Constraints to respect**:
- Constraint 1
- Constraint 2
- **Full decision context**: [excerpt or link]
---
### Constraints Summary
- [List all constraints from all ADRs]
Example
If task topic is "auth", and docs/adr/ contains:
ADR-001-authentication-strategy.md→ MATCHADR-002-database-schema.md→ no matchADR-003-oauth-provider-selection.md→ MATCH
Output would include ADR-001 and ADR-003 with their decisions and constraints.
Output to Caller
Return the summary to the caller's context so they can see:
- Which ADRs apply to their task
- What architectural decisions have been made
- What constraints must be respected during implementation/verification
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 · 76 lines · 14 tokens per session scan A 634315d51954
adr-context is a skill published in the GitHub repository qGolem/orc (5 stars, last pushed 5mo ago), licensed MIT. It adds 14 tokens to every session and 569 once invoked, about $0.0001 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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