research

分类: 调研与分析 | 上传者: wphillipmoore | 下载: 0 | 版本: v1.0（最新）
运行结构化研究——声明验证、查询回答和文档事实检查——并提供完整的证据存档输出。
更新日志: Source: GitHub https://github.com/wphillipmoore/ai-research-methodology
目录结构

当前层级: tree/develop/ai-research-methodology/skills/research/
📁 output-formats/
- 📄 default.md 16.1 KB
📁 prompts/
- 📁 compiled/
  - 📄 evidence-packets.md 22.9 KB
  - 📄 hypotheses.md 19.3 KB
  - 📄 reports.md 21.2 KB
  - 📄 research-input-clarified.md 21.6 KB
  - 📄 result-selector.md 21.3 KB
  - 📄 scorecards.md 20.7 KB
  - 📄 search-executor.md 21.6 KB
  - 📄 search-plans.md 19.0 KB
  - 📄 search-results.md 15.4 KB
  - 📄 self-audit.md 21.0 KB
  - 📄 synthesis.md 22.6 KB
📄 SKILL.md 22.4 KB
SKILL.md

---
name: research
description: Run structured research — claim verification, query answering, and document fact-checking — with full evidence archive output.
user_invocable: true
---

# Research

## Overview

Run a structured research investigation. Produces a complete evidence archive
with searches, sources, scorecards, evidence extracts, hypotheses, ACH
matrices, and self-audits.

The input can contain claims (assertions to verify), queries (questions to
answer), axioms (facts to assume true), or any combination. The research
methodology determines how to handle each type automatically.

## Commands

### run — Execute new research

```
/research run [file=<path>] [output=<dir>] [id=<id>] [runs=<count>] [confirm=yes|no]
```

| Parameter | Required | Description | Default | Example |
|-----------|----------|-------------|---------|---------|
| `file` | No | Path to a markdown file containing claims, queries, and/or axioms. | Ask interactively | `file=claims.md` |
| `output` | No | Output directory. | Ask interactively | `output=research/ai-trust` |
| `id` | No | Research instance ID. Only needed if your output format uses it. | Auto-generated | `id=R0005` |
| `runs` | No | Number of independent runs. Each run is blind to the others. After all complete, a synthesis step produces aggregate results with consistency metrics. | `3` | `runs=1` |
| `confirm` | No | `yes`: confirm before running. `no`: batch mode, just run. | `yes` | `confirm=no` |

Any `key=value` parameter not listed above is ignored. If an unrecognized
parameter is provided, do not ask about it — silently ignore it.

**Examples:**

```
/research run file=claims.md output=research/ai-trust
/research run file=claims.md output=research/ai-trust runs=1
/research run
```

### rerun — Re-execute previous research

```
/research rerun <path-to-research-directory> [runs=<count>] [confirm=yes|no]
```

Re-executes a previous research run using the saved input spec. The input
spec file (`research-input.json`) is saved in the research instance directory
during the original run. The rerun creates a new timestamped directory
alongside the existing one(s). The `runs` parameter works the same as for
`run` (default: 3).

**CRITICAL: Isolation rule.** The rerun MUST be executed with NO knowledge
of prior run results. The skill reads ONLY `research-input.json` from the
research directory. It MUST NOT read, reference, or pass to the subagent
ANY files from existing date directories (prior run results, assessments,
evidence, or any other output). The subagent MUST NOT be given the path
to prior runs. This is essential for reproducibility — reading prior results
would bias the new research through anchoring and confirmation effects.

**Difference report.** After the rerun completes (and ONLY after — the
isolation rule must not be violated), the skill automatically generates a
difference report comparing the new run against the most recent prior run.

The diff is produced by a SEPARATE step that reads both runs AFTER the
rerun subagent has finished. This preserves isolation — the rerun agent
never sees prior results. Only the post-run comparison step reads both.

The diff report is saved in `{research-directory}/diffs/` as
`{prior-date}_vs_{new-date}.md`. If no prior run exists (first run),
no diff is generated.

