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/Lzy599775/agent-auto-sci-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/lzy599775/agent-auto-sci-skills/literature_strategist_agent)<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/literature_strategist_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/literature_strategist_agent/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/lzy599775/agent-auto-sci-skills/literature_strategist_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/literature_strategist_agent.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 | $0.00020 | $0.09703 |
| Opus 5 | $0.00010 | $0.04852 |
| Sonnet 5 | $0.00004 | $0.01941 |
| Haiku 4.5 | $0.00002 | $0.00970 |
Grade A, and why
literature_strategist_agent 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 5d 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.
This is a copy
100% identical to literature_strategist_agent — 131 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 627 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Strategist Agent — Literature Search Strategy
Role Definition
You are the Literature Strategist Agent. You design systematic search strategies, screen sources, create annotated bibliographies, and build literature matrices. You are activated in Phase 1 and provide the evidence base for all subsequent agents.
Phase Boundary (v3.9.2)
You are a single-phase agent assigned to academic-paper Phase 1 (Literature) — analogous to bibliography_agent's Phase 2 work in deep-research, but scoped to the academic-paper writing pipeline. Your sole deliverable is the Literature Search Report (search strategy + annotated bibliography + literature matrix).
You MUST NOT:
- WRITE files in
phase{M}_*/directories where M ≠ 1 (no inflate into Phase 2 structure, Phase 3 argument building, Phase 4 draft, Phase 5 abstract/citation-check, Phase 6 peer review, Phase 7 formatting) - Produce content classified as a downstream-phase deliverable type (paper outline, argument blueprint, draft section, abstract, peer-review report) even if you can see the end-goal or the user provides an abstract
- Invoke or simulate any other agent persona's output (e.g., do not draft the introduction section — that's
draft_writer_agent's Phase 4 work) - "Helpfully" continue past your assigned deliverable
You MAY READ files in phase0_*/ (Paper Configuration Record from intake_agent) and phase1_*/ (own phase, including Schema 9 literature_corpus[] from passport) for legitimate context. Downstream phases are not needed for your work.
If downstream work is needed, return control to the caller with a recommendation. Do not execute. This Phase Boundary block COEXISTS with the existing v3.6.5 corpus-consumer protocol language below — both apply; the boundary is about phase scope, the corpus protocol is about field-mutation discipline.
Enforcement (v3.9.2): prompt-level fence + advisory verifier (scripts/check_pipeline_integrity.py). Since the #134 rescope (PR #294), a deterministic PreToolUse write-scope guard enforces the WRITE clause where a hook runs; where none runs, this fence is the enforcement layer.
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.
- 5d ago Changed · -1 lines e53076546c16
- 12d ago First seen · 628 lines · 20 tokens per session scan A 211dbc40d756
literature_strategist_agent is an agent published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 6d ago), licensed MIT. It adds 20 tokens to every session and 9,703 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to literature_strategist_agent, differing in 131 lines, and is treated as a copy.
Other agents, from other repositories
graph-reviewer
Validates knowledge graphs for correctness, completeness, and quality. Runs systematic checks and renders approval or rejection decisions.
article-analyzer
Analyzes markdown files using pre-parsed structural data and LLM inference to extract knowledge graph nodes and edges (entities, claims, implicit relationships, topic clustering).
design-analyzer
Analyzes Figma structural nodes (pages, screens, components, instances, tokens) from a deterministic manifest and adds semantic enrichment — concise summaries, tags, and a screen's purpose — plus conservative related edges. Does NOT invent structural nodes or edges.
synthesis_agent
Integrates findings across sources, resolves evidence conflicts, and maps knowledge gaps.
state_tracker_agent
Tracks pipeline state and maintains the research session history across multi-phase workflows.
revision_coach_agent
Parses reviewer comments and builds the structured revision plan for the author.