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JiamingShi-JsPhysJiamingShi-JsPhys/agent-skills/skills/conflict-detector/SKILL.md

conflict-detector

Skill4 starsChanged 24 days agoMITView on GitHub

Cross-document conflict detector. Use when the user has two or more documents, datasets, or extracted fact tables and wants to find contradictions between them. Triggers on "detect conflicts", "find contradictions", "do these documents agree", "cross-check claims", "inconsistency check", or when comparing a claim across multiple sources. Runs three detection levels: entity (numeric / boolean mismatch on the same key), clause (categorical mismatch), and argument (free-text disagreement). Emits a machine-readable conflict report in JSON / CSV / Markdown.

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The file

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3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.

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  1. Distinctive:

    Before the first run, probe the runtime deps for this skill:

    line 57
  2. Very specific:

    If PyYAML is missing and the input is YAML, tell the user and ask whether to pip install pyyaml.

    line 63
  3. Very specific:

    Verify every documents[].value / documents[].path is reachable.

    line 77

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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Files
SKILL.md

conflict-detector

A self-contained skill that finds **contradictions across a set of

documents**. Each document is loaded (path / inline text / inline JSON),

its facts are extracted, and a cross-document rule engine pairs claims by

canonical key and flags mismatches.

MANDATORY WORKFLOW

Dependency bootstrap (run on first use)

Before the first run, probe the runtime deps for this skill:

  • Core: pure standard library — no install required.
  • Optional: PyYAML (only when the config / input is a .yaml; JSON inputs work with the standard library alone).

If PyYAML is missing and the input is YAML, tell the user and ask whether to pip install pyyaml; install only after they confirm. Never install into the global environment without explicit consent.

  1. Load the YAML config (config/examples/two_docs.yaml as reference).
  2. Verify every documents[].value / documents[].path is reachable.
  3. python -m src.cli run --config <path> [--format json,csv,md].
  4. Report the per-level conflict count and point the user at

    ./conflicts/conflicts_<run_id>.*.

When to use

  • Two or more documents, datasets, or extracted fact tables must be checked

    against each other for contradictions.

  • The user asks to "detect conflicts", "find contradictions", "cross-check

    claims", or asks "do these documents agree".

  • A single claim (a number, a status, a named fact) appears in several

    sources and must be compared across them.

  • A fact table extracted from documents must be audited for internal

    consistency before it is relied on.

Usage examples




# Validate a config before running it (fast, no report written).



python -m src.cli validate --config config/examples/two_docs.yaml







# List the document sources a config resolves to.



python -m src.cli list-docs --config config/examples/two_docs.yaml







# Run a detection; pick the output formats (default: json).



python -m src.cli run --config config/examples/two_docs.yaml --format json,csv,md


Reports are written to ./conflicts/conflicts_<run_id>.{json,csv,md}; the

JSON is the machine-readable contract.

Failure handling

  • Config invalid (validate exits non-zero) - fix the reported field

    and re-validate; do not hand-edit around a schema violation.

  • A document source is unreachable - the run aborts naming the

    offending documents[] entry; verify the path or inline value, retry.

  • No conflicts found - a valid, reportable result (an empty report),

    not an error; say so explicitly instead of re-running.

  • Unexpected exit code - report the message verbatim; do not fall back

    to comparing the documents by hand.

Detection levels

| Level | Kind | Severity | Example |

| --- | --- | --- | --- |

| entity | numeric_mismatch, boolean | high | speed = 10 vs speed = 20 |

| clause | categorical | medium | status = "active" vs status = "archived" |

| argument | categorical (text) | low | free-text lines that disagree |

Limitations

  • Extraction is key-value oriented; free-text paragraphs only participate

    at the argument level (line-level comparison).

  • Unit normalisation is table-driven (unit_map); custom unit rules must

    be supplied via the config.

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