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 agents/affaan-m/agentshield/readergit clone --depth 1 https://github.com/affaan-m/agentshieldWhat 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 | $0.00013 | $0.00045 |
| Opus 5 | $0.00006 | $0.00023 |
| Sonnet 5 | $0.00003 | $0.00009 |
| Haiku 4.5 | $0.00001 | $0.00005 |
Grade A, and why
expensive-reader 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 yesterday.
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.
What it actually says
Search the codebase for patterns and report findings.
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.
- yesterday First seen · 9 lines · 13 tokens per session scan A 865cd6d8327b
expensive-reader is an agent published in the GitHub repository affaan-m/agentshield (1,103 stars, last pushed 1mo ago), licensed MIT. It adds 13 tokens to every session and 45 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 2026-08-30.
Other agents, from other repositories
parallel-dispatcher
Use when a Compound V manifest (or a plan with a verified Partition Map) is ready to execute and you want to offload the batched, manifest-driven, multi-backend parallel dispatch. Refuses to start if partition-reviewer did not return PASS or if no audit context exists. Runs the git-derived scope gate after every job…
partition-reviewer
Use when a Compound V manifest (or a plan with a Partition Map) is ready and you need to verify its partition is genuinely disjoint and its invariants hold BEFORE executing parallel dispatch. Runs compound-v-validate-manifest.py as the deterministic backing gate, then returns PASS or FAIL with specific violations…
spec-reviewer
Use to run Compound V's three-pass Review Gate. Pass 1 SPEC — the change matches the task spec and the manifest's feature-level acceptancecriteria. Pass 2 QUALITY — code quality, no regressions, no fabricated metrics. Pass 3 INTEGRATION — cross-job seams hold and the build is green. DONE is gated on all three passing.…
domain-expert
Use when a brainstorming spec has any user-facing or domain-specific surface — payments, auth, healthcare, localization, mapping, astrology, LLM/AI features, regulated data, anything where domain knowledge or regulatory rules apply. Skip only for pure internal plumbing (build config, lint rules, dev tooling). Catches…
code-archaeologist
Use when a brainstorming spec touches existing code — middleware, auth, credentials, session, shared-state variables, mode/server/auth-type branching, "path like X but for Y" patterns, or external APIs. Skip when greenfield in a new directory, pure UI, or copy/config edits.
doc-validator
Use when a brainstorming spec names or implies any library, SDK, framework, language version, or external API — almost always. Skip only when the spec has zero technical dependencies (pure prose/UX copy). Catches abandoned libraries, version drift, and outdated API signatures the LLM's training data missed.