render-review-rows
Automates the assembly of data review reports.
Install
mkdir -p .claude/skills/render-review-rows && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/13555" && unzip -o skill.zip -d .claude/skills/render-review-rows && rm skill.zipInstalls to .claude/skills/render-review-rows
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
1. The filing is the single source of truth. Every fact traces to `source_accession_number` + verbatim `source_text`. 2. Fail closed on source ambiguity. Fail open on extraction incompleteness. 3. Facts before judgments. Proposals are raw events. Classification is Skill 8, not SkKey capabilities
- →Denormalize extraction and enrichment artifacts into a 47-column CSV.
- →Rebuild `master_rows.csv` after manual overrides.
- →Rebuild the global `Data/views/master.csv` after deal changes.
- →Populate 47 columns per a column specification.
- →Derive `bid_note` from `event_type` using a mapping table.
How it works
This skill reads various JSONL and JSON input artifacts, processes them according to a column specification, and outputs a denormalized 47-column CSV file, along with review status and an overrides file.
Inputs & outputs
When to use render-review-rows
- →Rebuilding master review files
- →Aggregating deal extraction data
- →Generating compliance reports
About this skill
render-review-rows
Design Principles
- The filing is the single source of truth. Every fact traces to
source_accession_number+ verbatimsource_text. - Fail closed on source ambiguity. Fail open on extraction incompleteness.
- Facts before judgments. Proposals are raw events. Classification is Skill 8, not Skill 5.
- Alex's collection instructions are the extraction spec. Used for methodology and taxonomy. Never as a factual source.
- master.csv is the review artifact, not the estimation artifact.
Overview
Denormalize all extraction and enrichment artifacts into a 47-column review CSV. Pure mechanical assembly -- no judgment, no classification, no interpretation. If the input artifacts exist, this skill always succeeds.
When To Use
- After Skills 1-8 have completed (orchestrated or independent).
- To rebuild
master_rows.csvafter manual overrides or re-extraction. - To rebuild the global
Data/views/master.csvafter any deal changes.
Input Artifacts
All paths relative to Data/deals/<slug>/:
| Artifact | Source Skill |
|---|---|
extraction/deal.json | 5. extract-events |
extraction/actors.jsonl | 4. build-party-register |
extraction/actors_extended.jsonl | 5. extract-events (if new actors minted) |
extraction/events.jsonl | 5. extract-events |
extraction/event_actor_links.jsonl | 5. extract-events |
enrichment/process_cycles.jsonl | 7. segment-processes |
enrichment/judgments.jsonl | 8. classify-bids-and-boundary |
extraction/audit_flags.json | 6. audit-and-reconcile (optional) |
Output Artifacts
| Artifact | Description |
|---|---|
master_rows.csv | 47-column review CSV (one row per event-actor link) |
review/review_status.json | Review status and flags for this deal |
review/overrides.csv | Header-only CSV for reviewer corrections |
Data/views/master.csv | Global rebuild: all Data/deals/*/master_rows.csv concatenated |
Tools
| Tool | Purpose |
|---|---|
| File read | Read all input artifacts from disk |
| File write | Write master_rows.csv, review_status.json, overrides.csv |
| File search | Find all Data/deals/*/master_rows.csv for global rebuild |
Procedure
1. Read deal.json -> extract Group A (deal header) fields.
2. Read actors.jsonl + actors_extended.jsonl (if exists) -> build actor lookup.
3. Read events.jsonl -> build event list.
4. Read event_actor_links.jsonl -> build link list.
5. Read process_cycles.jsonl -> build cycle/round lookup.
6. Read judgments.jsonl -> build classification lookup.
7. Read audit_flags.json (if exists) -> build review flag inputs.
8. For each event:
a. Look up event-actor links for this event.
b. If links exist: emit one row per link (event + actor columns).
c. If no links: emit one row with blank actor columns.
d. Populate all 47 columns per the column spec.
e. Derive bid_note from event_type using the mapping table.
f. Derive review flags (approximate_date, missing_nda, etc.).
9. Sort rows: deal_slug -> event_date -> event_type priority -> actor_alias.
10. Write master_rows.csv with header + sorted rows.
11. Write review/review_status.json with status, flags, extraction date.
12. Write review/overrides.csv (header only).
13. Rebuild global Data/views/master.csv:
- Find all Data/deals/*/master_rows.csv files.
- Validate headers are consistent across all deals.
- Concatenate all rows sorted by deal_slug.
- Write to Data/views/master.csv.
Gate
master_rows.csv exists in Data/deals/<slug>/.
Failure Policy
Always succeeds if input artifacts exist. This is pure denormalization -- no judgment calls, no ambiguity. If an input artifact is missing, skip its columns (leave blank) and note in review_status.json.
Required Reading
references/column-spec.md-- 47-column specification, sorting rules, bid_note mapping, review flag derivation, global master.csv rebuild
When not to use it
- →When Skills 1-8 have not completed.
- →When judgment, classification, or interpretation is required.
- →When an input artifact is missing and its columns should not be skipped.
Limitations
- →This skill always succeeds if input artifacts exist, even if they are incomplete.
- →It does not perform any judgment, classification, or interpretation.
- →It relies on `source_accession_number` + `source_text` for factual tracing.
How it compares
This skill performs a purely mechanical assembly of data without judgment or classification, ensuring a consistent and interpretation-free aggregation of processed information.
Compared to similar skills
render-review-rows side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| render-review-rows (this skill) | 0 | 5mo | No flags | Intermediate |
| literature-review | 559 | 2mo | Review | Advanced |
| openalex-database | 48 | 7mo | Review | Intermediate |
| market-research-reports | 38 | 7mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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