The diff report contains:

1. **Per-claim/query comparison**: for each entity that exists in both
   runs, compare:
   - Probability/confidence shift (old → new rating)
   - Verdict change (which hypothesis prevailed)
   - New sources found (in new run but not prior)
   - Sources dropped (in prior but not new run)
   - Evidence direction shift

2. **Collection-level comparison**:
   - Overall distribution shift (how many claims moved between ratings)
   - New cross-cutting patterns
   - Gap changes (gaps closed, new gaps identified)
   - Source overlap (shared vs unique sources betweeruns)

3. **Article impact summary**:
   - Claims that strengthened (no action needed)
   - Claims that weakened (may need softening)
   - Claims that flipped (correction needed)
   - New information that should be incorporated

### extract — Extract verifiable claims from a document

```
/research extract <url-or-path> [output=<dir>]
```

Reads a document and produces a JSON file of verifiable factual claims
suitable for input to `/research run`.

| Parameter | Required | Description | Default | Example |
|-----------|----------|-------------|---------|---------|
| `<url-or-path>` | Yes | URL or local file path | — | `articles/A0005/drafts/draft-v1.md` |
| `output` | No | Directory to write the claims file | Local path: dirname of input. URL: must specify. | `output=research/` |

**Standard output file**: `extracted-claims.json` (placed in the output
directory). This name is fixed — do not ask the user for a filename.
The output MUST conform to the `extracted-claims.schema.json` schema.

**Extraction rules:**

1. **Read the document body only.** Do NOT read the References, Bibliography,
   Sources, or Works Cited section. The references are a cheat sheet — claims
   must be extracted as they appear in the prose. If the prose names a study,
   institution, or statistic, that's fair game. If the only way to identify
   the source is to read the reference list, skip it.

2. **Extract verifiable factual claims only.** A claim is a statement that
   can be checked against evidence. Skip:
   - Opinions and editorial framing ("I believe...", "This is concerning...")
   - Rhetorical questions
   - Definitions that are being stipulated, not asserted as fact
   - Structural statements about the article itself

3. **Include named entities and categorizations.** Product names, tool names,
   framework names, and categorization claims ("X is a type of Y") are
   verifiable and MUST be extracted. Do not skip "simple" assertions.

4. **If the input is a URL**, fetch the page content and extract the article
   body. Strip navigation, headers, footers, ads, sidebars, and other HTML
   chrome. If the page structure makes it unclear where the article body is,
   err on the side of including too much rather than too little.

5. **Present the claims to the user for review** before saving (unless
   `confirm=no` — see below). The user may add, remove, or modify claims.
   The extraction is a starting point, not a final product.

**Output format** (`extracted-claims.json`):

```json
{
  "source": "url or path to the source document",
  "extracted_at": "2026-04-13T12:00:00Z",
  "claims": [
    {"text": "first verifiable claim as it appears in the prose"},
    {"text": "second claim"}
  ],
  "queries": [],
  "axioms": []
}
```

The `queries` and `axioms` arrays are empty by default — the user may
add items before running verification. This JSON is directly usable as
input to `/research run file=extracted-claims.json`.

### fact-check — Extract and verify in one step

```
/research fact-check <url-or-path> [output=<dir>] [confirm=yes|no]
```

Extracts claims from a document and immediately runs verification on them.

| Parameter | Required | Description | Default | Example |
|-----------|----------|-------------|---------|---------|
| `<url-or-path>` | Yes | URL or local file path | — | `articles/A0005/drafts/draft-v1.md` |
| `output` | No | Directory for all output (claims + research) | Local path: dirname of input. URL: must specify. | `output=research/A0005-claims/` |
| `confirm` | No | `yes`: present claims for review before verifying. `no`: skip confirmation, just run. | `yes` | `confirm=no` |

**Workflow:**

1. Check if `{output}/extracted-claims.json` exists AND is newer than the
   source document. If yes, skip extraction and use the existing claims file.
   If no, run extraction.
2. **Pre-fact-check gate** (reference audit): Read the document's References
   section. For each reference:
   - If it has research evidence links (R/Q, SRC) → pass (already scored)
   - If it is clearly informational (link to an organization homepage,
     Wikipedia, regulatory text with no associated claim) → pass
   - If it supports a factual claim but has no scorecard → **flag it**
   Report any flagged references to the user. If flags exist and
   `confirm=yes`: ask the user to resolve them before proceeding. If
   `confirm=no`: report the flags as warnings but continue.
3. If `confirm=yes` (default): present the extracted claims to the user for
   review. The user may add, remove, or modify claims before proceeding.
4. If `confirm=no`: skip review, treat extracted claims as final.
5. Pass the claims to `run` for verification. The full research output is
   written to the output directory alongside the claims file.

