Agentic tool-use skills for Cognitive Security and analytic tradecraft — a library of 100 analytic skills (Structured Analytic Techniques, cognitive- security defenses, OSINT integrity, counterintelligence, and critical review), each authored once as a harness-neutral skill with default adapters for Claude Code, Codex, and Hermes, and the same structural contract for any additional configured harness.
git clone https://github.com/docxology/CogSecSkills.git
cd CogSecSkills
uv sync
PYTHONPATH="src:." python -m cogsecskills validate # 0 errors over all 100 skills
# Find the right skill for an analytic need, then inspect it
python -m cogsecskills route "verify a viral claim before sharing it"
python -m cogsecskills show sat.analysis_of_competing_hypothesesTo connect an agent harness, point it at skills/<group>/<slug>/SKILL.md, run
workflow.md, and bind tools through the matching harness/<name>.md adapter.
The full command reference is in Usage; harness details are in
docs/harness-installation.md and
docs/harness-cookbook.md.
- Manuscript (PDF):
CogSecSkills.pdf— the full, reproducible skills-system report (also attached to each release). - Archived on Zenodo: concept DOI
10.5281/zenodo.20804585 (always
resolves to the latest version); v1.0.0 version DOI
10.5281/zenodo.20804586. Citation
metadata in
CITATION.cff. - New here?
QUICKSTART.md· Docs map:docs/README.md
The library is strictly defensive, educational, and accountable. Its educational upstream is the AGEINT curriculum. These skills recognize, assess, and defend against cognitive attack — they do not author manipulation.
| Artifact | Role | Where |
|---|---|---|
| Teach | the concepts (the why) | docs/ageint/ |
| Plan | catalogue of all 100 skill areas (the what) | registry/skills.yaml |
| Build | canonical definitions and rendered multiharness skill implementations (the how) | definitions/ and skills/ |
python -m cogsecskills validate keeps Plan and Build coherent: every on-disk
skill must be catalogued, and every implemented catalogue entry must exist.
| Group | Focus | Count |
|---|---|---|
sat |
Structured Analytic Techniques (Heuer & Pherson) | 34 |
cognitive_security |
Defending perception, reasoning, decision-making | 24 |
critical_review |
Adversarial + constructive review (incl. project critical review) | 12 |
osint_integrity |
Open-source collection with provenance discipline | 10 |
counterintelligence |
Denial/deception detection, process hardening | 8 |
information_environment |
Narratives, influence ops, coordinated behavior | 7 |
research_methods |
Synthesis, evidence grading, calibrated estimation | 5 |
All 100 areas are fully implemented as conforming, multiharness skill folders —
each with a real, technique-accurate procedure, a fitting tool plan, and adapters
that bind every declared verb under the configured harness set. The default
configured set is Claude Code, Codex, and Hermes.
python -m cogsecskills report shows the live counts (implemented: 100).
All 100 skills now have persistent canonical definitions under definitions/.
python -m cogsecskills definitions --write renders the full skills/ tree
deterministically from that source layer, and definitions --check proves the
definitions and rendered files have not drifted.
python -m cogsecskills scenarios --check adds a deterministic defensive
readiness gate over curated safe-use and unsafe-redirect fixtures; it checks
routing, local skill contracts, expected response-shape metadata, and reviewed
expected-answer fixtures without calling external model runtimes.
python -m cogsecskills examples --write generates one worked defensive
example per skill from examples/skill-worked-examples.yaml.
python -m cogsecskills evals --write generates offline local output-review
fixtures and reports from scenario expected answers; it does not call live
models.
python -m cogsecskills dashboard --write generates a 100-skill quality
dashboard for navigation and drift review across scenarios, offline evals,
worked examples, quality capsules, harnesses, references, and source paths,
with Markdown, static HTML, and JSON views.
python -m cogsecskills release-metadata --write generates the local release
claim matrix and metadata snapshot without publishing, tagging, or archiving.
Exact git revision, branch, and dirty-state values are observed at command
runtime rather than embedded in committed generated files.
skills/<group>/<slug>/
skill.yaml # generated harness-neutral spec
SKILL.md # Claude Code native entry point (frontmatter + doc)
workflow.md # the agentic procedure, each step tagged with a tool verb
harness/
claude.md # default adapter: Claude Code tools
codex.md # default adapter: Codex tools
hermes.md # default adapter: Hermes function calls
<name>.md # optional configured harness adapter
A skill declares its capabilities as closed-set tool verbs (read, search,
write, exec, reason, web, delegate, ask). Each harness adapter binds
those verbs to concrete tools in a Markdown binding table. The validator checks
that every declared verb is present in every adapter, so "multiharness" is a
property the test suite proves, not a hope.
