research-overview-pipeline

research-overview-pipeline is a skill for Claude Code, Codex from panjose/Co-Scientist. It costs 17 tokens per session (1,280 once invoked), scanned A, original, Apache-2.0.

A pipeline step that turns the best-supported research hypotheses into one final research overview. A hypothesis is a proposed explanation or research direction that has been assessed and ranked.

In plain words
What is it for?
Use it to produce the final overview for a research project, based on its goal, constraints, preferences, and highest-ranked hypotheses.
Why use it?
It removes the need to combine research plans, review results, evidence, and project state by hand. It also records whether evidence retrieval or relatedness checks were incomplete.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to produce the final overview for a research project, based on its goal, constraints, preferences, and highest-ranked hypotheses.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/panjose/co-scientist/research-overview-pipeline
Install

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.

Any agent
npx skills add panjose/Co-Scientist --skill research-overview-pipeline
Clone the repo
git clone --depth 1 https://github.com/panjose/Co-Scientist

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,280 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash 015f8e442a47, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

skills/research-overview-pipeline/SKILL.md · 90 lines

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.json artifacts 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.py before updating state/PIPELINE_STATE.json or state/CURRENT_STAGE.json.
  • Read state/EVOLUTION_STATE.json before writing the overview.
  • Read literature/queries/*/EVIDENCE_BUNDLE.json when literature artifacts exist and record whether any retrieval_metadata.status is partial or blocked.
  • Read state/PROXIMITY_STATUS.json when it exists and record whether proximity was succeeded or degraded to a skipped/failed fallback state.
  • Use research_goal as the synthesis anchor.
  • Use preferences and constraints to 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.json exists, 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 partial or blocked, include a concise literature retrieval limitation note; do not describe the run as having comprehensive literature coverage in that case.
    • When PROXIMITY_STATUS.json records 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.stopReason is exactly convergence_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 ResearchOverviewContract from packages/agent_contracts/meta_review.py.
    • When updating state/PIPELINE_STATE.json, preserve the exact PipelineStateContract from packages/agent_contracts/pipeline_runtime.py.
    • When this synthesis step starts, use from tools import sync_pipeline_stage_artifacts so currentPhase = Research Overview, currentSkill = research-overview-pipeline, and stageTrail stay aligned across both state artifacts.
    • Use ## sections for major directions and ### subsections where helpful.
    • Keep the overview concise and decision-oriented.

Execution Steps:

  1. Open skills/shared-references/schema-index.md, then read packages/agent_contracts/meta_review.py and packages/agent_contracts/pipeline_runtime.py before writing meta/RESEARCH_OVERVIEW.json or updating run-level stage artifacts.
  2. Before synthesizing the overview, call tools.sync_pipeline_stage_artifacts(run_dir, current_phase="Research Overview", current_skill="research-overview-pipeline").
  3. Read state/EVOLUTION_STATE.json and record whether the stop reason is convergence_reached, max_iterations_reached, safety_iteration_limit_reached, no_viable_candidates, or still empty.
  4. Read the research plan and current top-ranked hypothesis artifacts.
  5. Read run-level literature bundle statuses and proximity status when those artifacts exist.
  6. Read run-level critique insights if they exist.
  7. Group the strongest hypotheses into coherent directions.
  8. 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 partial or blocked, 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.
  9. Write meta/RESEARCH_OVERVIEW.json.
  10. Update state/PIPELINE_STATE.json and state/CURRENT_STAGE.json as required by the top-level workflow.
  11. Validate the run artifacts before declaring completion.

Read the full file on GitHub · 90 lines

Changes

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.

  1. 11d ago First seen · 90 lines · 17 tokens per session scan A 015f8e442a47

Subscribe to this mod's changes

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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