**`confirm=no` (batch mode):**
- No confirmation prompt before extraction or verification
- No clarification of input
- Treat all input as final — the user specified everything on the command line
- Just run
- Still save research-input.json, snapshots, and all output artifacts

This is a convenience command. It does nothing that `extract` followed by
`run` doesn't do — it chains them and adds smart caching of the extraction.

**Future**: Support for directory input (collection of files) to fact-check
documentation trees is planned but not yet implemented.

## Workflow (for `run` and `rerun`)

### Step 1: Gather input

Parse any `key=value` parameters from the invocation. For any parameter NOT
provided on the command line, ask the user interactively. For parameters
that WERE provided, use them directly — do not re-ask.

1. **Input** (`file=` parameter) — either:
   - A file path was provided: read it and extract the claims, queries,
     axioms, and any candidate evidence attached to claims
   - No file provided: ask the user to type them or provide a path

2. **Output directory** (`output=` parameter) — where to write the results.
   If not provided, ask the user. If the user doesn't have one, suggest
   `research/{slug}/` as a starting point.

3. **Research ID** (`id=` parameter) — optional. If not provided, auto-generate
   from the output directory slug or use `RXXXX` as a placeholder.

### Step 2: Confirm and save input spec

Present a summary of what will be researched:

```
Research ID: R0005
Output directory: research/ai-trust/
Run date: 2026-03-31
Axioms: 0
Claims: 5 (2 with candidate evidence)
Queries: 2
  C001: First claim text...
  C002: Second claim text... [+1 candidate evidence URL]
  ...
  Q001: First query text...
  Q002: Second query text...
```

Ask the user to confirm before proceeding.

After confirmation, save the input specification as
`{output_directory}/research-input.json`. This file enables future re-runs
without re-specifying parameters.

**Guardrail**: Before writing `research-input.json`, check if one already
exists in the output directory. If it does, this is NOT a new research run —
it is an attempted reuse of an existing research directory. **STOP and fail
with an error:**

```
ERROR: {output_directory}/research-input.json already exists.
This directory belongs to an existing research instance.
To re-run this research, use: /research rerun {output_directory}
To start new research, use a different output directory.
```

Do NOT overwrite, merge, or append to an existing `research-input.json`.
Do NOT proceed with execution. This is a hard stop.

### Step 3: Create run group directory

Create `{output_directory}/{YYYY-MM-DD-HHMMSS}/` using the current date and
time (24-hour format, including seconds). This is the **run group** directory
that will contain all independent runs plus synthesis files.

Within the run group, create `run-1/`, `run-2/`, ... `run-{N}/` subdirectories.
**Zero-pad the run number** based on the total number of runs: if runs<=9,
use single digits (`run-1`). If runs is 10-99, use two digits (`run-01`,
`run-02`). If runs is 100-999, use three digits (`run-001`). This ensures
correct sort order in all file browsers.

### Step 4: Save methodology snapshot

Copy the compiled unified methodology into the run group directory (not
into each run subdirectory — one copy shared across all runs):

- `prompt-snapshot.md` — copy of `ai-research-methodology/standalone/research.md`

This captures both the common behavioral guidelines and every sub-agent
prompt as compiled at run time, producing a permanent record of the
methodology that was in effect.

### Step 5: Execute independent research runs

Each run follows the 11-step pipeline, producing JSON files at each
step. The compiled sub-agent prompts (in `skills/research/prompts/compiled/`) include
both the task instructions and the JSON schema. The sub-agent MUST
produce JSON conforming to the schema — no markdown, no prose.

**Isolation rule**: Each run is completely blind to the others. No run
may read, reference, or be influenced by any other run's output. This
is the same isolation principle as reruns — each run must be independent
to provide a valid signal about reproducibility.

**Parallelism**: For runs<=5, launch runs in parallel where possible. For
larger n, the agent may batch runs to manage resources.

**MANDATORY — Search tools**: Check your available tools list for
`dio_search` and `dio_fetch`. If these MCP tools are present:

- You MUST use `dio_search` for ALL web searches. Do NOT use the
  built-in `web_search` tool. Do NOT use `WebSearch`. Do NOT perform
  any web search through any mechanism other than `dio_search`.
- You MUST use `dio_fetch` for ALL page content retrieval. Do NOT
  read web pages directly.
- This is a hard cost control requirement. The built-in web search
  consumes expensive AI tokens. `dio_search` executes searches via
  Python at near-zero token cost. Using the wrong tool wastes the
  user's money.
- If `dio_search` returns an error (quota exhausted, service
  unreachable), STOP and inform the user. Do NOT silently fall back
  to `web_search`. Let the user decide whether to continue with the
  more expensive option.