The persistent source of truth for authored skill substance is
definitions/<group>/<slug>.yaml; skill.yaml and the companion Markdown files
are the generated, harness-facing build outputs.
# Install from GitHub and validate the library
git clone https://github.com/docxology/CogSecSkills.git
cd CogSecSkills
uv sync
PYTHONPATH="src:." python -m cogsecskills validate
# List the catalogue (all 100 areas)
python -m cogsecskills list
python -m cogsecskills list --group sat --status implemented
# Inspect one skill
python -m cogsecskills show sat.analysis_of_competing_hypotheses
# Find the best skill for a free-text analytic need
python -m cogsecskills route "verify a viral claim before sharing it"
# Validate the whole library (plan <-> build coherence + multiharness conformance)
python -m cogsecskills validate
# JSON status report
python -m cogsecskills report
# Statistics, grouped catalogue, and quality lint
python -m cogsecskills stats
python -m cogsecskills catalogue --markdown --output docs/catalogue.md
python -m cogsecskills doctor
python -m cogsecskills scenarios --check
python -m cogsecskills examples --write
python -m cogsecskills examples --check
python -m cogsecskills evals --write
python -m cogsecskills evals --check
python -m cogsecskills dashboard --write
python -m cogsecskills dashboard --check
python -m cogsecskills release-metadata --write
python -m cogsecskills release-metadata --check
# Regenerate manuscript supplements and figures from the live library
python -m cogsecskills manuscript-assets --write
python -m cogsecskills manuscript-assets --check
# Regenerate all rendered skills from canonical YAML definitions
python -m cogsecskills definitions --write
python -m cogsecskills definitions --check
# Author a full skill deterministically from a structured JSON/YAML definition
python -m cogsecskills author path/to/definition.yaml # render one
python -m cogsecskills author-batch # compatibility path for skills/**/_def.json
# Scaffold a brand-new planned area from the registry (skeleton to deepen)
python -m cogsecskills scaffold sat.some_new_area(From the project root, with PYTHONPATH="src:." or after uv sync.)
To connect a harness, point it at skills/<group>/<slug>/SKILL.md, use
workflow.md for the neutral procedure, and bind tools through the matching
harness/<name>.md adapter. The default configured set is claude, codex,
and hermes; add more harnesses in cogsecskills.yaml and regenerate adapters
with python -m cogsecskills definitions --write.
For bounded examples, see
examples/harness-smoke-transcripts.md
examples/group-worked-examples.md, and
docs/skill-worked-examples.md.
For claim discipline, see docs/claim-boundaries.md.
For the generated quality dashboard, see
docs/quality-dashboard.md and
docs/quality-dashboard.html. Visual style rules
for generated figures, the cover image, dashboard pages, and manuscript tables
live in DESIGN.md.
(All 100 skills are implemented; these 8 are the hand-authored references.)
| Skill | Group |
|---|---|
sat.analysis_of_competing_hypotheses |
Structured Analytic Techniques |
sat.key_assumptions_check |
Structured Analytic Techniques |
sat.devils_advocacy |
Structured Analytic Techniques |
cognitive_security.narrative_threat_assessment |
Cognitive Security |
cognitive_security.source_credibility_evaluation |
Cognitive Security |
critical_review.project_critical_review |
Critical Review & Assurance |
osint_integrity.claim_provenance_verification |
OSINT & Source Integrity |
research_methods.structured_literature_synthesis |
Research & Synthesis Methods |
PYTHONPATH="src:." python -m pytest \
tests/test_cogsecskills_*.py tests/test_skill_library_conformance.py \
--cov=src/cogsecskills --cov-report=term-missingThe live conformance test (tests/test_skill_library_conformance.py) runs against
the real skills/ tree: adding a malformed skill, or an implemented registry
entry with no build, fails the suite.
- Canonical public repository: https://github.com/docxology/CogSecSkills. Local manuscript/render validation may use the sibling docxology template, but the deliverable is the skills system: registry, skills, AGEINT docs, runner, tests, generated manuscript supplements, and figures that describe those source surfaces.
- Archived release: v1.0.0 on Zenodo — concept DOI 10.5281/zenodo.20804585 (all versions), version DOI 10.5281/zenodo.20804586. GitHub releases at https://github.com/docxology/CogSecSkills/releases carry the rendered manuscript PDF.
- Educational upstream: AGEINT (concept DOI 10.5281/zenodo.20732274).
- The skill catalogue draws on Heuer & Pherson, Structured Analytic Techniques for Intelligence Analysis, and the cognitive-security literature cited per-primer.
See ISA.md for the project's ideal-state articulation and acceptance
criteria.