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 skills add panjose/Co-Scientist --skill research-overview-pipelinegit clone --depth 1 https://github.com/panjose/Co-ScientistWrote 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/skills/panjose/co-scientist/research-overview-pipeline)<a href="https://agentmods.dev/skills/panjose/co-scientist/research-overview-pipeline"><img src="https://agentmods.dev/badge/skills/panjose/co-scientist/research-overview-pipeline/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/skills/panjose/co-scientist/research-overview-pipeline"><img src="https://agentmods.dev/badge/skills/panjose/co-scientist/research-overview-pipeline.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 | $0.00017 | $0.01280 |
| Opus 5 | $0.00009 | $0.00640 |
| Sonnet 5 | $0.00003 | $0.00256 |
| Haiku 4.5 | $0.00002 | $0.00128 |
Grade A, and why
research-overview-pipeline 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 11d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
research-overview-pipeline
Goal:
- Generate the final research overview from the top-ranked hypotheses.
Inputs:
research_plan/RESEARCH_PLAN.json- top-ranked
hypotheses/<id>/HYPOTHESIS.jsonartifacts with completed review payloads - optional
meta/INSIGHTS_FROM_REVIEWS.json
Outputs:
meta/RESEARCH_OVERVIEW.json- updated
state/PIPELINE_STATE.json - updated
state/CURRENT_STAGE.json
Context Loading:
- Read
research_plan/RESEARCH_PLAN.json. - Read
packages/agent_contracts/pipeline_runtime.pybefore updatingstate/PIPELINE_STATE.jsonorstate/CURRENT_STAGE.json. - Read
state/EVOLUTION_STATE.jsonbefore writing the overview. - Read
literature/queries/*/EVIDENCE_BUNDLE.jsonwhen literature artifacts exist and record whether anyretrieval_metadata.statusispartialorblocked. - Read
state/PROXIMITY_STATUS.jsonwhen it exists and record whether proximity wassucceededor degraded to a skipped/failed fallback state. - Use
research_goalas the synthesis anchor. - Use
preferencesandconstraintsto frame which research directions are worth elevating. - Read the current top-ranked hypothesis artifacts together with their embedded or adjacent review results.
- If
meta/INSIGHTS_FROM_REVIEWS.jsonexists, use it to highlight recurring critique patterns and unresolved risks across the run.
Execution Prompt Contract:
- System Intent:
- You are synthesizing the strongest current hypotheses into a final research overview.
- Required Reasoning Focus:
- Group the strongest candidates into 3-5 coherent research directions or areas where possible.
- Explain why each direction matters relative to the research goal.
- Suggest concrete experiments or next steps for each direction.
- Use review evidence and recurring critique patterns to keep the overview realistic rather than promotional.
- Explain the actual run stop state from
state/EVOLUTION_STATE.json. - When any evidence bundle is
partialorblocked, include a concise literature retrieval limitation note; do not describe the run as having comprehensive literature coverage in that case. - When
PROXIMITY_STATUS.jsonrecords a skipped or failed fallback, include a concise proximity embedding fallback limitation note; do not describe ranking as embedding-, proximity-, or similarity-informed in that case.
- Do Not Do:
- Do not simply concatenate ranked hypotheses.
- Do not ignore major recurring weaknesses surfaced by review.
- Do not produce a vague essay without clear actionable research directions.
- Do not claim that the frontier converged unless
EVOLUTION_STATE.stopReasonis exactlyconvergence_reached. - Do not describe a safety stop, capped stop, or paused state as scientific convergence.
- Do not call partial or blocked literature retrieval comprehensive, complete, exhaustive, or full literature coverage.
- Do not call receipt-gated manual placement embedding-informed, proximity-informed, or similarity-driven ranking.
- Do not omit degraded literature or proximity bridge limitations from a completed overview.
- Output Shape:
- Produce the exact
ResearchOverviewContractfrompackages/agent_contracts/meta_review.py. - When updating
state/PIPELINE_STATE.json, preserve the exactPipelineStateContractfrompackages/agent_contracts/pipeline_runtime.py. - When this synthesis step starts, use
from tools import sync_pipeline_stage_artifactssocurrentPhase = Research Overview,currentSkill = research-overview-pipeline, andstageTrailstay aligned across both state artifacts. - Use
##sections for major directions and###subsections where helpful. - Keep the overview concise and decision-oriented.
- Produce the exact
Execution Steps:
- Open
skills/shared-references/schema-index.md, then readpackages/agent_contracts/meta_review.pyandpackages/agent_contracts/pipeline_runtime.pybefore writingmeta/RESEARCH_OVERVIEW.jsonor updating run-level stage artifacts. - Before synthesizing the overview, call
tools.sync_pipeline_stage_artifacts(run_dir, current_phase="Research Overview", current_skill="research-overview-pipeline"). - Read
state/EVOLUTION_STATE.jsonand record whether the stop reason isconvergence_reached,max_iterations_reached,safety_iteration_limit_reached,no_viable_candidates, or still empty. - Read the research plan and current top-ranked hypothesis artifacts.
- Read run-level literature bundle statuses and proximity status when those artifacts exist.
- Read run-level critique insights if they exist.
- Group the strongest hypotheses into coherent directions.
- Write one synthesized overview that explains importance, evidence, next experiments, and the real stop state. Use convergence language only for
stopReason=convergence_reached; for safety stops use language such as "synthesis after the safety ceiling" rather than "frontier converged". When literature or proximity is degraded, include the limitation in plain language without overstating the pipeline evidence.- If literature retrieval is
partialorblocked, state that external evidence coverage was partial, limited, blocked, or otherwise incomplete. - If proximity embedding is skipped or failed, state that proximity embedding was unavailable/skipped and ranking continued through documented fallback rather than true embedding/proximity-informed placement.
- If literature retrieval is
- Write
meta/RESEARCH_OVERVIEW.json. - Update
state/PIPELINE_STATE.jsonandstate/CURRENT_STAGE.jsonas required by the top-level workflow. - Validate the run artifacts before declaring completion.
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.
- 11d ago First seen · 90 lines · 17 tokens per session scan A 015f8e442a47
research-overview-pipeline is a skill published in the GitHub repository panjose/Co-Scientist (5 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 17 tokens to every session and 1,280 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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