findings-audit
Audit a project's empirical findings against external evidence — anecdotes (news, interviews, court opinions) and aggregate reports (CNJ, IPEA, OECD, FMI).
Skill by hsigstad
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/hsigstad-findings-audit
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About
Use cases: security, writing
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/findings-audit
What's inside
- Skill name
findings-audit- Description
- Audit a project's empirical findings against external evidence — anecdotes (news, interviews, court opinions) and aggregate reports (CNJ, IPEA, OECD, FMI). Flags counter-evidence to load-bearing interpretations and surfaces patterns no current finding addresses. Use when the user wants a reality check on findings, before writing up, or after a corpus refresh.
- License
- MIT
- Instructions
- 439 lines in SKILL.md · view on GitHub
Agent Skills spec issues
- files: referenced but missing: references/news/stories.csv
Files (3)
- SKILL.md
- open-ended-mode.md · reference/
- output-schemas.md · reference/
SKILL.md (first lines)
# /findings-audit — Audit findings against external evidence
Reality-check a project's empirical findings against external evidence the
project has already collected. The audit is **asymmetric by design**: it looks
for counter-evidence to load-bearing interpretations, not confirmation.
Pro-evidence is over-selected (interpretations were authored having seen
supportive evidence) — what tests them is contradictions.
External evidence comes in two families and four source types:
| Family | Source type | Typical home | Bias |
|-----------|--------------------|-----------------------------------------------|---------------------------------------------------|
| Narrative | `news` | `references/news/stories.csv` | Over-samples unusual / contested / extreme events |
| Narrative | `interview` | `references/interviews/*.md` | Subject self-selection; contested-case salience |
| Narrative | `court-opinion` | `references/cases/*.md` | Appellate / published-decision over-representation |
| Aggregate | `aggregate-report` | `references/reports/*.pdf`, `references/cnj/` | Methodology-bound; definitional gaps |
The procedure is the same in shape but **adjudication and falsification
thresholds differ by type** — see §5 and §6.
Two modes:
- **Targeted (default).** For each finding, derive or accept a falsification
pattern ("what evidence would contradict this?"), then scan each source
type for matches.
- **Open-ended (`--open-ended`).** Find evidence patterns that no current
finding or hypothesis addresses. Surfaces blind spots and new-hypothesis
candidates. Slower; run quarterly or after a corpus expansion.
## When to invoke
- "sanity check findings against the news / interviews / CNJ reports"
- before drafting a section that leans heavily on a single interpretation
- after a corpus refresh (new articles fetched, new CNJ release, new transcripts)
Don't use it for:
- collecting new evidence (that's `/anecdotes` for news; manual for the rest)
- checking prose against scripts (that's `/validate-section`)
- pure literature lookup — that's `WebFetch`/research
## Args
```
/findings-audit [--findings <path>] [--corpus <path>...] [--mode targeted|open-ended]
[--finding <slug-or-heading>] [--types news,interview,court-opinion,aggregate-report]
[--full] [--max-rows N] [--auto]
```
- `--findings` — findings doc to audit. Default: discovered (§1).
- `--corpus` — one or more evidence sources. Default: discovered (§2).
- `--mode` — `targeted` (default) or `open-ended`.
- `--finding` — restrict to one finding (heading slug or phrase).
- `--types` — restrict to a subset of source types. Default: all that exist.
- `--full` — ignore `last_audited_sha` cache.
- `--max-rows` — cap rows examined per flag (cost guardrail).
- `--auto` — skip the §0 confirmation gate. Use for autonomous / scheduled runs.
**Default behavior on bare invocation (`/findings-audit` with no args):**
discover findings doc and sources, derive a plan, **present the plan and ask
the user to confirm or edit before any classification work runs**. See §0.
## Procedure
### 0. Plan and confirm (default; skip with `--auto`)
On bare invocation, run §1, §2, and §3 first as a *plan-only pass* — no
classification, no LLM cost — then present the plan and wait for the user.
The plan should include:
```
Findings audit — plan
Findings doc: <path> (<N> entries; sha <short>)
Mode: targeted [pass --mode open-ended for residual sweep]
Cache: last audit on <date> at sha <short>; <K> findings changed,
<M> sources changed since [or: no prior audit]
Sources detected:
[x] news <N> rows <path>
[x] aggregate-report <N> documents <paths>
[ ] interview (none found)
[ ] court-opinion (none found)
Restrict to one finding? no [pass --finding <slug> to restrict]
Candidates to read (after keyword/theme filter):
news <N1> rows across <K> findings
aggregate-report <N2> tables across <K> findings
...
Total: <N> snippets (warn if >300; see Cost guardrails)
Derived falsification patterns (per finding × source family):
- <finding-slug-1>:
narrative: "..."
aggregate: "..."
- <finding-slug-2>:
narrative: "..."
aggregate: "..." [or: n/a — finding has no numeric form]
...
```
Then ask:
> Proceed as shown? Or edit:
> (1) findings doc, (2) mode, (3) source types,
> (4) restrict to one finding, (5) falsification patterns, (6) cancel.
If the user picks an edit option, revise that piece and re-show the plan.
Loop until the user confirms or cancels.
If `--auto` is passed, skip this section and proceed to §4.
If args were partially specified (e.g. `--mode open-ended` but no
`--findings`), fill in defaults via discovery, show the plan, and confirm
once. Don't re-prompt on individual fields the user already pinned.
If discovery comes up empty for a load-bearing input (no findings doc;
no evidence sources at all), stop here and ask — don't show a plan youFrom hsigstad/research-kit, MIT.
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Signals
- Stars
- 0
- Forks
- 1
- Open issues
- 1
- Last commit (this folder)
- Aug 3, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/hsigstad/research-kit
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
findings-audit skill. Agent Mart. https://agentmart.dev/skills/hsigstad-findings-audit (last updated 2026-10-24)