ONLY if `dio_search` and `dio_fetch` are NOT in your available tools
list (MCP server not configured), fall back to the built-in web search.

**MANDATORY — Event logging**: If `dio_init_run` is in your available
tools list, call it ONCE at the start of each run, passing the run
directory path and run ID:

```
dio_init_run(output_dir="path/to/run-1", run_id="run-1")
```

This initializes the pipeline event log. All subsequent `dio_fetch` and
`dio_search` calls will automatically record errors (fetch failures,
search errors) to `pipeline-events.json` in the run directory. You do
NOT need to log these errors yourself — the Python MCP layer captures
them transparently.

After the last research step (archive), call `dio_flush_events()` to
write the accumulated event log to disk. This MUST happen before
`dio_render` so the renderer can include the Pipeline Notes section.

Each subagent executes these steps, writing JSON output files to its
`run-{N}/` directory:

**Step 5a: Clarify input** — Read `skills/research/prompts/compiled/research-input-clarified.md`.
For each claim and query, clarify, surface assumptions, map vocabulary.
Write `research-input.json` (clarified input with IDs assigned).

**Step 5b: Generate hypotheses** — Read
`skills/research/prompts/compiled/hypotheses.md`. For each claim/query, pass
the clarified item and axioms. Write `hypotheses.json`.

**Step 5c: Design searches** — Read
`skills/research/prompts/compiled/search-plans.md`. For each item, pass the
clarified item and its hypotheses. Write `search-plans.json`.

**Step 5d: Execute searches** — For each search in the plan:
- If `dio_search` MCP tool is available: call it with the search terms.
- If not available: use built-in web search.
For each batch of results, score relevance (0-10) using the criteria
in `skills/research/prompts/compiled/search-results.md`. Filter by score >= 5,
deduplicate by URL. Write `search-results.json`.

**Step 5e: Score sources** — For each selected source:
- If `dio_fetch` MCP tool is available: call it to get page content.
- If not available: the subagent reads the source directly.
Score reliability, relevance, and six bias domains per
`skills/research/prompts/compiled/scorecards.md`. Write `scorecards.json`.

**Step 5f: Extract evidence packets** — Read
`skills/research/prompts/compiled/evidence-packets.md`. For each item, pass
the clarified item, its hypotheses, and the scorecards (including
`content_extract`). The extractor emits verbatim excerpts tied to specific
hypotheses (or search themes, in open-ended mode) with an explicit
supports / refutes / nuances / context relationship. Write
`evidence-packets.json`. This is the grounding layer between source
scoring and synthesis — synthesis downstream should cite packets rather
than paraphrase from memory.

**Step 5f.1: Validate evidence packets** — If `dio_validate_packets` is
in your available tools list, call it IMMEDIATELY after writing
`evidence-packets.json`:

```
dio_validate_packets(run_dir="path/to/run-1")
```

This runs the Python deterministic verbatim verifier against every
packet. It checks each claimed excerpt against the source's content
(using the server-side fetch cache from `dio_fetch` — even if you
dropped `content_extract` from the scorecards, the cache has it).
Non-verbatim packets are removed and `verbatim_stats` is rewritten
with real numbers. Do NOT populate `verbatim_stats` yourself — the
validator overwrites whatever you wrote with deterministic results.

**Step 5g: Synthesize, assess, gaps** — Read
`skills/research/prompts/compiled/synthesis.md`. For each item, pass the
scorecards, hypotheses, and evidence packets from Step 5f. Synthesize the
evidence collection, produce probability assessment, identify gaps. The
packets are the primary grounded input; the scorecards weight them.
Write `synthesis.json`.

**Step 5h: Self-audit** — Read `skills/research/prompts/compiled/self-audit.md`. For
each item, pass the full chain including `evidence_packets`. Audit the
process, verify source interpretations (Step 9b traces assessment claims
back to packet excerpts), produce reading list. Write `self-audit.json`.

**Step 5i: Report** — Read `skills/research/prompts/compiled/reports.md`. For
each item, assemble the final report from all prior steps. Write
`reports.json`.

**Step 5j: Archive** — Combine all JSON outputs into `archive.json`
with a timestamp and pipeline version.

**Step 5k: Usage** — Record token usage, API call counts, and estimated
costs. Write `usage.json` to the run group directory.

**Output files per run directory** (all JSON, no markdown):

```
run-{N}/
├── research-input.json
├── hypotheses.json
├── search-plans.json
├── search-results.json
├── scorecards.json
├── evidence-packets.json
├── synthesis.json
├── self-audit.json
├── reports.json
└── archive.json
```

### Step 5l: Synthesize across runs

**This step runs ONLY after ALL runs have completed.** It reads the
JSON output from every `run-{N}/` directory and produces group-level
JSON files in the run group directory.

Produce these files:

1. **group-synthesis.json** — aggregate result from all runs:
   - For each claim/query: consensus verdict, divergences, union of sources
   - Overall assessment that integrates findings from all runs
   - Where runs agree: state with increased confidence
   - Where runs disagree: note the divergence and classify the root cause

2. **group-consistency.json** — similarity metrics across runs:
   - Source overlap (% shared between each pair, sources found in all/most/one)
   - Verdict agreement (did all runs support the same hypothesis?)
   - Scoring consistency (same source scored the same way?)
   - Overall similarity score
   - Diagnostic: if <50% overlap, flag as "query may be too ambiguous"

3. **group-reading-list.json** — consolidated reading list across all runs,
   deduplicated, with provenance (which runs found each source)

4. **usage.json** — combined usage across all runs

### Step 6: Report completion

When synthesis finishes, report to the user:

```
## Research Complete

- **Runs**: {n} independent runs
- **Items investigated**: {count} claims/queries
- **Output**: {path to run group directory}
- **Duration**: {wall clock time}
- **Consistency**: {overall similarity score}%

### Verdict Summary (from synthesis)

| Probability | Count | Items |
|-------------|-------|-------|
| Almost certain | {n} | {list} |
| Very likely | {n} | {list} |
| Likely | {n} | {list} |
| Unlikely | {n} | {list} |

### Run Consistency

| Metric | Value |
|--------|-------|
| Source overlap (avg pairwise) | {n}% |
| Verdict agreement | {all agree / N of M agree} |
| Scoring consistency | {consistent / N divergences} |

### Flags for Attention

| Item | Issue |
|------|-------|
| {ID} | {description of issue needing correction} |
```

## Constraints

- Do not proceed without user confirmation (Step 2)
- Do not modify files outside the output directory
- The output format spec is the source of truth for file structure
- Every search result must be dispositioned (no silent drops)
- Always produce a minimum of 3 hypotheses per claim or query.
  The first three are mandatory: H1 (affirmative/accurate), H2 (negative/
  partially correct), H3 (nuanced/materially wrong). Additional hypotheses
  (H4, H5, ...) may be added when the evidence supports more than three
  distinct explanations.
- **Run isolation**: The subagent MUST NOT read prior run results. On a rerun,
  read ONLY `research-input.json`. Do NOT pass prior run paths to the subagent.
  Prior results bias new research through anchoring and confirmation effects.
- **Claim extraction blindness**: When extracting claims from a document
  (via `extract` or `check`), read ONLY the document body. Do NOT read or
  include the References section — it biases the fact-checker. Claims should
  be extracted as they appear in the prose.
- **Unrecognized parameters**: Silently ignore any `key=value` parameter not
  documented above. Do not ask about it.

## Customization

The research pipeline produces JSON at every step. The compiled sub-agent
prompts in `skills/research/prompts/compiled/` contain both the task instructions and the
JSON schema for each step's output. To customize, modify the source prompts
in `src/diogenes/prompts/sub-agents/` and/or the schemas in
`src/diogenes/schemas/`, then run `python scripts/compile-prompts.py` to
rebuild the compiled versions.

Markdown rendering from JSON is a separate step handled by the `dio_render`
MCP tool or a fallback text description. The research methodology is
independent of the rendering — you can change how results are presented
without changing how research is conducted.

## Future extensions

- **dio_render** MCP tool for JSON-to-markdown rendering
- **report** mode (topic exploration with facets)
- **check-triggers** command to evaluate revisit conditions (#87)
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